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Review Article | DOI: https://doi.org/10.31579/2690-1919/593
1Clinical Research Center-Translational Medicine Laboratory of PLA No.924 Hospital, Guilin 541000, China.
2Wuhan East Lake College, Wuhan 430212, Hubei Province.
*Corresponding Author: Lu Kun, Clinical Research Center-Translational Medicine Laboratory of PLA No.924 Hospital, Guilin 541000, China.
Citation: Lu Kun, Wang Shutong, Wang Yalin, Ying Qunbo, (2026), Application of Artificial Intelligence Enabled Targeted Clinical Diagnosis and Treatment, J Clinical Research and Reports, 23(2); DOI:10.31579/2690-1919/593
Copyright: © 2026, Lu Kun. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received: 26 January 2026 | Accepted: 09 February 2026 | Published: 18 February 2026
Keywords: clinical diagnosis and treatment; personalized adaptation; federated learning; standardized intelligent diagnosis and treatment models; multimodal data processing technology; multi-source heterogeneous medical data; digital therapeutics
Diagnosis and treatment, the core pillars of the clinical healthcare system, remain critical to safeguarding patient health. The long-accumulated clinical expertise in these areas demonstrates significant value in preventing and treating major diseases. Traditional clinical diagnosis and treatment practices face several core challenges, including inefficient data processing, inadequate standardization of diagnosis and treatment aligned with standardized intelligent diagnosis and treatment models, and insufficient personalized adaptation, which are increasingly exposed by the pressing need for precise, efficient, and personalized medicine. With its targeted breakthroughs and mature applications in medical data processing and intelligent decision-making modeling, AI technology has emerged as the only viable and indispensable core technological solution to overcome these challenges. This paper examines how AI tackles these issues by focusing on the core challenges of traditional clinical diagnosis and treatment systems, including data processing efficiency, standardization of diagnosis and treatment, and personalized adaptation capabilities. It systematically expounds how artificial intelligence (AI) technologies—including federated learning architectures, multimodal data processing technology (for integrating multi-source heterogeneous medical data), and personalized modeling technologies—can specifically address these challenges and create clinical value. Meanwhile, it analyzes the practical challenges faced by AI in clinical applications, such as the balance between data privacy and quality, insufficient interpretability, ambiguous definition of ethical responsibilities, and commercialization difficulties. Beyond these hurdles, this paper concludes by outlining future development pathways, including advancing multimodal data processing technology, building trustworthy systems, promoting ecological collaboration, and expanding universal access. It ultimately demonstrates how AI can drive high-quality healthcare development (laying the groundwork for digital therapeutics via personalized interventions) and provides a roadmap for medical system upgrades.
Today’s healthcare landscape demands precision, efficiency, and personalized treatment, but traditional clinical models are struggling to keep up due to limitations in data utilization, decision-making efficiency, and individualized adaptation[1] . For instance, traditional clinical models are limited to integrating only 1-2 types of modal data, such as text-based medical records and basic laboratory tests, whereas the integration rate of multimodal data, including imaging, genetic, and wearable device data, remains below 15%[2] , leaving significant data value untapped. To overcome these challenges, artificial intelligence (AI) is emerging as a pivotal force in reshaping clinical paradigms, leveraging its unique strengths in data analysis, pattern recognition, and dynamic decision-making. The following analysis will first dissect the inherent limitations of traditional clinical care models, then delve into how AI technology provides targeted solutions to these pain points through specialized applications[3] .
Within the global healthcare system, traditional diagnostic and treatment models serve as the foundational framework. However, their inherent reliance on human labor, coupled with limitations in resource allocation and data processing, exposes multidimensional shortcomings when addressing complex medical needs. This makes them ill-suited to meet modern medicine's pursuit of efficiency, precision, and universal accessibility[4] .
3.1 From the Perspective of Medical Resource Allocation
The “siphoning effect” of high-quality medical resources is a universal challenge worldwide. According to a 2024 report by the World Health Organization (WHO)[6] , high-income countries account for 72% of the world's total medical resources, while low-income countries hold only 4.5%. The disparity in per capita healthcare expenditure reaches a staggering 55-fold difference[7] .
This global imbalance in medical resource distribution also manifests as pronounced “centralization” within individual nations[8] : High-tier hospitals, leveraging their resource concentration advantages, often concentrate over 80% of a nation's senior physicians, advanced equipment, and core diagnostic expertise. Conversely, primary healthcare facilities commonly face challenges such as insufficient physician qualifications, limited case experience, and outdated equipment. This directly results in a severe mismatch between primary care capabilities and patient needs[9] .
In sub-Saharan Africa, healthcare resource scarcity leads to preventable disease mortality rates 40% higher than in high-income regions[10] . Even in Europe and the United States with relatively well-developed healthcare systems, patients in remote rural areas often require lengthy referrals to urban central hospitals for basic care. This not only increases healthcare costs but also risks worsening conditions due to delayed treatment, perpetuating a vicious cycle where “greater resource concentration leads to weaker primary care.”
3.2 In terms of diagnostic efficiency and precision
With the widespread adoption of medical imaging and genetic testing technologies, medical data is growing exponentially. Traditional models heavily rely on individual physician expertise, yet human processing capacity has reached a global bottleneck when confronting the rapidly expanding volume of multimodal data in modern medicine[11] .
WHO statistics indicate that the annual growth rate of global medical imaging examinations reached 15% in 2023[12] . Imaging data such as CT and MRI scans account for over 80% of total medical data. A single chest CT scan can generate 300-500 images[13] , while a comprehensive genetic sequencing report contains millions of data points. Even dedicating their full attention, physicians struggle to achieve rapid and thorough interpretation of all this data.
Simultaneously, processing multidimensional patient data—such as medical history, laboratory indicators, and medication responses—requires manually integrating information scattered across disparate systems. This process is not only time-consuming but also prone to diagnostic errors due to information omissions. Under prolonged high-intensity workloads, the global rate of manual misdiagnosis for early-stage tumors and other minute lesions can reach 15%-20%[14] .Even in developed regions like Europe and the United States, manual image interpretation carries an 8%-12% risk of missed diagnoses. In low- and middle-income countries, constrained by physician experience and limited equipment resolution, the rate of missing such subtle lesions can exceed 30%[15] .
