Artificial Intelligence and Language Learning Models Can Be Improved By Curated Input of Medical Training Data But Still Face The Limitations of Available Literature And Require Continued Human Oversight.

IF 4.4 1区 医学 Q1 ORTHOPEDICS
Farah Selman, Kristine Obletz, Valeria Vismara, Robert Putko, Nicholas P J Perry
{"title":"Artificial Intelligence and Language Learning Models Can Be Improved By Curated Input of Medical Training Data But Still Face The Limitations of Available Literature And Require Continued Human Oversight.","authors":"Farah Selman, Kristine Obletz, Valeria Vismara, Robert Putko, Nicholas P J Perry","doi":"10.1016/j.arthro.2025.03.042","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial intelligence (AI) and Language Learning Models (LLM) are rapidly evolving. Several popular and easily accessible platforms, like ChatGPT and Gemini, are increasingly being explored by clinicians and patients for their utility in clinical decision-making. While these tools provide rapid access to information, their inconsistent adherence to evidence-based guidelines raises concerns. A potential solution is to generate more specialized LLM's for orthopaedics. A curated database of validated orthoapediic literature can be used as input, in order to address concerns about the quality of input data. However, a curated LLM may still have limitations of selection bias and limited high-quality literature. In additionally, patients using these models may possess limited health literacy. LLM's represent an advancement and potentially powerful clinical tool but still require ongoing evaluation, refinement, and validation. AI should continued to be viewed as an evolving resource rather than a replacement for clinical judgment.</p>","PeriodicalId":55459,"journal":{"name":"Arthroscopy-The Journal of Arthroscopic and Related Surgery","volume":" ","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Arthroscopy-The Journal of Arthroscopic and Related Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.arthro.2025.03.042","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
引用次数: 0

Abstract

Artificial intelligence (AI) and Language Learning Models (LLM) are rapidly evolving. Several popular and easily accessible platforms, like ChatGPT and Gemini, are increasingly being explored by clinicians and patients for their utility in clinical decision-making. While these tools provide rapid access to information, their inconsistent adherence to evidence-based guidelines raises concerns. A potential solution is to generate more specialized LLM's for orthopaedics. A curated database of validated orthoapediic literature can be used as input, in order to address concerns about the quality of input data. However, a curated LLM may still have limitations of selection bias and limited high-quality literature. In additionally, patients using these models may possess limited health literacy. LLM's represent an advancement and potentially powerful clinical tool but still require ongoing evaluation, refinement, and validation. AI should continued to be viewed as an evolving resource rather than a replacement for clinical judgment.

人工智能(AI)和语言学习模型(LLM)发展迅速。一些流行且易于访问的平台,如 ChatGPT 和 Gemini,越来越多地被临床医生和患者用于临床决策。虽然这些工具可以快速获取信息,但它们与循证指南的一致性却令人担忧。一个潜在的解决方案是为骨科生成更专业的 LLM。为了消除对输入数据质量的担忧,可以使用经过整理的骨科有效文献数据库作为输入。不过,经过筛选的 LLM 可能仍然存在选择偏差和高质量文献有限的局限性。此外,使用这些模型的患者可能具备有限的健康知识。LLM 代表着一种进步和潜在的强大临床工具,但仍需要不断评估、完善和验证。人工智能应继续被视为一种不断发展的资源,而不是临床判断的替代品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
9.30
自引率
17.00%
发文量
555
审稿时长
58 days
期刊介绍: Nowhere is minimally invasive surgery explained better than in Arthroscopy, the leading peer-reviewed journal in the field. Every issue enables you to put into perspective the usefulness of the various emerging arthroscopic techniques. The advantages and disadvantages of these methods -- along with their applications in various situations -- are discussed in relation to their efficiency, efficacy and cost benefit. As a special incentive, paid subscribers also receive access to the journal expanded website.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信