JMIR AI最新文献

{"title":"Momentary Depressive Feeling Detection using X (formerly Twitter) data: A Contextual Language Approach (Preprint)","authors":"Ali Akbar Jamali, Corinne Berger, Raymond J Spiteri","doi":"10.2196/49531","DOIUrl":"https://doi.org/10.2196/49531","url":null,"abstract":"","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135477883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the Hype: The Actual Role and Risks of AI in Today's Medical Practice (Preprint)","authors":"Steffan Hansen, Carl Joakim Brandt, Jens Søndergaard","doi":"10.2196/49082","DOIUrl":"https://doi.org/10.2196/49082","url":null,"abstract":"","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135960078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reporting and Methodological Observations on Prognostic and Diagnostic Machine Learning Studies","authors":"K. El Emam, W. Klement, Bradley Malin","doi":"10.2196/47995","DOIUrl":"https://doi.org/10.2196/47995","url":null,"abstract":"Common reporting and methodological patterns were observed from the peer reviews of prognostic and diagnostic machine learning modeling studies submitted to JMIR AI. In this editorial, we summarized some key observations to inform future studies and their reporting.","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78487362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BGEM™: Assessing Elevated Blood Glucose Levels Using Machine Learning and Wearable Photoplethysmography Sensors (Preprint)","authors":"Bohan Shi, Satvinder Singh Dhaliwal, Marcus Soo, Cheri Chan, Jocelin Wong, Natalie W.C. Lam, Entong Zhou, Vivien Paitimusa, Kum Yin Loke, Joel Chin, Mei Tuan Chua, Kathy Chiew Suan Liaw, Amos WH Lim, Fadil Fatin Insyirah, Shih-Cheng Yen, Arthur Tay, Seng Bin Ang","doi":"10.2196/48340","DOIUrl":"https://doi.org/10.2196/48340","url":null,"abstract":"","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135414997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Early Health Care Intervention by Detecting Depression in Users of Web-Based Forums using Language Models: Longitudinal Analysis and Evaluation.","authors":"David Owen,&nbsp;Dimosthenis Antypas,&nbsp;Athanasios Hassoulas,&nbsp;Antonio F Pardiñas,&nbsp;Luis Espinosa-Anke,&nbsp;Jose Camacho Collados","doi":"10.2196/41205","DOIUrl":"https://doi.org/10.2196/41205","url":null,"abstract":"<p><strong>Background: </strong>Major depressive disorder is a common mental disorder affecting 5% of adults worldwide. Early contact with health care services is critical for achieving accurate diagnosis and improving patient outcomes. Key symptoms of major depressive disorder (depression hereafter) such as cognitive distortions are observed in verbal communication, which can also manifest in the structure of written language. Thus, the automatic analysis of text outputs may provide opportunities for early intervention in settings where written communication is rich and regular, such as social media and web-based forums.</p><p><strong>Objective: </strong>The objective of this study was 2-fold. We sought to gauge the effectiveness of different machine learning approaches to identify users of the mass web-based forum Reddit, who eventually disclose a diagnosis of depression. We then aimed to determine whether the time between a forum post and a depression diagnosis date was a relevant factor in performing this detection.</p><p><strong>Methods: </strong>A total of 2 Reddit data sets containing posts belonging to users with and without a history of depression diagnosis were obtained. The intersection of these data sets provided users with an estimated date of depression diagnosis. This derived data set was used as an input for several machine learning classifiers, including transformer-based language models (LMs).</p><p><strong>Results: </strong>Bidirectional Encoder Representations from Transformers (BERT) and MentalBERT transformer-based LMs proved the most effective in distinguishing forum users with a known depression diagnosis from those without. They each obtained a mean <i>F</i>₁-score of 0.64 across the experimental setups used for binary classification. The results also suggested that the final 12 to 16 weeks (about 3-4 months) of posts before a depressed user's estimated diagnosis date are the most indicative of their illness, with data before that period not helping the models detect more accurately. Furthermore, in the 4- to 8-week period before the user's estimated diagnosis date, their posts exhibited more negative sentiment than any other 4-week period in their post history.</p><p><strong>Conclusions: </strong>Transformer-based LMs may be used on data from web-based social media forums to identify users at risk for psychiatric conditions such as depression. Language features picked up by these classifiers might predate depression onset by weeks to months, enabling proactive mental health care interventions to support those at risk for this condition.