Whether due to inefficient, time-consuming data processing or the risk of missed diagnoses caused by fatigue, these factors fundamentally sever the link between “real-time condition monitoring” and “treatment plan adjustments,” ultimately making the dynamic, precise management of chronic diseases a significant challenge[16] .
3.3 Regarding the degree of treatment plan personalization
Traditional diagnostic and treatment models remain constrained by a "group experience-oriented" core logic[17] .Standardized protocols based on authoritative clinical guidelines overlook the dynamic variations in patients' genetics, physiological states, and living conditions. This limitation is particularly pronounced in global cancer and chronic disease treatment[18] .
Take cancer treatment as an example: patients with the same cancer type and stage exhibit significant variations in response to chemotherapy and targeted therapies due to differences in genetic profiling, immune status, and lifestyle habits[19] .Traditional protocols can only prescribe medications based on clinical guideline recommendations. Data from the National Cancer Institute indicates that traditional approaches achieve an overall response rate of just 35% for advanced solid tumor patients. 28% of patients receive ineffective treatment due to failure to identify drug resistance genes beforehand[20] . Moreover, reliance on delayed imaging assessments for efficacy evaluation often delays timely adjustments.
Furthermore, in chronic disease management, traditional models fail to capture real-time fluctuations in blood glucose and blood pressure, nor can they integrate dietary and exercise data to develop personalized intervention plans. This limitation profoundly impacts the 1.3 billion hypertensive and 500 million diabetic patients globally—failing to accurately monitor critical data like nocturnal hypertension and postprandial glucose spikes[21] . hypertension patients' nighttime medication needs remain inadequately addressed, and diabetes patients' glycemic control rates (HbA1c < 7>
Artificial intelligence, with its core capabilities of autonomous evolution through deep learning, parallel processing of massive datasets, and precise integration of multimodal information, precisely addresses the shortcomings of traditional diagnosis and treatment[22] . It is not merely a technical add-on but fundamentally aligns with the limitations of conventional medical practices, serving as the pivotal solution to overcome these challenges[23].
4.1 Federated Learning Enables Standardized Intelligent Diagnosis and Treatment Models—Addressing Imbalances in High-Quality Healthcare Resource Allocation
Artificial intelligence provides the algorithmic framework and technological foundation for constructing standardized intelligent diagnosis and treatment models. As a key branch technology, federated learning addresses the challenge of medical data silos through distributed collaborative training under privacy protection, enabling secure cross-institutional data reuse[24] . This empowers models to enhance their generalization capabilities and clinical adaptability under unified standards. Its core logic is as follows:
Standardized intelligent diagnosis and treatment models (hereafter referred to as “diagnosis models”) serve as the core technological vehicle for addressing capacity gaps at the grassroots level[25] . They are defined as AI models constructed based on multicenter evidence-based medical data, capable of generating personalized clinical decision outputs. This model must satisfy two core characteristics: First, “standardization”—meaning the model training data covers standardized diagnostic and therapeutic cases from tertiary hospitals, and the generated medication recommendations and risk warnings must demonstrate≥90% consistency[26] with clinical guidelines and expert consensus. Second,“intelligence”—the model dynamically adjusts decisions based on individual patient characteristics rather than mechanically applying group guidelines[27] .
Federated learning, as a distributed machine learning technology[28] ,provides critical support for constructing clinical decision models. Its core logic enables collaborative training of multi-center medical data while ensuring data privacy and compliance. The specific implementation path is as follows:
1. Data Collaboration Layer: Organized around “tertiary hospitals + primary care institutions” as collaborative units, tertiary hospitals provide high-quality annotated data while primary care institutions contribute real-world data from local patients[29]. All institutions store data locally without transferring raw data across organizations.
2. Model Training Layer: Adopts an iterative “local training-parameter aggregation-global update” model—each institution trains an initial model using local data, uploading only model parameters to the federated learning center server; The server aggregates and optimizes parameters via encryption algorithms, generates a global model, and distributes it back to institutions to complete one training cycle[30] . After 10-15 iterations, the model adapts to clinical scenarios across different regions and healthcare levels.
3. Model Validation Layer: Multi-center clinical validation ensures the model achieves≥88?curacy[31] in medication recommendations for primary care patients and ≥85% sensitivity in complication alerts, meeting practical primary care needs.
The scarcity of primary care data and the difficulty in disseminating high-quality clinical expertise—resulting from the “centralization” of medical resources—fundamentally stem from the contradiction between data silos and insufficient standardized clinical capabilities[32] . Federated learning enables distributed collaborative training under privacy protection. This approach preserves AI's advantage in processing massive datasets while overcoming compliance and security barriers to cross-institutional data transfer. It facilitates secure collaboration between tertiary hospitals' high-quality data resources and primary care's real-world data, thereby advancing the development of standardized intelligent diagnosis and treatment models. This shifts AI technology from “point applications” to “holistic empowerment,” precisely addressing primary care's urgent need for standardized clinical capabilities.
The underlying logic and technical pathways of federated learning enabling standardized intelligent diagnosis and treatment models have been thoroughly validated through collaborative research among multiple scientific teams and medical institutions both domestically and internationally, yielding a series of practical outcomes with clinical application value. For instance, teams including Tencent's Tianyan Lab[33] , Peking University Health Science Center, and Ruijin Hospital have respectively developed standardized intelligent models for stroke prediction, pediatric pneumonia assessment, and rare disease diagnosis using federated learning. While safeguarding data privacy, these models achieved prediction/diagnostic accuracy rates as high as 98.7%, significantly reducing misdiagnosis rates at primary care levels and outperforming single-center models. Institutions like the U.S. SIIM, France's Owkin, and the UK's NHS leveraged federated learning frameworks to integrate multi-center medical data[34] , successfully developing intelligent models for renal cell carcinoma segmentation, breast cancer recurrence prediction, and cardiovascular disease screening. These advancements significantly improved diagnostic consistency, predictive AUC values, and screening efficiency, providing efficient solutions for cross-institutional collaborative diagnosis and treatment[35] .
The aforementioned “federated learning-enabled standardized intelligent diagnosis and treatment models” represent a typical practical pathway for AI technology to address the imbalance in the distribution of high-quality medical resources[36] . This is not the sole model for AI's participation in balancing healthcare resource allocation. AI can also contribute through establishing “AI pre-screening + expert remote precision diagnosis” systems, developing portable diagnostic devices with embedded AI, and building AI-driven physician training systems[37] . Though these approaches differ in implementation, they all leverage AI as a bridge to break down barriers between high-quality resources and grassroots needs, collectively expanding more feasible pathways for universal access to healthcare resources.
