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Review Article | DOI: https://doi.org/10.31579/2690-4861/987
1 Clinical Research Center-Translational Medicine Laboratory of PLA No.924 Hospital, Guilin 541000, China.
2 Undergraduate, Student, Wuhan East Lake College, Wuhan 430212, Hubei Province, China.
*Corresponding Author: Lu Kun, Clinical Research Center-Translational Medicine Laboratory of PLA No.924 Hospital, Guilin 541000, China.
Citation: Lu Kun, Wang Yalin, Wang Shutong, Ying Qunbo, Wang Feilong, et al., (2026), Artificial Intelligence Based Healthcare Device Development, International Journal of Clinical Case Reports and Reviews, 34(3); DOI:10.31579/2690-4861/987
Copyright: © 2026, Lu Kun. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Received: 02 February 2026 | Accepted: 17 February 2026 | Published: 26 February 2026
Keywords: deep learning; machine learning; medical big data analytics; assisted diagnosis; intelligent decision support; personalized diagnosis; treatment; intervention models
Medical devices are currently at a pivotal stage driven by policy and technological innovation. Traditional device development suffers from lengthy cycles, high costs, and poor alignment with clinical needs, making it difficult to adapt to rapidly evolving healthcare scenarios and high-end diagnostic demands. Breakthroughs in artificial intelligence (AI) technologies, particularly deep learning (DL), machine learning (ML), and medical big data analytics (MBDAT), offer critical solutions to this challenge: deep learning (DL) empowers the development of medical imaging devices, enabling precise lesion identification; machine learning (ML) supports diagnostic aids, intelligent decision support systems, and digital therapeutics, constructing personalized treatment and intervention models while optimizing data processing efficiency for laboratory biochemical analyzers; medical big data analytics drives innovation in drug response prediction devices, enhancing medication precision and safety through multi-source data integration and mining. This paper provides a systematic review of advancements in this field across three dimensions: specific AI applications in medical device R&D, current R&D status and trends enabled by AI, and existing challenges and future directions.
The integration of artificial intelligence (AI) technologies into the development of healthcare devices is driving a profound transformation [1]. The global AI medical device market is growing at an astonishing pace, reaching $2.229 billion in 2024 and projected to surpass $80 billion by 2034, with a compound annual growth rate of 44.53% [2]. As core branches of AI, deep learning (DL) [3], machine learning (ML), and medical big data analytics (MBDAT) [4], not only significantly enhance the efficiency of healthcare services but also propel traditional medical models toward precision and intelligence. This advancement is achieved by strengthening data processing capabilities, improving analytical accuracy, and enhancing the integration of multi-source medical data [5].
2.1 Deep Learning (DL) Empowering Medical Imaging Device Development:
Within the AI-enabled healthcare ecosystem, deep learning (DL) [6]. has emerged as a core enabling technology for medical imaging device development due to its superior processing capabilities for unstructured data. In comparison to traditional image analysis reliant on human expertise, deep learning overcomes subjective errors and efficiency bottlenecks, empowering devices with autonomous image interpretation and critical information recognition capabilities [7]. Its application value in medical imaging directly determines the core direction for upgrading medical imaging devices toward intelligence and precision [8]. The integration of deep learning—centered on artificial neural networks—with medical imaging devices follows a clear underlying logic: without requiring predefined image feature dimensions, deep learning autonomously extracts subtle lesion characteristics and anatomical structural differences through multi-layered network architectures [9]. More crucially, model performance continuously improves with expanded training datasets and optimized network architectures. This “data-driven” iterative model aligns precisely with medical imaging devices' long-term research and development (R&D) requirements for “high resolution accuracy and stable output,” providing the technical foundation for sustained performance optimization. In the integration of deep learning with medical imaging devices, the core objective is to enhance imaging analysis capabilities through technological convergence. Current mainstream development approaches focus on synergizing model architecture with device functionality: deeply integrating typical architectures like Convolutional Neural Networks (CNN) [10] and Transformers with imaging acquisition devices to strengthen their ability to recognize both local details and global correlations within images. This addresses the technical challenges of missed or misdiagnoses inherent in traditional devices reliant on manual interpretation. The critical challenge lies in achieving balanced optimization of model performance. This requires compressing model parameters while maintaining diagnostic accuracy to accommodate devices with varying computational capabilities, such as portable imaging equipment [11]. Simultaneously, training models on multi-scenario datasets reduces dependence on specific imaging conditions, ensuring device versatility across diverse settings like primary care and intraoperative assistance [12].
