Pub Date : 2026-03-21DOI: 10.1007/s10916-026-02363-8
Gianmarco Sirago, Francesco Calò, Annachiara Vinci, Paolo Visci, Biagio Solarino, Alessandro Dell'Erba, Davide Ferorelli
{"title":"Clinical Use of Non-Certified Generative AI in Healthcare: Governing the Regulatory Grey Zone from Convenience to Legal Accountability.","authors":"Gianmarco Sirago, Francesco Calò, Annachiara Vinci, Paolo Visci, Biagio Solarino, Alessandro Dell'Erba, Davide Ferorelli","doi":"10.1007/s10916-026-02363-8","DOIUrl":"https://doi.org/10.1007/s10916-026-02363-8","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147491190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-18DOI: 10.1007/s10916-026-02364-7
Shinji Kobayashi
{"title":"Protecting Peer Review from the Surge of Low Fidelity Systematic Reviews in the Generative AI Era.","authors":"Shinji Kobayashi","doi":"10.1007/s10916-026-02364-7","DOIUrl":"https://doi.org/10.1007/s10916-026-02364-7","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147480893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-18DOI: 10.1007/s10916-026-02360-x
Semerjit Bains, Luis Ahumada, Fiorella Gonzales, Frederick Kuo, Ryan Shargo, Brant Tudor, Mohamed Rehman, Nicholas M Dalesio
{"title":"Tracking Greenhouse Gas Emission Initiatives Across a Large Academic Health System Utilizing Innovative Dashboards.","authors":"Semerjit Bains, Luis Ahumada, Fiorella Gonzales, Frederick Kuo, Ryan Shargo, Brant Tudor, Mohamed Rehman, Nicholas M Dalesio","doi":"10.1007/s10916-026-02360-x","DOIUrl":"https://doi.org/10.1007/s10916-026-02360-x","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147480952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-17DOI: 10.1007/s10916-026-02358-5
Mikko Nuutinen, Anna-Maria Hiltunen, Riikka-Leena Leskelä, Maikki Messo, Anna Salminen, Mari Lahelma, Johanna de Almeida Mello, Anja Declercq, Olena Švihnosová, Kateřina Langmaierová, Daniela Fialová, Federica Mammarella, Rosa Liperoti, Collin Exmann, Hein van Hout, Vanja Pešić, Elizabeth Howard, Agata Stodolska, Katarzyna Szczerbińska, Mor Alon, Ira Haavisto
{"title":"The Impact of Healthcare Professionals' Characteristics on the Evaluation of Clinical Decision Support Systems: Insights from a Cross-Country Usability and Technology Acceptance Study of the iCARE Tool.","authors":"Mikko Nuutinen, Anna-Maria Hiltunen, Riikka-Leena Leskelä, Maikki Messo, Anna Salminen, Mari Lahelma, Johanna de Almeida Mello, Anja Declercq, Olena Švihnosová, Kateřina Langmaierová, Daniela Fialová, Federica Mammarella, Rosa Liperoti, Collin Exmann, Hein van Hout, Vanja Pešić, Elizabeth Howard, Agata Stodolska, Katarzyna Szczerbińska, Mor Alon, Ira Haavisto","doi":"10.1007/s10916-026-02358-5","DOIUrl":"10.1007/s10916-026-02358-5","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12996014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147473407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-13DOI: 10.1007/s10916-026-02361-w
Anna G Quinlan, Mitchell H Tsai, Joshua M Zimmerman
{"title":"Emerging Utility of Multimodal Large Language Models in Cardiovascular Diagnostics.","authors":"Anna G Quinlan, Mitchell H Tsai, Joshua M Zimmerman","doi":"10.1007/s10916-026-02361-w","DOIUrl":"https://doi.org/10.1007/s10916-026-02361-w","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-11DOI: 10.1007/s10916-026-02348-7
Eui Min Jeong, Hwan Kim, Saebom Jeon, Jae Kyoung Kim, Seockhoon Chung