4.2 AI Multimodal Data Processing Empowers Diagnostic and Treatment Process Optimization—Breaking Through Bottlenecks in Efficiency and Accuracy
As a vital branch of artificial intelligence, AI multimodal data processing leverages core technologies such as deep learning and cross-modal fusion to break down modal barriers between different types of medical data[38] . Through precise integration and efficient analysis, it provides critical support for enhancing the efficiency and precision of diagnostic and treatment processes, specifically addressing core bottlenecks in traditional medical models. Its technical logic and implementation pathways are detailed below:
Multimodal data processing constitutes a comprehensive technological framework encompassing “image recognition, temporal prediction, and multi-source data fusion,” centered on two key technologies:
First, CNN-based image analysis technology simulates the human visual system through convolutional neural networks to automatically extract pathological features from medical images[39] —such as pulmonary nodules <5mm>
Second, RNN/Transformer-based time-series data processing technology captures fluctuation patterns in continuous monitoring data across the temporal dimension[40] . It predicts indicator trends over the next 4-24 hours, achieving 30%-50% higher prediction accuracy than traditional statistical methods. Both technologies share the common feature of “eliminating the need for manual annotation of all data.” They reduce data dependency through semi-supervised learning, making them suitable for scenarios with scarce medical data[41] .
In the field of imaging screening, AI systems developed through multi-center collaboration can simultaneously process multiple modalities of medical images, automatically annotate suspicious lesion areas, and reduce the average processing time per image from tens of minutes to minutes. This significantly lowers the rate of missed early-stage diagnoses, providing primary care institutions with standardized imaging diagnostic support tools. For instance, Google Health's AI for breast cancer screening, developed using CNNs[42] , can simultaneously process mammography and ultrasound images while automatically labeling suspicious lesions. In primary care hospitals across Europe and the US, this system reduced single-image processing time from 20 minutes to 1 minute, lowering the early breast cancer missed diagnosis rate from 18% to 7%[43] . A domestic team's “Diabetic Retinopathy AI Screening System,” compatible with fundus cameras in primary hospitals, allows image uploads via mobile devices. The AI outputs “no lesions/mild/moderate/severe” diagnoses within 30 seconds, boosting primary care fundus screening efficiency tenfold and expanding annual coverage from 5,000 to 50,000 individuals.
In chronic disease dynamic management, mainstream intelligent platforms integrate multidimensional time-series data—including physiological metrics and lifestyle habits—to automatically generate personalized health recommendations and proactively alert users to potential indicator abnormalities[44] . This significantly enhances patient self-management efficiency and physicians' clinical coordination capabilities. For instance, Livongo's diabetes management platform leverages RNN technology to integrate continuous glucose monitoring data, dietary logs, and exercise metrics. It automatically generates “high GI food avoidance lists” and “post-meal exercise recommendations,” while providing 6-hour advance warnings for “potential post-breakfast blood glucose exceeding 8.3 mmol/L the following day.” This approach increased timely intervention rates for abnormal blood glucose from 35% to 82%, enabling physicians to manage 150 patients monthly compared to the previous 50[45] .
AI multimodal data processing technology encompasses capabilities like image analysis and time-series prediction. It enables “minute-level integration + precise identification”: For medical imaging, it automatically screens for anomalies frame by frame, processing 20 times faster than manual methods while reducing missed diagnoses by 60%[46] .For chronic disease time-series data, it correlates diet, exercise, and indicator fluctuations in real time, issuing risk alerts 4-6 hours in advance and automatically generating preliminary analysis reports. This technological empowerment shifts clinical workflows from “human-driven” to “AI-assisted.” Physicians no longer need to handle tedious data processing, freeing them to focus on treatment decisions. Individual patient consultations are reduced to under 5 minutes, while early anomaly detection accuracy surpasses 90%[47] .
It should be clarified that AI multimodal data processing is not the sole AI pathway for enhancing clinical efficiency. Multiple AI technologies are synergistically advancing clinical care from diverse dimensions[48] . For instance, deep learning-based clinical decision support systems uncover implicit correlations within vast electronic health records, providing multidimensional treatment references for complex diseases. Natural language processing technology automatically extracts key information from electronic medical records and organizes it structurally, significantly reducing physicians' administrative workload while enhancing the utilization efficiency of medical record data. Reinforcement learning technology simulates the long-term effects of different treatment plans[49] , providing dynamic decision support for optimizing personalized treatment strategies for chronic diseases like cancer. These technologies complement and synergize with multimodal data processing techniques, collectively building an intelligent diagnosis and treatment ecosystem. This ecosystem delivers diversified solutions for advancing precision and efficiency in clinical practice[50] .
4.3 AI Personalized Modeling Empowers Dynamic Intervention in Digital Therapeutics—Addressing Shortcomings in Treatment Personalization
As a vital application branch of artificial intelligence, AI personalized modeling leverages core technologies such as user profiling, real-time data mining, and dynamic decision algorithms to empower digital therapeutics with precise dynamic interventions[51] . This approach specifically addresses the lack of personalization in traditional treatment plans, laying the groundwork for subsequent in-depth exploration of its technical pathways and practical value. Its technical logic and implementation pathways are detailed below[52] :
AI personalized modeling constitutes a proprietary predictive and intervention model technology system centered on individual data[53] , with the core objective of transcending reliance on group statistical patterns to achieve precise individual adaptation. This system primarily relies on three key technologies: Reinforcement learning optimizes intervention strategies through a “trial-and-error-feedback” mechanism, enhancing patient treatment adherence; Natural language processing technology enables deep analysis of patient text and voice data, accurately identifying cognitive distortions and potential psychological issues with emotional judgment accuracy exceeding 85%[54] ; multimodal interaction technology integrates biofeedback with VR/AR environments, enabling real-time linkage between physiological signals and intervention measures. These technologies all revolve around individual data, ensuring models precisely match each patient's specific needs.
Digital therapy dynamic intervention[55] represents a novel, real-time data-driven treatment paradigm characterized by “dynamicity” and “actionability.” Serving as a critical data gateway and intervention vehicle for AI personalized modeling, it fulfills three key functions: comprehensively collecting multidimensional patient data including genetic profiling, health metrics, and treatment task completion; translating AI model predictions into actionable interventions such as medication reminders and customized dietary plans; and continuously gathering feedback data to support iterative model optimization, ensuring treatment plans dynamically adjust to patient status[56] .