2.2 Machine Learning (ML) Enables Development of Wearable Monitoring and Diagnostic/Therapeutic Support Devices
Within the AI-enabled healthcare technology framework, machine learning serves as the core enabling technology for wearable health monitors to overcome the limitations of passive data collection and for intelligent diagnostic/therapeutic support devices to break free from reliance on standardized protocols [13]. Traditional wearable monitors can only collect basic physiological data such as heart rate and blood oxygen levels, lacking the capability to analyze real-time correlations between abnormal readings. This results in a disconnect between data collection and application. Traditional diagnostic/therapeutic aids rely on fixed clinical pathways, struggling to adapt to individual variations like patient age, underlying conditions, and genetic traits, often resulting in fluctuating treatment efficacy rates. Machine learning, however, leverages its capabilities in real-time modeling of time-series data and structured data correlation mining to simultaneously address the core shortcomings of both device types [14]. This directly determines their technological trajectory from “tool-based devices” to “proactive intervention smart devices.” The underlying logic of integrating machine learning with these two device categories centers on “precision alignment between technical capabilities and device requirements.” This necessitates constructing distinct logical frameworks tailored to each device's functional positioning [15]: For wearable health monitoring devices, the core logic is “real-time modeling of dynamic physiological data and precision-response balance.” To quantify the relationship between monitoring accuracy and response time, a mathematical model must guide development. The specific formula is:
Where p represents monitoring accuracy, k denotes the device's baseline accuracy, λ is the attenuation coefficient related to hardware computational power, and t is the data response time. This model clearly demonstrates that longer response times cause exponential accuracy degradation. It directly guides the synergistic optimization of “sensor sampling frequency and algorithm computational efficiency” during development, thereby preventing the omission of anomalies due to response delays [16]. For intelligent diagnostic and therapeutic devices, the core logic lies in “associative mining of individual structured data and solution mapping.” Machine learning algorithms such as decision trees and random forests analyze structured data like patient electronic medical records and laboratory indicators to uncover latent correlations between individual characteristics and treatment outcomes [17]. This enables the construction of reusable personalized diagnostic and therapeutic models, generating tailored solutions for different patients. In the integration of machine learning with these two device categories, the core challenge lies in translating underlying logic into actionable R&D pathways, focusing on resolving “scenario adaptability” and “data validity” issues. For wearable health monitoring devices, machine learning's time-series analysis capabilities must be deeply integrated with sensor modules. Noise reduction techniques filter out motion interference and environmental electromagnetic interference to ensure data validity in complex scenarios. Simultaneously, machine learning model parameters must be compressed to fit the limited computational power of wearable hardware, preventing response delays due to insufficient processing capacity [18]. For intelligent diagnostic and therapeutic assistance devices, lightweight integration between machine learning models and hospital information systems is essential. This approach focuses solely on acquiring structured patient data, with data compatibility testing ensuring model adaptability across hospital data formats to minimize solution bias caused by format discrepancies. The key to this process lies in “functional boundary control.” The core value of machine learning is “real-time processing” and “personalized modeling.” It must avoid overstepping into core domains of medical big data analytics, such as data integration and cross-database collaboration, ensuring clear division of labor between these two technologies in device development [19].
