Back

Medical ChatGPT - A systematic Meta-Review

Egger, J.; Sallam, M.; Luijten, G.; Gsaxner, C.; Pepe, A.; Kleesiek, J.; Puladi, B.; Li, J.

2024-04-03 health informatics
10.1101/2024.04.02.24304716 medRxiv
Show abstract

Since its release at the end of 2022, ChatGPT has seen a tremendous rise in attention, not only from the general public, but also from medical researchers and healthcare professionals. ChatGPT definitely changed the way we can communicate now with computers. We still remember the limitations of (voice) assistants, like Alexa or Siri, that were "overwhelmed" by a follow-up question after asking about the weather, not to mention even more complex questions, which they could not handle at all. ChatGPT and other Large Language Models (LLMs) turned that in the meantime upside down. They allow fluent and continuous conversations on a human-like level with very complex sentences and diffused in the meantime into all kinds of applications and areas. One area that was not spared from this development, is the medical domain. An indicator for this is the medical search engine PubMed, which comprises currently more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books. As of March 2024, the search term "ChatGPT" already returns over 2,700 results. In general, it takes some time, until reviews, and especially systematic reviews appear for a "new" topic or discovery. However, not for ChatGPT, and the additional search restriction to "systematic review" for article type under PubMed, returns still 31 contributions, as of March 19 2024. After filtering out non-systematic reviews from the returned results, 19 publications are included. In this meta-review, we want to take a closer look at these contributions on a higher level and explore the current evidence of ChatGPT in the medical domain, because systematic reviews belong to the highest form of knowledge in science.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.