AI personalized modeling and digital therapeutics achieve deep synergistic empowerment through a closed-loop mechanism of “data collection-model prediction-intervention adjustment-feedback optimization.” AI personalized modeling provides precise decision support for digital therapeutics by mining individual data to predict patient responses to different treatment plans. Digital therapeutics, in turn, supply continuous data streams and practical scenarios to AI models, translating model predictions into concrete therapeutic actions while feeding back data to optimize the models[57] . This synergy liberates treatment plans from reliance on traditional group guidelines, achieving the personalized treatment goal of “one plan per person with real-time dynamic adjustments.”
In practical clinical applications, this collaborative model has demonstrated significant value across multiple domains. For instance, MedTech's diabetes digital therapeutics platform deployed in Hainan uses AI algorithms to analyze multidimensional patient data—including blood glucose and dietary patterns—to generate personalized management plans. By establishing a tripartite coordination system linking patients, physicians, and specialists, the platform increased fasting blood glucose compliance rates among managed diabetes patients in the pilot region by 18.63%[58] .
The synergistic model of AI personalized modeling and digital therapy dynamic intervention fundamentally resolves the core pain point of traditional treatment schemes being “one size fits all.” Traditional approaches, often based on group statistical patterns, struggle to accommodate individual patient differences, leading to inconsistent treatment outcomes. This collaborative model centers on individual data, achieving precise alignment between treatment plans and patient characteristics through AI model predictions[59] and digital therapy interventions. Whether tailoring medication regimens based on genetic variations or adjusting rehabilitation plans according to behavioral data, this approach ensures targeted and effective treatment, completely addressing the shortcomings of traditional treatment plans in personalized adaptation.
This collaborative model is not the sole pathway for AI to address the personalized treatment gap. AI can also achieve this goal through: - Providing personalized diagnostic and treatment references via deep learning clinical decision systems - Supporting targeted therapies with AI-assisted genetic testing - Delivering real-time intervention recommendations through wearable devices and AI early warning systems - Expanding personalized treatment coverage via AI telemedicine platforms These complementary approaches collectively build a diversified AI-driven personalized healthcare technology ecosystem.
5.1 The Dual Constraints of Data Quality and Privacy Protection
The multimodal nature of medical data leads to heterogeneous formats and inconsistent standards. A significant portion of real-world data suffers from missing values and noise interference. Professional annotation requires substantial medical resources, further exacerbating the scarcity of high-quality training data. Simultaneously, regulations like GDPR[60] and the Personal Information Protection Law impose stringent compliance requirements across data collection, transmission, and utilization. While anonymization and de-identification techniques mitigate privacy risks, they may compromise certain data values. Furthermore, inadequate cross-institutional data-sharing mechanisms, coupled with data silos created by healthcare institutions for security and competitive reasons, hinder AI models' access to large-scale generalized data[61]. This severely constrains model performance enhancement and scenario adaptability.
5.2 Insufficient Clinical Interpretability Hinders Practical Implementation
Current mainstream deep learning models are predominantly “black box” systems, lacking human-understandable logical chains in their decision-making processes. In clinical settings, AI may accurately identify lesions or recommend treatment plans but cannot clearly articulate the basis for its judgments—for example, it cannot explain why a particular drug class is prioritized or the logical connection between a specific imaging feature and a disease[62] . This ambiguity not only diminishes clinicians' trust in AI tools but also fails to meet healthcare's core requirements for traceable and verifiable diagnostic processes. Particularly in diagnosing complex conditions, where physicians bear legal and medical responsibility for decisions, AI's “black box” nature hinders its integration into core clinical workflows. Consequently, many technically mature AI products[63] remain confined to supplementary reference roles, struggling to achieve widespread clinical adoption.
5.3 Ethical Risks and the Practical Dilemma of Defining Responsibility
Ethical controversies surrounding AI applications in healthcare are increasingly prominent, with the primary issue being ambiguous liability. When AI diagnoses result in misdiagnoses, missed diagnoses, or recommended treatment plans lead to medical disputes, the lack of clear legal frameworks and industry standards for allocating responsibility among developers, healthcare institutions, and treating physicians creates significant challenges in dispute resolution. Second, algorithmic bias may exacerbate healthcare inequities[64] : if training data exhibits demographic representativeness bias, AI diagnostic accuracy may significantly decline for specific groups—such as higher misdiagnosis rates among rare disease patients or elderly populations. Furthermore, unclear boundaries for AI autonomous decision-making, coupled with overreliance on AI, may erode physicians' clinical judgment capabilities. This raises ethical concerns about “technological dependency,” potentially disrupting traditional doctor-patient relationships and medical liability frameworks.
5.4 Commercialization Faces Multiple Practical Barriers
The commercialization of AI medical products is constrained by multiple stages including R&D, validation, and monetization. During the R&D phase, products must undergo rigorous clinical trials to validate efficacy and safety. For instance, the FDA requires[65]prospective controlled studies, which not only span several years but also demand substantial investments in data collection, sample recruitment, and outcome analysis. Market access is complicated by inconsistent medical regulatory standards across countries and regions, requiring products to adapt to multiple compliance requirements and further increasing R&D costs and time. Regarding monetization models, hospitals' willingness to pay is directly influenced by insurance policies and departmental budgets. Most AI products remain excluded from insurance reimbursement lists, forcing medical institutions to bear procurement and operational costs independently, which dampens purchasing enthusiasm[66] .Simultaneously, quantifying the value of AI healthcare products remains challenging. Demonstrating their impact on clinical efficiency and patient outcomes through straightforward cost-benefit data is difficult, further hindering commercialization efforts.
Over the next 5-10 years, AI healthcare will steadily advance toward greater precision, reliability, and accessibility by addressing these existing challenges. Technologically, multimodal fusion will emerge as a core development direction. By integrating diverse data sources—including text, imaging, genomic information, and physiological signals—it will enable more comprehensive patient profiling. This will elevate AI from single-function assistance to end-to-end clinical decision support, achieving intelligent coverage across the entire chain from disease screening and diagnosis to treatment and rehabilitation management.