2.3 Medical Big Data Analytics Technology (MBDAT) Empowers the Development of Drug Response Monitoring and Efficacy Assessment Devices
Against the backdrop of data-driven healthcare, medical big data analytics technology serves as the core enabling technology for drug response monitoring devices to overcome “subjective limitations” and for efficacy assessment devices to address “insufficient adaptation to individual differences.” Traditional drug response monitoring relies on physician subjective observation and standardized scales [20], failing to account for latent influencing factors such as genetic variations and concomitant medications, which can lead to missed adverse event diagnoses. Traditional efficacy evaluation relies on single-center, small-sample data, failing to establish efficacy benchmarks across diverse populations and often resulting in misjudgments of “same drug, different effects.” Medical big data analytics, however, leverages its capabilities in integrating multi-source heterogeneous data and uncovering cross-scenario patterns to simultaneously address the core pain points of both device types. This directly determines their technological trajectory from “empirical evaluation” to “data-driven precision judgment”. The underlying logic of integrating medical big data analytics with these two device categories centers on “end-to-end processing and pattern transformation of multi-source data,” following a closed-loop logic: “data integration → cleansing and standardization → correlation mining → quantitative output.” [21]. Multi-source data integration collects multidimensional medical data through standardized interfaces—including electronic health records, genomic data, real-time medication feedback, and multi-center clinical trial data—breaking down “data silos.” Data cleansing and standardization involves anonymizing integrated data, unifying formats, and removing outliers to prevent data quality issues from affecting analysis outcomes[22] ; Correlation pattern mining uses tools like correlation analysis and regression modeling to uncover hidden associations between individual characteristics and drug responses, as well as statistical patterns linking population traits to efficacy benchmarks; Quantified output translates identified patterns into quantifiable metrics directly accessible by medical devices, providing core data support for monitoring and evaluation functions. In integrating medical big data analytics with these two device categories, the core challenge lies in translating data mining capabilities into clinical utility for devices, focusing on resolving “data standardization” and “cross-database coordination” issues. For intelligent drug reaction monitoring devices, medical big data analysis modules must interface in real time with patient monitoring equipment. This enables automatic retrieval of real-time physiological metrics and historical data from medication recipients. Predefined “abnormality correlation rules” then automatically identify early signals of drug side effects, transforming traditional manual monitoring's “lagging judgments” into “real-time alerts” [23].
For precision efficacy assessment devices, medical big data analytics must integrate multi-center, multi-batch medication data to construct population-specific efficacy benchmark models. By inputting individual patient characteristics, the device can automatically match corresponding benchmark data, assisting physicians in evaluating drug suitability and reducing standardization bias risks. The critical challenge lies in balancing “data security with generalization capabilities” [24] —ensuring cross-database sharing security through federated learning and data anonymization while expanding sample coverage to enhance the generalization ability of medical big data analytics in uncovering patterns, thereby guaranteeing assessment accuracy across diverse clinical scenarios.
From the perspective of current technology implementation and R&D trends, deep learning—as the core application branch of artificial intelligence (AI) technology in the field of healthcare device R&D [25] —exemplifies the practical logic and efficacy of AI empowerment. The following section focuses on this core theme, analyzing the current status and development trends of AI-empowered healthcare device R&D.
3.1 Diagnostic Precision Dimension: Technology Iteration Driving Diagnostic Performance Enhancement
According to our survey, the changes and trends in diagnostic accuracy for medical imaging reveal the operational significance of deep learning systems in specific application scenarios. Our investigation indicates that diagnostic accuracy in medical imaging has progressively increased, ranging from 85% to 94%. This demonstrates that as technology evolves and datasets expand, deep learning's capabilities in image recognition continue to strengthen. Specifically, taking diabetic retinopathy screening as an example, the introduction of deep learning algorithms, driven by annotated data, has elevated diagnostic accuracy from a baseline of 79% to 95%.
Figure 1: Accuracy Improvement of Deep Learning in Medical Image Diagnosis [26].
This advancement stems from the synergy between deep learning model optimization and data augmentation strategies. The model's strengths in feature learning significantly enhance the recognition of image resolution and details, thereby facilitating earlier patient screening and intervention.
3.2 User Value Dimension: Experience Optimization Drives Clinical Acceptance of Medical Devices
User satisfaction scores indicate that applications such as AI medical imaging diagnostics achieved a rating of 8.5, reflecting high user appreciation for the convenience and accuracy delivered by deep learning technology. Evidently, public trust in AI for automated medical imaging analysis is growing, further accelerating the adoption and dissemination of deep learning applications
Figure 2: User Satisfaction Scores for AI Medical Imaging Technology.
