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Human AI collaboration for unsupervised categorization of live surgical feedback
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-20 DOI: 10.1038/s41746-024-01383-3
Rafal Kocielnik, Cherine H. Yang, Runzhuo Ma, Steven Y. Cen, Elyssa Y. Wong, Timothy N. Chu, J. Everett Knudsen, Peter Wager, John Heard, Umar Ghaffar, Anima Anandkumar, Andrew J. Hung
{"title":"Human AI collaboration for unsupervised categorization of live surgical feedback","authors":"Rafal Kocielnik, Cherine H. Yang, Runzhuo Ma, Steven Y. Cen, Elyssa Y. Wong, Timothy N. Chu, J. Everett Knudsen, Peter Wager, John Heard, Umar Ghaffar, Anima Anandkumar, Andrew J. Hung","doi":"10.1038/s41746-024-01383-3","DOIUrl":"https://doi.org/10.1038/s41746-024-01383-3","url":null,"abstract":"<p>Formative verbal feedback during live surgery is essential for adjusting trainee behavior and accelerating skill acquisition. Despite its importance, understanding optimal feedback is challenging due to the difficulty of capturing and categorizing feedback at scale. We propose a Human-AI Collaborative Refinement Process that uses unsupervised machine learning (Topic Modeling) with human refinement to discover feedback categories from surgical transcripts. Our discovered categories are rated highly for clinical clarity and are relevant to practice, including topics like <i>“Handling and Positioning of (tissue)”</i> and <i>“(Tissue) Layer Depth Assessment and Correction [during tissue dissection].”</i> These AI-generated topics significantly enhance predictions of trainee behavioral change, providing insights beyond traditional manual categorization. For example, feedback on <i>“Handling Bleeding”</i> is linked to improved behavioral change. This work demonstrates the potential of AI to analyze surgical feedback at scale, informing better training guidelines and paving the way for automated feedback and cueing systems in surgery.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"72 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Probabilistic medical predictions of large language models
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-19 DOI: 10.1038/s41746-024-01366-4
Bowen Gu, Rishi J. Desai, Kueiyu Joshua Lin, Jie Yang
{"title":"Probabilistic medical predictions of large language models","authors":"Bowen Gu, Rishi J. Desai, Kueiyu Joshua Lin, Jie Yang","doi":"10.1038/s41746-024-01366-4","DOIUrl":"https://doi.org/10.1038/s41746-024-01366-4","url":null,"abstract":"<p>Large Language Models (LLMs) have shown promise in clinical applications through prompt engineering, allowing flexible clinical predictions. However, they struggle to produce reliable prediction probabilities, which are crucial for transparency and decision-making. While explicit prompts can lead LLMs to generate probability estimates, their numerical reasoning limitations raise concerns about reliability. We compared explicit probabilities from text generation to implicit probabilities derived from the likelihood of predicting the correct label token. Across six advanced open-source LLMs and five medical datasets, explicit probabilities consistently underperformed implicit probabilities in discrimination, precision, and recall. This discrepancy is more pronounced with smaller LLMs and imbalanced datasets, highlighting the need for cautious interpretation, improved probability estimation methods, and further research for clinical use of LLMs.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"11 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-19 DOI: 10.1038/s41746-024-01389-x
Michael D. Abràmoff, Philip T. Lavin, Julie R. Jakubowski, Barbara A. Blodi, Mia Keeys, Cara Joyce, James C. Folk