{"title":"The Dysfunctional Self-Focus Attributes Scale-7 (DSAS-7): A Machine Learning-based Development of a Shortened Version of the DSAS.","authors":"Eui Min Jeong, Hwan Kim, Saebom Jeon, Jae Kyoung Kim, Seockhoon Chung","doi":"10.1007/s10916-026-02348-7","DOIUrl":"https://doi.org/10.1007/s10916-026-02348-7","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147433800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1007/s10916-026-02355-8
Ying Zhao, Xincheng Shu, Chi-Sing Leung, Eric W M Wong, Qi Xuan, Kar-Lung Lee, Anne Leung, Lowell Ling, Hoi-Ping Shum, Wing-Lun Wan, Pauline Yeung Ng, Tsz-Kin Yim, Wai-Ming Tang, Kenny King-Chung Chan, Gavin Joynt
{"title":"Machine Learning-Driven Prediction of Intensive Care Units Mortality and Length of Stay: A 11-Year Retrospective Study in Hong Kong Public Hospitals.","authors":"Ying Zhao, Xincheng Shu, Chi-Sing Leung, Eric W M Wong, Qi Xuan, Kar-Lung Lee, Anne Leung, Lowell Ling, Hoi-Ping Shum, Wing-Lun Wan, Pauline Yeung Ng, Tsz-Kin Yim, Wai-Ming Tang, Kenny King-Chung Chan, Gavin Joynt","doi":"10.1007/s10916-026-02355-8","DOIUrl":"10.1007/s10916-026-02355-8","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12975804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147433803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-09DOI: 10.1007/s10916-026-02359-4
Mohammad Moharrami, Erin Watson, Shao Hui Huang, Sreenath Madathil, John Kim, Andrew McPartlin, Nauman H Malik, Sonica Singhal, Ezra Hahn, John Waldron, Scott Bratman, John de Almeida, Christopher Yao, Andrew Hope, Carlos Quinonez, Michael Glogauer, Ali Hosni
To develop and validate predictive models for osteoradionecrosis (ORN) after head and neck radiation therapy (RT) using time-to-event data with death as the competing risk, and to quantify the degree of risk overestimation when the competing risk is ignored. In this prognostic study of patients who underwent curative RT between 2011 and 2018, with ongoing follow-up, sociodemographic, clinical, and dosimetric data were collected. The binary ORN outcome was defined by the ClinRad system (grade ≥ 1); all-cause mortality was the competing event. Fine-Gray regression (FGR), Random Survival Forests (RSF) with Gray's test splitting rule, and DeepHit were implemented using repeated nested stratified cross-validation. Feature selection and interpretation were guided by SHapley Additive exPlanations (SHAP). For comparison, non-competing risk models such as Cox proportional hazards (Cox PH) and standard RSF (S-RSF) with log-rank splitting rule were also trained. Of 2,466 patients, 183 developed ORN during follow-up, and 714 died. Three versions of each model were developed using 20, 10, and 5 features. The 10- and 5-feature RSF models performed best. Considering simplicity, the 5-feature model, which included tumor site, D10cc, smoking pack-years, periodontal condition, and dental insurance, was selected for production. At 60 months, Brier Score was 0.061 (95% CI: 0.060-0.063), Integrated Brier Score 0.038 (95% CI: 0.037-0.040), time-dependent AUC 0.776 (95% CI: 0.762-0.789), and C-index 0.772 (95% CI: 0.757-0.787). FGR closely followed, whereas DeepHit underperformed. Non-competing models, including the S-RSF, overestimated ORN risk, predicting an average 60-month cumulative incidence of 8.7% versus 6.8% with the 5-feature RSF. A parsimonious RSF model reliably estimated individualized ORN risk while avoiding overestimation from ignored competing risks. An interactive web application was developed to support clinical implementation.