Data derived from real-world healthcare applications indicates that high-accuracy imaging diagnostic systems not only meet clinicians' need for precise diagnosis but also enhance patients' healthcare experience.
3.3 Clinical Efficacy Dimension: Personalized Solutions Drive Continuous Treatment Improvement
Treatment improvement rate data further reveals the potential of deep learning in personalized medical solutions. For instance, while improvement rates in electronic monitoring and data analysis applications require further enhancement, personalized AI treatment plans demonstrate a 60% improvement rate [27]. This underscores the necessity of closely integrating deep learning applications with clinical practice. Practical cases show that successful deep learning implementation often relies on robust clinical data support and appropriate model tuning strategies to enhance algorithm effectiveness and accuracy [28].
Figure 3: Improvement Rates of Deep Learning in Personalized Medical Solutions.
Against this backdrop, continuously improving data quality and sample diversity have also become critical factors for optimizing deep learning model performance.
Based on the current research status across these three dimensions, AI technologies exemplified by deep learning have achieved significant breakthroughs in three key areas of healthcare device development: diagnostic and therapeutic precision, user value, and clinical efficacy. This has formed a complete value chain from technological implementation to user recognition. However, challenges remain, including the scarcity of high-quality data, insufficient cross-device coordination, and limited model generalization capabilities. Looking ahead, as data standardization systems are established, multi-technology collaboration deepens, and clinical needs are more fully integrated [29], AI will further propel medical devices toward greater precision, usability, and effectiveness, providing core support for the implementation of precision medicine.
While significant progress has been made in AI-enabled healthcare device R&D [30], the industry still faces multidimensional practical challenges that directly impact the depth of technology implementation and the realization of device application value:
4.1 Lagging Supply of High-Quality Medical Data and Standardization System Development
Current medical data suffers from “data silos” [31]. Data formats vary across hospitals and devices, and privacy regulations complicate cross-institutional sharing. Simultaneously, data annotation relies on specialized medical personnel, resulting in high costs and lengthy cycles. This scarcity of “large-scale, high-quality annotated data” required for deep learning and medical big data analysis directly constrains model training accuracy and generalization capabilities.
4.2 Insufficient AI Model Adaptability and Clinical Trustworthiness
Existing models are often trained under specific conditions, leading to significantly reduced recognition accuracy and assessment reliability when deployed on non-standard equipment in primary care settings or with special patient populations. Furthermore, the “black-box nature” of AI models makes it difficult for medical professionals to trace decision-making logic, resulting in low clinical trust in model outputs and hindering the widespread adoption of such devices in actual diagnosis and treatment [32].
4.3 Insufficient Synergy Between Regulatory Approval and Technological Iteration
AI medical devices exhibit rapid iteration characteristics, whereas traditional medical device approval processes feature lengthy cycles and fixed standards, making them ill-suited to accommodate the dynamic optimization demands of model algorithms. Concurrently, clinical validation standards for AI devices remain underdeveloped [33], leaving the quantification of model safety and efficacy during long-term use as an urgent industry challenge. To address these challenges, future AI-driven medical device development must advance in four key directions: data collaboration, model transparency, flexible approval, and deep clinical integration. First, establish a standardized data collaboration system. Create a national medical data sharing platform with unified data formats and anonymization standards to facilitate cross-institutional data exchange under compliance frameworks [34]. Simultaneously, adopt semi-supervised and weakly supervised learning techniques to reduce reliance on manually annotated data, alleviating supply pressures. Second, enhance model adaptability and interpretability. Develop lightweight, multi-scenario adaptable model architectures. Strengthen model generalization capabilities through multi-center, multi-population data training. Simultaneously, incorporate explainable AI (XAI) technologies to translate model decision logic into clinically interpretable metrics for healthcare professionals, thereby boosting clinical trust. Third, refine flexible regulatory approval mechanisms. Regulatory bodies should establish a “dynamic approval pathway for AI medical devices,” adopting a “filing system” rather than “re-approval” for algorithm iterations to shorten development cycles. Concurrently, collaborate with industry stakeholders to develop clinical validation guidelines for AI devices [35], clarifying verification metrics and processes for different AI device types. Finally, deepen the integration of technology and clinical practice. Promote tripartite collaboration among medical institutions, research organizations, and enterprises, guiding R&D toward actual clinical needs to ensure precise alignment between technological innovation and clinical application requirements, thereby truly achieving the core objective of “technology serving clinical practice”.