As the healthcare sector undergoes a critical transition toward precision, efficiency, and personalization, artificial intelligence has emerged as the core engine driving this change through breakthrough applications in balanced resource allocation, optimized diagnostic workflows, and personalized interventions. This approach tackles deep-seated challenges in traditional models, including imbalanced resource allocation, insufficient efficiency and accuracy, and inadequate personalized adaptation, and provides viable solutions for upgrading global healthcare systems universally and intelligently through technical pathways like federated learning, multimodal data processing, and personalized modeling.
However, dual constraints on data quality and privacy protection, insufficient clinical interpretability, and the challenge of defining ethical responsibilities remain tangible barriers to the large-scale implementation of AI in healthcare. Moving forward, a dual-pronged approach of “technological breakthroughs + ecosystem co-creation” is essential. On one hand, technological and institutional innovations must be strengthened across dimensions like algorithmic explainability, data governance systems, and ethical regulatory frameworks. On the other hand, deep collaboration among medical institutions, technology companies, and regulatory bodies must be fostered to continuously expand the application boundaries of AI healthcare while ensuring safety and compliance. Only through this approach can the true potential of artificial intelligence be unleashed, propelling clinical diagnosis and treatment from an “experience-driven” to a “data-intelligence-driven” paradigm shift. This will ultimately achieve universal access to healthcare resources, comprehensive improvements in treatment quality, and personalized safeguarding of patient health, laying a solid foundation for building the next-generation intelligent healthcare ecosystem.
The authors declare no competing financial interests.
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I am delighted to publish our manuscript entitled "A Perspective on Cocaine Induced Stroke - Its Mechanisms and Management" in the Journal of Neuroscience and Neurological Surgery. The peer review process, support from the editorial office, and quality of the journal are excellent. The manuscripts published are of high quality and of excellent scientific value. I recommend this journal very much to colleagues.
Dr.Tania Muñoz, My experience as researcher and author of a review article in The Journal Clinical Cardiology and Interventions has been very enriching and stimulating. The editorial team is excellent, performs its work with absolute responsibility and delivery. They are proactive, dynamic and receptive to all proposals. Supporting at all times the vast universe of authors who choose them as an option for publication. The team of review specialists, members of the editorial board, are brilliant professionals, with remarkable performance in medical research and scientific methodology. Together they form a frontline team that consolidates the JCCI as a magnificent option for the publication and review of high-level medical articles and broad collective interest. I am honored to be able to share my review article and open to receive all your comments.
“The peer review process of JPMHC is quick and effective. Authors are benefited by good and professional reviewers with huge experience in the field of psychology and mental health. The support from the editorial office is very professional. People to contact to are friendly and happy to help and assist any query authors might have. Quality of the Journal is scientific and publishes ground-breaking research on mental health that is useful for other professionals in the field”.
Dear editorial department: On behalf of our team, I hereby certify the reliability and superiority of the International Journal of Clinical Case Reports and Reviews in the peer review process, editorial support, and journal quality. Firstly, the peer review process of the International Journal of Clinical Case Reports and Reviews is rigorous, fair, transparent, fast, and of high quality. The editorial department invites experts from relevant fields as anonymous reviewers to review all submitted manuscripts. These experts have rich academic backgrounds and experience, and can accurately evaluate the academic quality, originality, and suitability of manuscripts. The editorial department is committed to ensuring the rigor of the peer review process, while also making every effort to ensure a fast review cycle to meet the needs of authors and the academic community. Secondly, the editorial team of the International Journal of Clinical Case Reports and Reviews is composed of a group of senior scholars and professionals with rich experience and professional knowledge in related fields. The editorial department is committed to assisting authors in improving their manuscripts, ensuring their academic accuracy, clarity, and completeness. Editors actively collaborate with authors, providing useful suggestions and feedback to promote the improvement and development of the manuscript. We believe that the support of the editorial department is one of the key factors in ensuring the quality of the journal. Finally, the International Journal of Clinical Case Reports and Reviews is renowned for its high- quality articles and strict academic standards. The editorial department is committed to publishing innovative and academically valuable research results to promote the development and progress of related fields. The International Journal of Clinical Case Reports and Reviews is reasonably priced and ensures excellent service and quality ratio, allowing authors to obtain high-level academic publishing opportunities in an affordable manner. I hereby solemnly declare that the International Journal of Clinical Case Reports and Reviews has a high level of credibility and superiority in terms of peer review process, editorial support, reasonable fees, and journal quality. Sincerely, Rui Tao.
Clinical Cardiology and Cardiovascular Interventions I testity the covering of the peer review process, support from the editorial office, and quality of the journal.
Clinical Cardiology and Cardiovascular Interventions, we deeply appreciate the interest shown in our work and its publication. It has been a true pleasure to collaborate with you. The peer review process, as well as the support provided by the editorial office, have been exceptional, and the quality of the journal is very high, which was a determining factor in our decision to publish with you.
The peer reviewers process is quick and effective, the supports from editorial office is excellent, the quality of journal is high. I would like to collabroate with Internatioanl journal of Clinical Case Reports and Reviews journal clinically in the future time.
Clinical Cardiology and Cardiovascular Interventions, I would like to express my sincerest gratitude for the trust placed in our team for the publication in your journal. It has been a true pleasure to collaborate with you on this project. I am pleased to inform you that both the peer review process and the attention from the editorial coordination have been excellent. Your team has worked with dedication and professionalism to ensure that your publication meets the highest standards of quality. We are confident that this collaboration will result in mutual success, and we are eager to see the fruits of this shared effort.
Dear Dr. Jessica Magne, Editorial Coordinator 0f Clinical Cardiology and Cardiovascular Interventions, I hope this message finds you well. I want to express my utmost gratitude for your excellent work and for the dedication and speed in the publication process of my article titled "Navigating Innovation: Qualitative Insights on Using Technology for Health Education in Acute Coronary Syndrome Patients." I am very satisfied with the peer review process, the support from the editorial office, and the quality of the journal. I hope we can maintain our scientific relationship in the long term.
Dear Monica Gissare, - Editorial Coordinator of Nutrition and Food Processing. ¨My testimony with you is truly professional, with a positive response regarding the follow-up of the article and its review, you took into account my qualities and the importance of the topic¨.
