Evidence map›Paper›PMID 40420209›Full record

ReviewBMC sports science, medicine & rehabilitation2025

Promises and perils of generative artificial intelligence: a narrative review informing its ethical and practical applications in clinical exercise physiology.

Oscar Lederman, Alessandro Llana, James Murray, Robert Stanton, Ritesh Chugh, Darren Haywood, Amanda Burdett, Geoff Warman, Joanne Walker, Nicolas H Hart

Erratum issuedAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMC sports science, medicine & rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Oscar LedermanHuman Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia. oscar.lederman@uts.edu.au.
Alessandro LlanaHuman Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
James MurrayHuman Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
Robert StantonSchool of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD, Australia.
Ritesh ChughSchool of Engineering and Technology, Central Queensland University, Melbourne, VIC, Australia.
Darren HaywoodHuman Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
Amanda BurdettHuman Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
Geoff WarmanHuman Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
Joanne WalkerHuman Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
Nicolas H HartHuman Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative Artificial Intelligence (GenAI) is transforming various sectors, including healthcare, offering both promising opportunities and notable risks. The infancy and rapid development of GenAI raises questions regarding its effective, safe, and ethical use by health professionals, including clinical exercise physiologists. This narrative review aims to explore existing interdisciplinary literature and summarise the ethical and practical considerations of integrating GenAI into clinical exercise physiology practice. Specifically, it examines the 'promises' of improved exercise programming and healthcare delivery, as well as the 'perils' related to data privacy, person-centred care, and equitable access. Recommendations for the responsible integration of GenAI in clinical exercise physiology are described, in addition to recommendations for future research to address gaps in knowledge. Future directions, including the roles and responsibilities of specific stakeholder groups are discussed, highlighting the need for clear professional guidelines in facilitating safe and ethical deployment of GenAI into clinical exercise physiology practice. Synthesis of current literature serves as an essential step in guiding strategies to ensure the safe, ethical, and effective integration of GenAI in clinical exercise physiology, providing a foundation for future guidelines, training, and research to enhance service delivery while maintaining high standards of practice.

Indexed as

Artificial intelligenceChatGPTClinical exercise physiologyGenerative artificial intelligenceHealthcareNarrative review

Identifiers

PMID40420209
PMCPMC12105363

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.