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.
Farah Selman, Kristine Obletz, Valeria Vismara, Robert Putko, Nicholas P J Perry
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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.
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.