{"title":"Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease","authors":"Michael D. Abràmoff, Philip T. Lavin, Julie R. Jakubowski, Barbara A. Blodi, Mia Keeys, Cara Joyce, James C. Folk","doi":"10.1038/s41746-024-01389-x","DOIUrl":"https://doi.org/10.1038/s41746-024-01389-x","url":null,"abstract":"<p>Where adopted, Autonomous artificial Intelligence (AI) for Diabetic Retinal Disease (DRD) resolves longstanding racial, ethnic, and socioeconomic disparities, but AI adoption bias persists. This preregistered trial determined sensitivity and specificity of a previously FDA authorized AI, improved to compensate for lower contrast and smaller imaged area of a widely adopted, lower cost, handheld fundus camera (RetinaVue700, Baxter Healthcare, Deerfield, IL) to identify DRD in participants with diabetes without known DRD, in primary care. In 626 participants (1252 eyes) 50.8% male, 45.7% Hispanic, 17.3% Black, DRD prevalence was 29.0%, all prespecified non-inferiority endpoints were met and no racial, ethnic or sex bias was identified, against a Wisconsin Reading Center level I prognostic standard using widefield stereoscopic photography and macular Optical Coherence Tomography. Results suggest this improved autonomous AI system can mitigate AI adoption bias, while preserving safety and efficacy, potentially contributing to rapid scaling of health access equity. ClinicalTrials.gov NCT05808699 (3/29/2023).</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"58 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimising the paradigms of human AI collaborative clinical coding
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-19 DOI: 10.1038/s41746-024-01363-7
Yue Gao, Yuepeng Chen, Minghao Wang, Jinge Wu, Yunsoo Kim, Kaiyin Zhou, Miao Li, Xien Liu, Xiangling Fu, Ji Wu, Honghan Wu
{"title":"Optimising the paradigms of human AI collaborative clinical coding","authors":"Yue Gao, Yuepeng Chen, Minghao Wang, Jinge Wu, Yunsoo Kim, Kaiyin Zhou, Miao Li, Xien Liu, Xiangling Fu, Ji Wu, Honghan Wu","doi":"10.1038/s41746-024-01363-7","DOIUrl":"https://doi.org/10.1038/s41746-024-01363-7","url":null,"abstract":"<p>Automated clinical coding (ACC) has emerged as a promising alternative to manual coding. This study proposes a novel human-in-the-loop (HITL) framework, CliniCoCo. Using deep learning capacities, CliniCoCo focuses on how such ACC systems and human coders can work effectively and efficiently together in real-world settings. Specifically, it implements a series of collaborative strategies at annotation, training and user interaction stages. Extensive experiments are conducted using real-world EMR datasets from Chinese hospitals. With automatically optimised annotation workloads, the model can achieve F1 scores around 0.80–0.84. For an EMR with 30% mistaken codes, CliniCoCo can suggest halving the annotations from 3000 admissions with an ignorable 0.01 F1 decrease. In human evaluations, compared to manual coding, CliniCoCo reduces coding time by 40% on average and significantly improves the correction rates on EMR mistakes (e.g., three times better on missing codes). Senior professional coders’ performances can be boosted to more than 0.93 F1 score from 0.72.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"26 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AI technology to support adaptive functioning in neurodevelopmental conditions in everyday environments: a systematic review
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-19 DOI: 10.1038/s41746-024-01355-7
Nina Perry, Carter Sun, Martha Munro, Kelsie A. Boulton, Adam J. Guastella
{"title":"AI technology to support adaptive functioning in neurodevelopmental conditions in everyday environments: a systematic review","authors":"Nina Perry, Carter Sun, Martha Munro, Kelsie A. Boulton, Adam J. Guastella","doi":"10.1038/s41746-024-01355-7","DOIUrl":"https://doi.org/10.1038/s41746-024-01355-7","url":null,"abstract":"<p>Supports for adaptive functioning in individuals with neurodevelopmental conditions (NDCs) is of umost importance to long-term outcomes. Artificial intelligence (AI)-assistive technologies has enormous potential to offer efficient, cost-effective, and personalized solutions to address these challenges, particularly in everday environments. This systematic review examines the existing evidence for using AI-assistive technologies to support adaptive functioning in people with NDCs in everyday settings. Searches across six databases yielded 15 studies meeting inclusion criteria, focusing on robotics, phones/computers and virtual reality. Studies most frequently recruited children diagnosed with autism and targeted social skills (47%), daily living skills (26%), and communication (16%). Despite promising results, studies addressing broader transdiagnostic needs across different NDC populations are needed. There is also an urgent need to improve the quality of evidence-based research practices. This review concludes that AI holds enormous potential to support adaptive functioning for people with NDCs and for personalized health support. This review underscores the need for further research studies to advance AI technologies in this field.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"31 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A prospective comparison of two computer aided detection systems with different false positive rates in colonoscopy
