Evidence map›Paper›PMID 39372551›Full record

ReviewCellular and molecular bioengineering2024

Based on Medicine, The Now and Future of Large Language Models.

Ziqing Su, Guozhang Tang, Rui Huang, Yang Qiao, Zheng Zhang, Xingliang Dai

Abstract readReview
In one paragraph

Review in Cellular and molecular bioengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025
    Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ziqing Su *Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, 230022 P.R. China.
Guozhang Tang *Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, 230022 P.R. China.
Rui HuangDepartment of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, 230022 P.R. China.
Yang QiaoDepartment of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, 230022 P.R. China.
Zheng ZhangDepartment of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, 230022 P.R. China.
Xingliang DaiDepartment of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, 230022 P.R. China.ORCID 0000-0002-0685-4766

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This review explores the potential applications of large language models (LLMs) such as ChatGPT, GPT-3.5, and GPT-4 in the medical field, aiming to encourage their prudent use, provide professional support, and develop accessible medical AI tools that adhere to healthcare standards. Methods: This paper examines the impact of technologies such as OpenAI's Generative Pre-trained Transformers (GPT) series, including GPT-3.5 and GPT-4, and other large language models (LLMs) in medical education, scientific research, clinical practice, and nursing. Specifically, it includes supporting curriculum design, acting as personalized learning assistants, creating standardized simulated patient scenarios in education; assisting with writing papers, data analysis, and optimizing experimental designs in scientific research; aiding in medical imaging analysis, decision-making, patient education, and communication in clinical practice; and reducing repetitive tasks, promoting personalized care and self-care, providing psychological support, and enhancing management efficiency in nursing. Results: LLMs, including ChatGPT, have demonstrated significant potential and effectiveness in the aforementioned areas, yet their deployment in healthcare settings is fraught with ethical complexities, potential lack of empathy, and risks of biased responses. Conclusion: Despite these challenges, significant medical advancements can be expected through the proper use of LLMs and appropriate policy guidance. Future research should focus on overcoming these barriers to ensure the effective and ethical application of LLMs in the medical field.

Indexed as

ChatGPTGPTLarge language modelsMedicine

Identifiers

PMID39372551
PMCPMC11450117

What Socratic holds

Textmetadata
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.