Evidence map›Paper›PMID 41534067›Full record

ArticleJournal of medical Internet research2026

From Agents to Governance: Essential AI Skills for Clinicians in the Large Language Model Era.

Weiping Cao, Qing Zhang, Jialin Liu, Siru Liu

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
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  7. 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

4 authors.

Weiping CaoDepartment of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.ORCID https://orcid.org/0009-0000-9117-6367
Qing Zhang *Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.ORCID https://orcid.org/0000-0002-5652-4036
Jialin Liu *Information Center, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0002-1369-4625
Siru LiuDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID https://orcid.org/0000-0002-5003-5354

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large language models are rapidly transitioning from pilot schemes to routine clinical practice. This creates an urgent need for clinicians to develop the necessary skills to strike the right balance between seizing opportunities and taking accountability. We propose a 3-tier competency framework to support clinicians' evolution from cautious users to responsible stewards of artificial intelligence (AI). Tier 1 (foundational skills) defines the minimum competencies for safe use, including prompt engineering, human-AI agent interaction, security and privacy awareness, and the clinician-patient interface (transparency and consent). Tier 2 (intermediate skills) emphasizes evaluative expertise, including bias detection and mitigation, interpretation of explainability outputs, and the effective clinical integration of AI-generated workflows. Tier 3 (advanced skills) establishes leadership capabilities, mandating competencies in ethical governance (delineating accountability and liability boundaries), regulatory strategy, and model life cycle management-specifically, the ability to govern algorithmic adaptation and change protocols. Integrating this framework into continuing medical education programs and role-specific job descriptions could enhance clinicians' ability to use AI safely and responsibly. This could standardize deployment and support safer clinical practice, with the potential to improve patient outcomes.

Indexed as

Artificial IntelligenceHumansLarge Language Modelsagentartificial intelligencecliniciancompetencycontinuing medical educationeducationlarge language model

Identifiers

PMID41534067
PMCPMC12853083

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.