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-19 DOI: 10.1038/s41746-024-01334-y
Goh Eun Chung, Jooyoung Lee, Seon Hee Lim, Hae Yeon Kang, Jung Kim, Ji Hyun Song, Sun Young Yang, Ji Min Choi, Ji Yeon Seo, Jung Ho Bae
{"title":"A prospective comparison of two computer aided detection systems with different false positive rates in colonoscopy","authors":"Goh Eun Chung, Jooyoung Lee, Seon Hee Lim, Hae Yeon Kang, Jung Kim, Ji Hyun Song, Sun Young Yang, Ji Min Choi, Ji Yeon Seo, Jung Ho Bae","doi":"10.1038/s41746-024-01334-y","DOIUrl":"https://doi.org/10.1038/s41746-024-01334-y","url":null,"abstract":"<p>This study evaluated the impact of differing false positive (FP) rates in two computer-aided detection (CADe) systems on the clinical effectiveness of artificial intelligence (AI)-assisted colonoscopy. The primary outcomes were adenoma detection rate (ADR) and adenomas per colonoscopy (APC). The ADR in the control, system A (3.2% FP rate), and system B (0.6% FP rate) groups were 44.3%, 43.4%, and 50.4%, respectively, with system B showing a significantly higher ADR than the control group. The APC for the control, A, and B groups were 0.75, 0.83, and 0.90, respectively, with system B also showing a higher APC than the control. The non-true lesion resection rates were 23.8%, 29.2%, and 21.3%, with system B having the lowest. The system with lower FP rates demonstrated improved ADR and APC without increasing the resection of non-neoplastic lesions. These findings suggest that higher FP rates negatively affect the clinical performance of AI-assisted colonoscopy.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"38 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hospitalization prediction from the emergency department using computer vision AI with short patient video clips
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-19 DOI: 10.1038/s41746-024-01375-3
Wui Ip, Maria Xenochristou, Elaine Sui, Elyse Ruan, Ryan Ribeira, Debadutta Dash, Malathi Srinivasan, Maja Artandi, Jesutofunmi A. Omiye, Nicholas Scoulios, Hayden L. Hofmann, Ali Mottaghi, Zhenzhen Weng, Abhinav Kumar, Ananya Ganesh, Jason Fries, Serena Yeung-Levy, Lawrence V. Hofmann
{"title":"Hospitalization prediction from the emergency department using computer vision AI with short patient video clips","authors":"Wui Ip, Maria Xenochristou, Elaine Sui, Elyse Ruan, Ryan Ribeira, Debadutta Dash, Malathi Srinivasan, Maja Artandi, Jesutofunmi A. Omiye, Nicholas Scoulios, Hayden L. Hofmann, Ali Mottaghi, Zhenzhen Weng, Abhinav Kumar, Ananya Ganesh, Jason Fries, Serena Yeung-Levy, Lawrence V. Hofmann","doi":"10.1038/s41746-024-01375-3","DOIUrl":"https://doi.org/10.1038/s41746-024-01375-3","url":null,"abstract":"<p>In this study, we investigate the performance of computer vision AI algorithms in predicting patient disposition from the emergency department (ED) using short video clips. Clinicians often use “eye-balling” or clinical gestalt to aid in triage, based on brief observations. We hypothesize that AI can similarly use patient appearance for disposition prediction. Data were collected from adult patients at an academic ED, with mobile phone videos capturing patients performing simple tasks. Our AI algorithm, using video alone, showed better performance in predicting hospital admissions (AUROC = 0.693 [95% CI 0.689, 0.696]) compared to models using triage clinical data (AUROC = 0.678 [95% CI 0.668, 0.687]). Combining video and triage data achieved the highest predictive performance (AUROC = 0.714 [95% CI 0.709, 0.719]). This study demonstrates the potential of video AI algorithms to support ED triage and alleviate healthcare capacity strains during periods of high demand.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"100 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The emergence of medical futures studies uncovers medicine and healthcare’s untapped potential
