Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Advancements in large language model accuracy for answering physical medicine and rehabilitation board review questions
3
Zitationen
4
Autoren
2025
Jahr
Abstract
BACKGROUND: There have been significant advances in machine learning and artificial intelligence technology over the past few years, leading to the release of large language models (LLMs) such as ChatGPT. There are many potential applications for LLMs in health care, but it is critical to first determine how accurate LLMs are before putting them into practice. No studies have evaluated the accuracy and precision of LLMs in responding to questions related to the field of physical medicine and rehabilitation (PM&R). OBJECTIVE: To determine the accuracy and precision of two OpenAI LLMs (GPT-3.5, released in November 2022, and GPT-4o, released in May 2024) in answering questions related to PM&R knowledge. DESIGN: Cross-sectional study. Both LLMs were tested on the same 744 PM&R knowledge questions that covered all aspects of the field (general rehabilitation, stroke, traumatic brain injury, spinal cord injury, musculoskeletal medicine, pain medicine, electrodiagnostic medicine, pediatric rehabilitation, prosthetics and orthotics, rheumatology, and pharmacology). Each LLM was tested three times on the same question set to assess for precision. SETTING: N/A. PATIENTS: N/A. INTERVENTIONS: N/A. MAIN OUTCOME MEASURE: Percentage of correctly answered questions. RESULTS: For three runs of the 744-question set, GPT-3.5 answered 56.3%, 56.5%, and 56.9% of the questions correctly. For three runs of the same question set, GPT-4o answered 83.6%, 84%, and 84.1% of the questions correctly. GPT-4o outperformed GPT-3.5 in all subcategories of PM&R questions. CONCLUSIONS: LLM technology is rapidly advancing, with the more recent GPT-4o model performing much better on PM&R knowledge questions compared to GPT-3.5. There is potential for LLMs in augmenting clinical practice, medical training, and patient education. However, the technology has limitations and physicians should remain cautious in using it in practice at this time.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.549 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.443 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.941 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.792 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.