{"title":"利用大型语言模型进行诊断和治疗的概述。","authors":"Matteo Malgaroli, Daniel McDuff","doi":"10.1002/jts.23082","DOIUrl":null,"url":null,"abstract":"<p>There is an acute need for solutions to treat stress and trauma–related sequelae, and there are well-documented shortages of qualified human professionals. Artificial intelligence (AI) presents an opportunity to create advanced screening, diagnosis, and treatment solutions that relieve the burden on people and can provide just-in-time interventions. Large language models (LLMs), in particular, are promising given the role language plays in understanding and treating traumatic stress and other mental health conditions. In this article, we provide an overview of the state-of-the-art LLMs applications in diagnostic assessments, clinical note generation, and therapeutic support. We discuss the open research direction and challenges that need to be overcome to realize the full potential of deploying language models for use in clinical contexts. We highlight the need for increased representation in AI systems to ensure there are no disparities in access. Public datasets and models will help lead progress toward better models; however, privacy-preserving model training will be necessary for protecting patient data.</p>","PeriodicalId":17519,"journal":{"name":"Journal of traumatic stress","volume":"37 5","pages":"754-760"},"PeriodicalIF":2.4000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An overview of diagnostics and therapeutics using large language models\",\"authors\":\"Matteo Malgaroli, Daniel McDuff\",\"doi\":\"10.1002/jts.23082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>There is an acute need for solutions to treat stress and trauma–related sequelae, and there are well-documented shortages of qualified human professionals. Artificial intelligence (AI) presents an opportunity to create advanced screening, diagnosis, and treatment solutions that relieve the burden on people and can provide just-in-time interventions. Large language models (LLMs), in particular, are promising given the role language plays in understanding and treating traumatic stress and other mental health conditions. In this article, we provide an overview of the state-of-the-art LLMs applications in diagnostic assessments, clinical note generation, and therapeutic support. We discuss the open research direction and challenges that need to be overcome to realize the full potential of deploying language models for use in clinical contexts. We highlight the need for increased representation in AI systems to ensure there are no disparities in access. Public datasets and models will help lead progress toward better models; however, privacy-preserving model training will be necessary for protecting patient data.</p>\",\"PeriodicalId\":17519,\"journal\":{\"name\":\"Journal of traumatic stress\",\"volume\":\"37 5\",\"pages\":\"754-760\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of traumatic stress\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jts.23082\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of traumatic stress","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jts.23082","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
An overview of diagnostics and therapeutics using large language models
There is an acute need for solutions to treat stress and trauma–related sequelae, and there are well-documented shortages of qualified human professionals. Artificial intelligence (AI) presents an opportunity to create advanced screening, diagnosis, and treatment solutions that relieve the burden on people and can provide just-in-time interventions. Large language models (LLMs), in particular, are promising given the role language plays in understanding and treating traumatic stress and other mental health conditions. In this article, we provide an overview of the state-of-the-art LLMs applications in diagnostic assessments, clinical note generation, and therapeutic support. We discuss the open research direction and challenges that need to be overcome to realize the full potential of deploying language models for use in clinical contexts. We highlight the need for increased representation in AI systems to ensure there are no disparities in access. Public datasets and models will help lead progress toward better models; however, privacy-preserving model training will be necessary for protecting patient data.
期刊介绍:
Journal of Traumatic Stress (JTS) is published for the International Society for Traumatic Stress Studies. Journal of Traumatic Stress , the official publication for the International Society for Traumatic Stress Studies, is an interdisciplinary forum for the publication of peer-reviewed original papers on biopsychosocial aspects of trauma. Papers focus on theoretical formulations, research, treatment, prevention education/training, and legal and policy concerns. Journal of Traumatic Stress serves as a primary reference for professionals who study and treat people exposed to highly stressful and traumatic events (directly or through their occupational roles), such as war, disaster, accident, violence or abuse (criminal or familial), hostage-taking, or life-threatening illness. The journal publishes original articles, brief reports, review papers, commentaries, and, from time to time, special issues devoted to a single topic.