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-19 DOI: 10.1038/s41746-024-01365-5
Bertalan Mesko
{"title":"The emergence of medical futures studies uncovers medicine and healthcare’s untapped potential","authors":"Bertalan Mesko","doi":"10.1038/s41746-024-01365-5","DOIUrl":"https://doi.org/10.1038/s41746-024-01365-5","url":null,"abstract":"<p>Analyzing the future of medicine and healthcare, especially during the rise of digital health and artificial intelligence, should rely on established futures methods that the discipline of futures studies has been using for decades. By employing such methods, healthcare professionals, policymakers and patient leaders could better navigate the complexities of modern healthcare, anticipate emerging challenges, and shape a future that is not just awaited but actively constructed.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"87 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Advancing digital sensing in mental health research
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-18 DOI: 10.1038/s41746-024-01343-x
Samir Akre, Darsol Seok, Christopher Douglas, Adrian Aguilera, Simona Carini, Jessilyn Dunn, Matthew Hotopf, David C. Mohr, Alex A. T. Bui, Nelson B. Freimer
{"title":"Advancing digital sensing in mental health research","authors":"Samir Akre, Darsol Seok, Christopher Douglas, Adrian Aguilera, Simona Carini, Jessilyn Dunn, Matthew Hotopf, David C. Mohr, Alex A. T. Bui, Nelson B. Freimer","doi":"10.1038/s41746-024-01343-x","DOIUrl":"https://doi.org/10.1038/s41746-024-01343-x","url":null,"abstract":"<p>Digital sensing tools, like smartphones and wearables, offer transformative potential for mental health research by enabling scalable, longitudinal data collection. Realizing this promise requires overcoming significant challenges including limited data standards, underpowered studies, and a disconnect between research aims and community needs. This report, based on the 2023 Workshop on Advancing Digital Sensing Tools for Mental Health, articulates strategies to address these challenges to ensure rigorous, equitable, and impactful research.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"114 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Systematic review and meta-analysis of adverse events in clinical trials of mental health apps
IF 15.2 1区 医学
NPJ Digital Medicine Pub Date : 2024-12-18 DOI: 10.1038/s41746-024-01388-y
Jake Linardon, Matthew Fuller-Tyszkiewicz, Joseph Firth, Simon B. Goldberg, Cleo Anderson, Zoe McClure, John Torous
{"title":"Systematic review and meta-analysis of adverse events in clinical trials of mental health apps","authors":"Jake Linardon, Matthew Fuller-Tyszkiewicz, Joseph Firth, Simon B. Goldberg, Cleo Anderson, Zoe McClure, John Torous","doi":"10.1038/s41746-024-01388-y","DOIUrl":"https://doi.org/10.1038/s41746-024-01388-y","url":null,"abstract":"<p>Mental health apps are efficacious, yet they may pose risks in some. This review (CRD42024506486) examined adverse events (AEs) from mental health apps. We searched (May 2024) the Medline, PsycINFO, Web of Science, and ProQuest databases to identify clinical trials of mental health apps. The risk of bias was assessed using the Cochrane Risk of Bias tool. Only 55 of 171 identified clinical trials reported AEs. AEs were more likely to be reported in trials sampling schizophrenia and delivering apps with symptom monitoring technology. The meta-analytic deterioration rate from 13 app conditions was 6.7% (95% CI = 4.3, 10.1, <i>I</i><sup>2</sup> = 75%). Deterioration rates did not differ between app and control groups (OR = 0.79, 95% CI = 0.62–1.01, <i>I</i><sup>2</sup> = 0%). Reporting of AEs was heterogeneous, in terms of assessments used, events recorded, and detail provided. Overall, few clinical trials of mental health apps report AEs. Those that do often provide insufficient information to properly judge risks related to app use.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"85 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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