Dear Dr. Jessica Magne, Editorial Coordinator 0f Clinical Cardiology and Cardiovascular Interventions, The review process for the article “The Handling of Anti-aggregants and Anticoagulants in the Oncologic Heart Patient Submitted to Surgery” was extremely rigorous and detailed. From the initial submission to the final acceptance, the editorial team at the “Journal of Clinical Cardiology and Cardiovascular Interventions” demonstrated a high level of professionalism and dedication. The reviewers provided constructive and detailed feedback, which was essential for improving the quality of our work. Communication was always clear and efficient, ensuring that all our questions were promptly addressed. The quality of the “Journal of Clinical Cardiology and Cardiovascular Interventions” is undeniable. It is a peer-reviewed, open-access publication dedicated exclusively to disseminating high-quality research in the field of clinical cardiology and cardiovascular interventions. The journal's impact factor is currently under evaluation, and it is indexed in reputable databases, which further reinforces its credibility and relevance in the scientific field. I highly recommend this journal to researchers looking for a reputable platform to publish their studies.
Dear Editorial Coordinator of the Journal of Nutrition and Food Processing! "I would like to thank the Journal of Nutrition and Food Processing for including and publishing my article. The peer review process was very quick, movement and precise. The Editorial Board has done an extremely conscientious job with much help, valuable comments and advices. I find the journal very valuable from a professional point of view, thank you very much for allowing me to be part of it and I would like to participate in the future!”
Dealing with The Journal of Neurology and Neurological Surgery was very smooth and comprehensive. The office staff took time to address my needs and the response from editors and the office was prompt and fair. I certainly hope to publish with this journal again.Their professionalism is apparent and more than satisfactory. Susan Weiner
My Testimonial Covering as fellowing: Lin-Show Chin. The peer reviewers process is quick and effective, the supports from editorial office is excellent, the quality of journal is high. I would like to collabroate with Internatioanl journal of Clinical Case Reports and Reviews.
My experience publishing in Psychology and Mental Health Care was exceptional. The peer review process was rigorous and constructive, with reviewers providing valuable insights that helped enhance the quality of our work. The editorial team was highly supportive and responsive, making the submission process smooth and efficient. The journal's commitment to high standards and academic rigor makes it a respected platform for quality research. I am grateful for the opportunity to publish in such a reputable journal.
My experience publishing in International Journal of Clinical Case Reports and Reviews was exceptional. I Come forth to Provide a Testimonial Covering the Peer Review Process and the editorial office for the Professional and Impartial Evaluation of the Manuscript.
I would like to offer my testimony in the support. I have received through the peer review process and support the editorial office where they are to support young authors like me, encourage them to publish their work in your esteemed journals, and globalize and share knowledge globally. I really appreciate your journal, peer review, and editorial office.
Dear Agrippa Hilda- Editorial Coordinator of Journal of Neuroscience and Neurological Surgery, "The peer review process was very quick and of high quality, which can also be seen in the articles in the journal. The collaboration with the editorial office was very good."
I would like to express my sincere gratitude for the support and efficiency provided by the editorial office throughout the publication process of my article, “Delayed Vulvar Metastases from Rectal Carcinoma: A Case Report.” I greatly appreciate the assistance and guidance I received from your team, which made the entire process smooth and efficient. The peer review process was thorough and constructive, contributing to the overall quality of the final article. I am very grateful for the high level of professionalism and commitment shown by the editorial staff, and I look forward to maintaining a long-term collaboration with the International Journal of Clinical Case Reports and Reviews.
To Dear Erin Aust, I would like to express my heartfelt appreciation for the opportunity to have my work published in this esteemed journal. The entire publication process was smooth and well-organized, and I am extremely satisfied with the final result. The Editorial Team demonstrated the utmost professionalism, providing prompt and insightful feedback throughout the review process. Their clear communication and constructive suggestions were invaluable in enhancing my manuscript, and their meticulous attention to detail and dedication to quality are truly commendable. Additionally, the support from the Editorial Office was exceptional. From the initial submission to the final publication, I was guided through every step of the process with great care and professionalism. The team's responsiveness and assistance made the entire experience both easy and stress-free. I am also deeply impressed by the quality and reputation of the journal. It is an honor to have my research featured in such a respected publication, and I am confident that it will make a meaningful contribution to the field.
"I am grateful for the opportunity of contributing to [International Journal of Clinical Case Reports and Reviews] and for the rigorous review process that enhances the quality of research published in your esteemed journal. I sincerely appreciate the time and effort of your team who have dedicatedly helped me in improvising changes and modifying my manuscript. The insightful comments and constructive feedback provided have been invaluable in refining and strengthening my work".
I thank the ‘Journal of Clinical Research and Reports’ for accepting this article for publication. This is a rigorously peer reviewed journal which is on all major global scientific data bases. I note the review process was prompt, thorough and professionally critical. It gave us an insight into a number of important scientific/statistical issues. The review prompted us to review the relevant literature again and look at the limitations of the study. The peer reviewers were open, clear in the instructions and the editorial team was very prompt in their communication. This journal certainly publishes quality research articles. I would recommend the journal for any future publications.
Dear Jessica Magne, with gratitude for the joint work. Fast process of receiving and processing the submitted scientific materials in “Clinical Cardiology and Cardiovascular Interventions”. High level of competence of the editors with clear and correct recommendations and ideas for enriching the article.
We found the peer review process quick and positive in its input. The support from the editorial officer has been very agile, always with the intention of improving the article and taking into account our subsequent corrections.
My article, titled 'No Way Out of the Smartphone Epidemic Without Considering the Insights of Brain Research,' has been republished in the International Journal of Clinical Case Reports and Reviews. The review process was seamless and professional, with the editors being both friendly and supportive. I am deeply grateful for their efforts.
To Dear Erin Aust – Editorial Coordinator of Journal of General Medicine and Clinical Practice! I declare that I am absolutely satisfied with your work carried out with great competence in following the manuscript during the various stages from its receipt, during the revision process to the final acceptance for publication. Thank Prof. Elvira Farina
Dear Jessica, and the super professional team of the ‘Clinical Cardiology and Cardiovascular Interventions’ I am sincerely grateful to the coordinated work of the journal team for the no problem with the submission of my manuscript: “Cardiometabolic Disorders in A Pregnant Woman with Severe Preeclampsia on the Background of Morbid Obesity (Case Report).” The review process by 5 experts was fast, and the comments were professional, which made it more specific and academic, and the process of publication and presentation of the article was excellent. I recommend that my colleagues publish articles in this journal, and I am interested in further scientific cooperation. Sincerely and best wishes, Dr. Oleg Golyanovskiy.
