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

Journal: BMC sports science, medicine & rehabilitation
Published Date: May 26, 2025

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.

Authors

  • Oscar Lederman
    Human 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 Llana
    Human Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
  • James Murray
    Human Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
  • Robert Stanton
    Molecular Informatics, Machine Learning and Computational Sciences, Early Clinical Development, Pfizer, Cambridge, MA 02139, USA.
  • Ritesh Chugh
    School of Engineering and Technology, Central Queensland University, Melbourne, VIC, Australia.
  • Darren Haywood
    Human Performance Research Centre, INSIGHT Research Institute, University of Technology Sydney (UTS), Sydney, NSW, Australia.
  • Amanda Burdett
    Human Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
  • Geoff Warman
    Human Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
  • Joanne Walker
    Human Performance Research Centre, School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney (UTS), Moore Park, Sydney, NSW, Australia.
  • Nicolas H Hart
    Cancer and Palliative Care Outcomes Centre, Centre for Healthcare Transformation, Faculty of Health, Queensland University of Technology, Brisbane, Qld, Australia.

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