Evidence map›Paper›PMID 40916960›Full record

ReviewRevista de neurologia2025

[Proposal for Responsible Use of Generative Artificial Intelligence in Medical Practice].

David A Pérez Martínez

Abstract readEnglish AbstractReview
In one paragraph

Review in Revista de neurologia, 2025. 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. 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

1 author.

David A Pérez MartínezServicio de Neurología, Hospital Universitario 12 de Octubre, 28041 Madrid, Español.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe advancement of artificial intelligence (AI), particularly generative AI, has significantly transformed the field of medicine, impacting healthcare delivery, medical education, and research. While the opportunities are substantial, the implementation of AI also raises important ethical and technical challenges, including risks related to data bias, the potential erosion of clinical skills, and concerns about information privacy. DEVELOPMENT: AI has demonstrated great potential in optimizing both clinical and educational processes. However, its operation based on probabilistic prediction is inherently prone to errors and biases. Healthcare professionals must be aware of these limitations and advocate for a transparent, responsible, and safe integration of AI, while maintaining full ethical and legal responsibility for clinical decisions. It is essential to safeguard traditional clinical competencies and prioritize the use of AI in automating low-value, repetitive tasks. In biomedical research, transparency and independent validation are crucial to ensure the reproducibility of findings. Similarly, in medical education, structured training in AI is vital to enable professionals to apply these tools safely and effectively in clinical practice.

conclusionsGenerative AI offers a transformative potential for medicine, but its adoption must be guided by rigorous ethical standards. Comprehensive training, risk mitigation, and the preservation of core clinical skills are essential pillars for its responsible implementation. This transformation must be led by the medical profession to ensure a patient-centered approach to care.

Indexed as

Artificial IntelligenceEducation, MedicalGenerative Artificial IntelligenceHumansartificial intelligencebiomedical researchclinical practicedelivery of health caremedical educationmedical ethics

Identifiers

PMID40916960
PMCPMC12415884

What OpenQuestion 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 OpenQuestion graph.