Brief Review — Large Language Models Answer Medical Questions Accurately, but Can’t Match Clinicians’ Knowledge
So, What are the usages for LLM?
Large Language Models Answer Medical Questions Accurately, but Can’t Match Clinicians’ Knowledge
LLM Can’t Match Clincians’ Knowledge, by Emily Harris,
2023 JAMA, Over 40 Citations (Sik-Ho Tsang @ Medium)Healthcare/Medical LLM Evaluation
2023 [GPT-4 in Radiology] [ChatGPT & GPT‑4 on USMLE] 2024 [ChatGPT & GPT-4 on Dental Exam] [ChatGPT-3.5 on Radiation Oncology] [LLM on Clicical Text Summarization] [Extract COVID-19 Symptoms Using ChatGPT & GPT-4] [ChatGPT on Patients Medication] [AI Chatbot Study on Drug Info for Patients] 2024 [Low Resource LLM on Health Info]
My Healthcare and Medical Related Paper Readings and Tutorials
==== My Other Paper Readings Are Also Over Here ====
Present
In this paper, author mentions that LLM such as Med-PaLM delivered highly accurate answers to multiple-choice and long-form medical questions, but it fell short of clinicians’ responses to those queries. Integrating their capabilities into clinical workflows remains a challenge.
- Authors mentions some of the successful cases for LLM in answering medical-related question answering databases.
- Yet, there are answers that are potentially harmful.
- The inappropriate or incorrect content could be clinically significant.
Future
- The findings helped identify areas for future research to narrow the gap in performance between clinicians and LLMs. Prompting the models to cite their sources in answers and to convey their uncertainty around a response are 2 avenues that researchers could pursue to mitigate incorrect responses.
- Given their limitations, many LLMs, including Med-PaLM, are not ready for broad integration into clinical workflows just yet, and certainly not without ahumanin the loop to verify their work.
Experts say the technology might soon be used to streamline non-clinical administrative and operational work. A lot of information in medicine is unstructured, such as long, convoluted clinical guidelines; patient notes; and medical records.
Clinicians could use LLMs to navigate that information. The models could pull a specific answer out of the guidelines, extract information from clinical notes for billing purposes, or create a medical encounter note that clinicians could edit.
- Hence, LLMs may have a role to play in mitigating burnout by saving health care professionals time that they can reinvest in caring for patients.
