Evidence map›Paper›PMID 37487184›Full record

ArticleThe American journal of bioethics : AJOB2023

Ethics Education for Healthcare Professionals in the Era of ChatGPT and Other Large Language Models: Do We Still Need It?

Vasiliki Rahimzadeh, Kristin Kostick-Quenet, Jennifer Blumenthal Barby, Amy L McGuire

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Article in The American journal of bioethics : AJOB, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

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  18. Generative-AI-Generated Challenges for Health Data Research.The American journal of bioethics : AJOB · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Vasiliki RahimzadehBaylor College of Medicine.ORCID 0000-0003-3537-7601
Kristin Kostick-QuenetBaylor College of Medicine.ORCID 0000-0003-2510-0174
Jennifer Blumenthal BarbyBaylor College of Medicine.ORCID 0000-0003-1054-619X
Amy L McGuireBaylor College of Medicine.ORCID 0000-0002-7819-519X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we contend with whether we still need traditional ethics education as part of healthcare professional training given the abilities of chatGPT (generative pre-trained transformer) and other large language models (LLM). We reflect on common programmatic goals to assess the current strengths and limitations of LLMs in helping to build ethics competencies among future clinicians. Through an actual case analysis, we highlight areas in which chatGPT and other LLMs are conducive to common bioethics education goals. We also comment on where such technologies remain an imperfect substitute for human-led ethics teaching and learning. Finally, we conclude that the relative strengths of chatGPT warrant its consideration as a teaching and learning tool in ethics education in ways that account for current limitations and build in flexibility as the technology evolves.

Indexed as

BioethicsHealth PersonnelLanguageCurriculumGenerative Artificial IntelligenceHumansLarge Language ModelsModels, EducationalbioethicschatGPThealthcare professionalsmedical education

Identifiers

What OpenQuestion 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 OpenQuestion graph.