This section interprets the key findings of deep learning (DL), machine learning (ML), and medical big data analytics (MBDAT) in medical device R&D—focusing on practical value, industry relevance, and limitations—without repeating technical details. The findings solve long-standing gaps in traditional device development via AI. DL-enabled medical imaging devices reach 94% overall diagnostic accuracy, with 95% for diabetic retinopathy screening. This cuts reliance on senior physicians, reduces subjective errors, and enables high-precision screening in primary care with scarce specialists. ML drives patient-centric care: AI imaging diagnostics score 8.5 in user satisfaction, and personalized AI treatment plans boost improvement by 60%, moving beyond passive wearable data collection and one-size-fits-all protocols. MBDAT integrates electronic health records, genomic data, and multi-center trials to fix traditional drug monitoring bias and small-sample limits, while supporting DL/ML models. These align with industry trends. The AI medical device market is set to grow from $2.229 billion (2024) to over $80 billion (2034), driven by the clinical value here. The study’s focus on DL-based imaging, ML-based personalization, and MBDAT-based integration matches industry priorities. It also highlights common challenges: DL needs high-quality tertiary hospital data (exposing data silos), and personalized ML works poorly in low-resource primary care (showing weak generalization). Three limitations remain. Performance data mostly comes from controlled environments (single-center datasets, standard equipment), failing to reflect real-world variability. The three AI technologies are interdependent—bottlenecks like MBDAT’s inability to break data silos limit others. Regulatory frameworks lag AI’s iteration, delaying clinical translation. Solutions require collaborative action: build standardized, anonymized data platforms; develop lightweight, multi-scenario AI models; and establish dynamic regulatory pathways for algorithm updates. In summary, DL, ML, and MBDAT transform medical device R&D. Their full potential depends on resolving industry-wide data, model adaptability, and regulation issues. Future research should integrate these technologies with clinical practice to meet frontline needs.
Breakthroughs in artificial intelligence (AI) technologies—particularly deep learning, machine learning, and medical big data analytics—are profoundly reshaping the R&D logic and application value of clinical medical devices. Based on survey findings, AI has empowered medical devices across three core dimensions: diagnostic precision, user value, and clinical efficacy, forming a complete value chain from technological implementation to user recognition.
In terms of diagnostic precision, the accuracy of medical imaging diagnosis has increased to 85%-94%. Specifically, for diabetic retinopathy screening, driven by deep learning algorithms and annotated data, diagnostic accuracy has surged from 79% to 95%. This advancement stems from the synergy between model optimization and data augmentation strategies, which significantly enhances devices’ ability to recognize image details, facilitating early patient screening and intervention. From the user's perspective, AI-powered medical imaging diagnostics scored 8.5 in satisfaction, demonstrating high clinical recognition of the convenience and accuracy provided by deep learning, which is further accelerating the adoption of AI devices. In terms of clinical efficacy, personalized AI treatment plans have achieved a 60% improvement rate, underscoring the value of integrating AI closely with clinical practice. Although the medical device industry faces challenges such as limited high-quality data, inadequate cross-device coordination, and restricted model generalization, AI is expected to drive devices towards greater precision, usability, and effectiveness as data standardization systems improve, multi-technology collaboration strengthens, and clinical needs are better integrated. In the future, deploying lightweight intelligent devices in primary care settings can address resource shortages at the grassroots level, while personalized diagnosis and treatment models will support the implementation of precision medicine. Driven by policy support, technological innovation, and clinical demand, AI will continue to inject vitality into the R&D of clinical medical devices, providing critical support for the advancement of global healthcare.
The authors declare no competing financial interests.
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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.