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"2 ","pages":"e41205"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9974178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting Artificial Intelligence Trends in Healthcare: An International Patent Analysis (Preprint)","authors":"B. Meskó, S. Benjamens, Pranavsingh Dhunnoo, Márton Görög","doi":"10.2196/47283","DOIUrl":"https://doi.org/10.2196/47283","url":null,"abstract":"","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73175126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Adherence to Behavior Change Support Systems using Machine Learning: A Systematic Review (Preprint)","authors":"Akon Obu Ekpezu, Isaac Wiafe, Harri Oinas-Kukkonen","doi":"10.2196/46779","DOIUrl":"https://doi.org/10.2196/46779","url":null,"abstract":"","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Application of Artificial Intelligence in Health Care Resource Allocation Before and During the COVID-19 Pandemic: Scoping Review","authors":"Hao Wu, Xiao-Lin Lu, H. Wang","doi":"10.2196/38397","DOIUrl":"https://doi.org/10.2196/38397","url":null,"abstract":"\u0000 \u0000 Imbalanced health care resource distribution has been central to unequal health outcomes and political tension around the world. Artificial intelligence (AI) has emerged as a promising tool for facilitating resource distribution, especially during emergencies. However, no comprehensive review exists on the use and ethics of AI in health care resource distribution.\u0000 \u0000 \u0000 \u0000 This study aims to conduct a scoping review of the application of AI in health care resource distribution, and explore the ethical and political issues in such situations.\u0000 \u0000 \u0000 \u0000 A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). A comprehensive search of relevant literature was conducted in MEDLINE (Ovid), PubMed, Web of Science, and Embase from inception to February 2022. The review included qualitative and quantitative studies investigating the application of AI in health care resource allocation.\u0000 \u0000 \u0000 \u0000 The review involved 22 articles, including 9 on model development and 13 on theoretical discussions, qualitative studies, or review studies. Of the 9 on model development and validation, 5 were conducted in emerging economies, 3 in developed countries, and 1 in a global context. In terms of content, 4 focused on resource distribution at the health system level and 5 focused on resource allocation at the hospital level. Of the 13 qualitative studies, 8 were discussions on the COVID-19 pandemic and the rest were on hospital resources, outbreaks, screening, human resources, and digitalization.\u0000 \u0000 \u0000 \u0000 This scoping review synthesized evidence on AI in health resource distribution, focusing on the COVID-19 pandemic. The results suggest that the application of AI has the potential to improve efficacy in resource distribution, especially during emergencies. Efficient data sharing and collecting structures are needed to make reliable and evidence-based decisions. Health inequality, distributive justice, and transparency must be considered when deploying AI models in real-world situations.\u0000","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82684197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using conversational AI to facilitate mental health assessments and improve clinical efficiencies within psychotherapy services in a large real-world dataset (Preprint)","authors":"Max Rollwage, Johanna Habicht, Keno Juchems, Ben Carrington, Mona Stylianou, Tobias Hauser, Ross Harper","doi":"10.2196/44358","DOIUrl":"https://doi.org/10.2196/44358","url":null,"abstract":"","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of a Comprehensive Evaluation Framework to COVID-19 Studies: Systematic Review of Translational Aspects of Artificial Intelligence in Health Care.","authors":"Aaron Edward Casey,&nbsp;Saba Ansari,&nbsp;Bahareh Nakisa,&nbsp;Blair Kelly,&nbsp;Pieta Brown,&nbsp;Paul Cooper,&nbsp;Imran Muhammad,&nbsp;Steven Livingstone,&nbsp;Sandeep Reddy,&nbsp;Ville-Petteri Makinen","doi":"10.2196/42313","DOIUrl":"https://doi.org/10.2196/42313","url":null,"abstract":"<p><strong>Background: </strong>Despite immense progress in artificial intelligence (AI) models, there has been limited deployment in health care environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended.</p><p><strong>Objective: </strong>We previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to health care environments. In this study, we applied the TEHAI framework to the COVID-19 literature in order to assess how well translational topics are covered.</p><p><strong>Methods: </strong>A systematic literature search for COVID-19 AI studies published between December 2019 and December 2020 resulted in 3830 records. A subset of 102 (2.7%) papers that passed the inclusion criteria was sampled for full review. The papers were assessed for translational value and descriptive data collected by 9 reviewers (each study was assessed by 2 reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform.</p><p><strong>Results: </strong>We observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, nonmaleficence, and service adoption received failed scores in most studies.</p><p><strong>Conclusions: </strong>Using TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 health care environments.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"2 ","pages":"e42313"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9823667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}