{"title":"Machine Learning Models for Individualized Osteoradionecrosis Risk Prediction in Head and Neck Cancer.","authors":"Mohammad Moharrami, Erin Watson, Shao Hui Huang, Sreenath Madathil, John Kim, Andrew McPartlin, Nauman H Malik, Sonica Singhal, Ezra Hahn, John Waldron, Scott Bratman, John de Almeida, Christopher Yao, Andrew Hope, Carlos Quinonez, Michael Glogauer, Ali Hosni","doi":"10.1007/s10916-026-02359-4","DOIUrl":"https://doi.org/10.1007/s10916-026-02359-4","url":null,"abstract":"<p><p>To develop and validate predictive models for osteoradionecrosis (ORN) after head and neck radiation therapy (RT) using time-to-event data with death as the competing risk, and to quantify the degree of risk overestimation when the competing risk is ignored. In this prognostic study of patients who underwent curative RT between 2011 and 2018, with ongoing follow-up, sociodemographic, clinical, and dosimetric data were collected. The binary ORN outcome was defined by the ClinRad system (grade ≥ 1); all-cause mortality was the competing event. Fine-Gray regression (FGR), Random Survival Forests (RSF) with Gray's test splitting rule, and DeepHit were implemented using repeated nested stratified cross-validation. Feature selection and interpretation were guided by SHapley Additive exPlanations (SHAP). For comparison, non-competing risk models such as Cox proportional hazards (Cox PH) and standard RSF (S-RSF) with log-rank splitting rule were also trained. Of 2,466 patients, 183 developed ORN during follow-up, and 714 died. Three versions of each model were developed using 20, 10, and 5 features. The 10- and 5-feature RSF models performed best. Considering simplicity, the 5-feature model, which included tumor site, D10cc, smoking pack-years, periodontal condition, and dental insurance, was selected for production. At 60 months, Brier Score was 0.061 (95% CI: 0.060-0.063), Integrated Brier Score 0.038 (95% CI: 0.037-0.040), time-dependent AUC 0.776 (95% CI: 0.762-0.789), and C-index 0.772 (95% CI: 0.757-0.787). FGR closely followed, whereas DeepHit underperformed. Non-competing models, including the S-RSF, overestimated ORN risk, predicting an average 60-month cumulative incidence of 8.7% versus 6.8% with the 5-feature RSF. A parsimonious RSF model reliably estimated individualized ORN risk while avoiding overestimation from ignored competing risks. An interactive web application was developed to support clinical implementation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147390258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-07DOI: 10.1007/s10916-026-02336-x
Zhihao Lei
{"title":"\"Perfection or Overfitting?\" Reassessing the Validity of Deep Learning Models for Respiratory Sound Classification.","authors":"Zhihao Lei","doi":"10.1007/s10916-026-02336-x","DOIUrl":"https://doi.org/10.1007/s10916-026-02336-x","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147369651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1007/s10916-026-02357-6
Saygun Guler, Seyed Sajjad Mirbakht, Melih Can Tasdelen, Burcu Arman Kuzubasoglu, Faruk Ballipinar, Murat Kaya Yapici
Drop-on-demand inkjet printing has shown great potential for wearable health monitoring applications because of its ability to directly pattern on flexible substrates that can conform to curved surfaces such as the skin. Surface biopotential measurements such as electrocardiography is one such example requiring conductive electrodes that can be attached to skin to record the electrical activity of the heart, otherwise known as an electrocardiogram (ECG). Typical pre-gelled, silver/silver chloride (Ag/AgCl) electrodes; also known as "wet electrodes", are known to cause skin irritations with performance degradation over time, and therefore remain largely non-ideal especially in long-term, mobile heath monitoring scenarios. This paper reports, for the first time, the development of a single, fully inkjet-printed graphene-on-plastic monolithic wearable armband, whose performance was benchmarked against commercial Ag/AgCl electrodes during a 1-hour-long ECG recording with five participants. The inkjet-printed graphene-on-plastic armband displayed excellent ECG reception with a signal-to-noise ratio (SNR) of up to 4.2 dB higher than that of commercial electrodes.
{"title":"Inkjet-Printed Graphene Electrodes on a Plastic Armband for Mobile Electrocardiography.","authors":"Saygun Guler, Seyed Sajjad Mirbakht, Melih Can Tasdelen, Burcu Arman Kuzubasoglu, Faruk Ballipinar, Murat Kaya Yapici","doi":"10.1007/s10916-026-02357-6","DOIUrl":"10.1007/s10916-026-02357-6","url":null,"abstract":"<p><p>Drop-on-demand inkjet printing has shown great potential for wearable health monitoring applications because of its ability to directly pattern on flexible substrates that can conform to curved surfaces such as the skin. Surface biopotential measurements such as electrocardiography is one such example requiring conductive electrodes that can be attached to skin to record the electrical activity of the heart, otherwise known as an electrocardiogram (ECG). Typical pre-gelled, silver/silver chloride (Ag/AgCl) electrodes; also known as \"wet electrodes\", are known to cause skin irritations with performance degradation over time, and therefore remain largely non-ideal especially in long-term, mobile heath monitoring scenarios. This paper reports, for the first time, the development of a single, fully inkjet-printed graphene-on-plastic monolithic wearable armband, whose performance was benchmarked against commercial Ag/AgCl electrodes during a 1-hour-long ECG recording with five participants. The inkjet-printed graphene-on-plastic armband displayed excellent ECG reception with a signal-to-noise ratio (SNR) of up to 4.2 dB higher than that of commercial electrodes.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12960339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147355323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号