Dear Ashley Rosa, Editorial Coordinator of the journal - Psychology and Mental Health Care. " The process of obtaining publication of my article in the Psychology and Mental Health Journal was positive in all areas. The peer review process resulted in a number of valuable comments, the editorial process was collaborative and timely, and the quality of this journal has been quickly noticed, resulting in alternative journals contacting me to publish with them." Warm regards, Susan Anne Smith, PhD. Australian Breastfeeding Association.
Dear Jessica Magne, Editorial Coordinator, Clinical Cardiology and Cardiovascular Interventions, Auctores Publishing LLC. I appreciate the journal (JCCI) editorial office support, the entire team leads were always ready to help, not only on technical front but also on thorough process. Also, I should thank dear reviewers’ attention to detail and creative approach to teach me and bring new insights by their comments. Surely, more discussions and introduction of other hemodynamic devices would provide better prevention and management of shock states. Your efforts and dedication in presenting educational materials in this journal are commendable. Best wishes from, Farahnaz Fallahian.
Dear Maria Emerson, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews, Auctores Publishing LLC. I am delighted to have published our manuscript, "Acute Colonic Pseudo-Obstruction (ACPO): A rare but serious complication following caesarean section." I want to thank the editorial team, especially Maria Emerson, for their prompt review of the manuscript, quick responses to queries, and overall support. Yours sincerely Dr. Victor Olagundoye.
Dear Ashley Rosa, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews. Many thanks for publishing this manuscript after I lost confidence the editors were most helpful, more than other journals Best wishes from, Susan Anne Smith, PhD. Australian Breastfeeding Association.
Dear Agrippa Hilda, Editorial Coordinator, Journal of Neuroscience and Neurological Surgery. The entire process including article submission, review, revision, and publication was extremely easy. The journal editor was prompt and helpful, and the reviewers contributed to the quality of the paper. Thank you so much! Eric Nussbaum, MD
Dr Hala Al Shaikh This is to acknowledge that the peer review process for the article ’ A Novel Gnrh1 Gene Mutation in Four Omani Male Siblings, Presentation and Management ’ sent to the International Journal of Clinical Case Reports and Reviews was quick and smooth. The editorial office was prompt with easy communication.
Dear Erin Aust, Editorial Coordinator, Journal of General Medicine and Clinical Practice. We are pleased to share our experience with the “Journal of General Medicine and Clinical Practice”, following the successful publication of our article. The peer review process was thorough and constructive, helping to improve the clarity and quality of the manuscript. We are especially thankful to Ms. Erin Aust, the Editorial Coordinator, for her prompt communication and continuous support throughout the process. Her professionalism ensured a smooth and efficient publication experience. The journal upholds high editorial standards, and we highly recommend it to fellow researchers seeking a credible platform for their work. Best wishes By, Dr. Rakhi Mishra.
Dear Jessica Magne, Editorial Coordinator, Clinical Cardiology and Cardiovascular Interventions, Auctores Publishing LLC. The peer review process of the journal of Clinical Cardiology and Cardiovascular Interventions was excellent and fast, as was the support of the editorial office and the quality of the journal. Kind regards Walter F. Riesen Prof. Dr. Dr. h.c. Walter F. Riesen.
Dear Ashley Rosa, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews, Auctores Publishing LLC. Thank you for publishing our article, Exploring Clozapine's Efficacy in Managing Aggression: A Multiple Single-Case Study in Forensic Psychiatry in the international journal of clinical case reports and reviews. We found the peer review process very professional and efficient. The comments were constructive, and the whole process was efficient. On behalf of the co-authors, I would like to thank you for publishing this article. With regards, Dr. Jelle R. Lettinga.
Dear Clarissa Eric, Editorial Coordinator, Journal of Clinical Case Reports and Studies, I would like to express my deep admiration for the exceptional professionalism demonstrated by your journal. I am thoroughly impressed by the speed of the editorial process, the substantive and insightful reviews, and the meticulous preparation of the manuscript for publication. Additionally, I greatly appreciate the courteous and immediate responses from your editorial office to all my inquiries. Best Regards, Dariusz Ziora
Dear Chrystine Mejia, Editorial Coordinator, Journal of Neurodegeneration and Neurorehabilitation, Auctores Publishing LLC, We would like to thank the editorial team for the smooth and high-quality communication leading up to the publication of our article in the Journal of Neurodegeneration and Neurorehabilitation. The reviewers have extensive knowledge in the field, and their relevant questions helped to add value to our publication. Kind regards, Dr. Ravi Shrivastava.
Dear Clarissa Eric, Editorial Coordinator, Journal of Clinical Case Reports and Studies, Auctores Publishing LLC, USA Office: +1-(302)-520-2644. I would like to express my sincere appreciation for the efficient and professional handling of my case report by the ‘Journal of Clinical Case Reports and Studies’. The peer review process was not only fast but also highly constructive—the reviewers’ comments were clear, relevant, and greatly helped me improve the quality and clarity of my manuscript. I also received excellent support from the editorial office throughout the process. Communication was smooth and timely, and I felt well guided at every stage, from submission to publication. The overall quality and rigor of the journal are truly commendable. I am pleased to have published my work with Journal of Clinical Case Reports and Studies, and I look forward to future opportunities for collaboration. Sincerely, Aline Tollet, UCLouvain.
Dear Ms. Mayra Duenas, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews. “The International Journal of Clinical Case Reports and Reviews represented the “ideal house” to share with the research community a first experience with the use of the Simeox device for speech rehabilitation. High scientific reputation and attractive website communication were first determinants for the selection of this Journal, and the following submission process exceeded expectations: fast but highly professional peer review, great support by the editorial office, elegant graphic layout. Exactly what a dynamic research team - also composed by allied professionals - needs!" From, Chiara Beccaluva, PT - Italy.
Dear Maria Emerson, Editorial Coordinator, we have deeply appreciated the professionalism demonstrated by the International Journal of Clinical Case Reports and Reviews. The reviewers have extensive knowledge of our field and have been very efficient and fast in supporting the process. I am really looking forward to further collaboration. Thanks. Best regards, Dr. Claudio Ligresti
Dear Chrystine Mejia, Editorial Coordinator, Journal of Neurodegeneration and Neurorehabilitation. “The peer review process was efficient and constructive, and the editorial office provided excellent communication and support throughout. The journal ensures scientific rigor and high editorial standards, while also offering a smooth and timely publication process. We sincerely appreciate the work of the editorial team in facilitating the dissemination of innovative approaches such as the Bonori Method.” Best regards, Dr. Matteo Bonori.
