Evidence map›Paper›PMID 41031065›Full record

ArticleArXiv2024

Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare.

Yifan Yang, Qiao Jin, Qingqing Zhu, Zhizheng Wang, Francisco Erramuspe Álvarez, Nicholas Wan, Benjamin Hou, Zhiyong Lu

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Yifan YangNational Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.
Qiao JinNational Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.
Qingqing ZhuNational Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.
Zhizheng WangNational Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.
Francisco Erramuspe ÁlvarezNational Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.
Nicholas WanNational Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.
Benjamin HouNational Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.
Zhiyong LuNational Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large Language Models (LLMs) have gained significant attention in the medical domain for their human-level capabilities, leading to increased efforts to explore their potential in various healthcare applications. However, despite such a promising future, there are multiple challenges and obstacles that remain for their real-world uses in practical settings. This work discusses key challenges for LLMs in medical applications from four unique aspects: operational vulnerabilities, ethical and social considerations, performance and assessment difficulties, and legal and regulatory compliance. Addressing these challenges is crucial for leveraging LLMs to their full potential and ensuring their responsible integration into healthcare.

Identifiers

PMID41031065
PMCPMC12478440

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.