I recommend without hesitation submitting relevant papers on medical decision making to the International Journal of Clinical Case Reports and Reviews. I am very grateful to the editorial staff. Maria Emerson was a pleasure to communicate with. The time from submission to publication was an extremely short 3 weeks. The editorial staff submitted the paper to three reviewers. Two of the reviewers commented positively on the value of publishing the paper. The editorial staff quickly recognized the third reviewer’s comments as an unjust attempt to reject the paper. I revised the paper as recommended by the first two reviewers.
Dear Maria Emerson, Editorial Coordinator, Journal of Clinical Research and Reports. Thank you for publishing our case report: "Clinical Case of Effective Fetal Stem Cells Treatment in a Patient with Autism Spectrum Disorder" within the "Journal of Clinical Research and Reports" being submitted by the team of EmCell doctors from Kyiv, Ukraine. We much appreciate a professional and transparent peer-review process from Auctores. All research Doctors are so grateful to your Editorial Office and Auctores Publishing support! I amiably wish our article publication maintained a top quality of your International Scientific Journal. My best wishes for a prosperity of the Journal of Clinical Research and Reports. Hope our scientific relationship and cooperation will remain long lasting. Thank you very much indeed. Kind regards, Dr. Andriy Sinelnyk Cell Therapy Center EmCell
Dear Editorial Team, Clinical Cardiology and Cardiovascular Interventions. It was truly a rewarding experience to work with the journal “Clinical Cardiology and Cardiovascular Interventions”. The peer review process was insightful and encouraging, helping us refine our work to a higher standard. The editorial office offered exceptional support with prompt and thoughtful communication. I highly value the journal’s role in promoting scientific advancement and am honored to be part of it. Best regards, Meng-Jou Lee, MD, Department of Anesthesiology, National Taiwan University Hospital.
Dear Editorial Team, Journal-Clinical Cardiology and Cardiovascular Interventions, “Publishing my article with Clinical Cardiology and Cardiovascular Interventions has been a highly positive experience. The peer-review process was rigorous yet supportive, offering valuable feedback that strengthened my work. The editorial team demonstrated exceptional professionalism, prompt communication, and a genuine commitment to maintaining the highest scientific standards. I am very pleased with the publication quality and proud to be associated with such a reputable journal.” Warm regards, Dr. Mahmoud Kamal Moustafa Ahmed
Dear Maria Emerson, Editorial Coordinator of ‘International Journal of Clinical Case Reports and Reviews’, I appreciate the opportunity to publish my article with your journal. The editorial office provided clear communication during the submission and review process, and I found the overall experience professional and constructive. Best regards, Elena Salvatore.
Dear Mayra Duenas, Editorial Coordinator of ‘International Journal of Clinical Case Reports and Reviews Herewith I confirm an optimal peer review process and a great support of the editorial office of the present journal
Dear Editorial Team, Clinical Cardiology and Cardiovascular Interventions. I am really grateful for the peers review; their feedback gave me the opportunity to reflect on the message and impact of my work and to ameliorate the article. The editors did a great job in addition by encouraging me to continue with the process of publishing.
Dear Cecilia Lilly, Editorial Coordinator, Endocrinology and Disorders, Thank you so much for your quick response regarding reviewing and all process till publishing our manuscript entitled: Prevalence of Pre-Diabetes and its Associated Risk Factors Among Nile College Students, Sudan. Best regards, Dr Mamoun Magzoub.
International Journal of Clinical Case Reports and Reviews is a high quality journal that has a clear and concise submission process. The peer review process was comprehensive and constructive. Support from the editorial office was excellent, since the administrative staff were responsive. The journal provides a fast and timely publication timeline.
Dear Maria Emerson, Editorial Coordinator of International Journal of Clinical Case Reports and Reviews, What distinguishes International Journal of Clinical Case Report and Review is not only the scientific rigor of its publications, but the intellectual climate in which research is evaluated. The submission process is refreshingly free of unnecessary formal barriers and bureaucratic rituals that often complicate academic publishing without adding real value. The peer-review system is demanding yet constructive, guided by genuine scientific dialogue rather than hierarchical or authoritarian attitudes. Reviewers act as collaborators in improving the manuscript, not as gatekeepers imposing arbitrary standards. This journal offers a rare balance: high methodological standards combined with a respectful, transparent, and supportive editorial approach. In an era where publishing can feel more burdensome than research itself, this platform restores the original purpose of peer review — to refine ideas, not to obstruct them Prof. Perlat Kapisyzi, FCCP PULMONOLOGIST AND THORACIC IMAGING.
Dear Grace Pierce, International Journal of Clinical Case Reports and Reviews I appreciate the opportunity to review for Auctore Journal, as the overall editorial process was smooth, transparent and professionally managed. This journal maintains high scientific standards and ensures timely communications with authors, which is truly commendable. I would like to express my special thanks to editor Grace Pierce for his constant guidance, promt responses, and supportive coordination throughout the review process. I am also greatful to Eleanor Bailey from the finance department for her clear communication and efficient handling of all administrative matters. Overall, my experience with Auctore Journal has been highly positive and rewarding. Best regards, Sabita sinha
Dear Mayra Duenas, Editorial Coordinator of the journal IJCCR, I write here a little on my experience as an author submitting to the International Journal of Clinical Case Reports and Reviews (IJCCR). This was my first submission to IJCCR and my manuscript was inherently an outsider’s effort. It attempted to broadly identify and then make some sense of life’s under-appreciated mysteries. I initially had responded to a request for possible submissions. I then contacted IJCCR with a tentative topic for a manuscript. They quickly got back with an approval for the submission, but with a particular requirement that it be medically relevant. I then put together a manuscript and submitted it. After the usual back-and-forth over forms and formality, the manuscript was sent off for reviews. Within 2 weeks I got back 4 reviews which were both helpful and also surprising. Surprising in that the topic was somewhat foreign to medical literature. My subsequent updates in response to the reviewer comments went smoothly and in short order I had a series of proofs to evaluate. All in all, the whole publication process seemed outstanding. It was both helpful in terms of the paper’s content and also in terms of its efficient and friendly communications. Thank you all very much. Sincerely, Ted Christopher, Rochester, NY.
