Can artificial intelligence improve patient educational material readability? A systematic review and narrative synthesis.

Journal: Internal medicine journal
PMID: 39720869

Abstract

Enhancing patient comprehension of their health is crucial in improving health outcomes. The integration of artificial intelligence (AI) in distilling medical information into a conversational, legible format can potentially enhance health literacy. This review aims to examine the accuracy, reliability, comprehensiveness and readability of medical patient education materials (PEMs) simplified by AI models. A systematic review was conducted searching for articles assessing outcomes of use of AI in simplifying PEMs. Inclusion criteria are as follows: publication between January 2019 and June 2023, various modalities of AI, English language, AI use in PEMs and including physicians and/or patients. An inductive thematic approach was utilised to code for unifying topics which were qualitatively analysed. Twenty studies were included, and seven themes were identified (reproducibility, accessibility and ease of use, emotional support and user satisfaction, readability, data security, accuracy and reliability and comprehensiveness). AI effectively simplified PEMs, with reproducibility rates up to 90.7% in specific domains. User satisfaction exceeded 85% in AI-generated materials. AI models showed promising readability improvements, with ChatGPT achieving 100% post-simplification readability scores. AI's performance in accuracy and reliability was mixed, with occasional lack of comprehensiveness and inaccuracies, particularly when addressing complex medical topics. AI models accurately simplified basic tasks but lacked soft skills and personalisation. These limitations can be addressed with higher-calibre models combined with prompt engineering. In conclusion, the literature reveals a scope for AI to enhance patient health literacy through medical PEMs. Further refinement is needed to improve AI's accuracy and reliability, especially when simplifying complex medical information.

Authors

  • Mohamed Nasra
    Department of Medicine, Northern Health, Melbourne, Victoria, Australia.
  • Rimsha Jaffri
    Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.
  • Davor Pavlin-Premrl
    Department of Neurology, Austin Health, Melbourne, Victoria, Australia.
  • Hong Kuan Kok
    Interventional Radiology Service, Department of Radiology, Beaumont Hospital, Dublin, Ireland.
  • Ali Khabaza
    Department of Interventional Neuroradiology, Austin Health, Melbourne, Victoria, Australia.
  • Christen Barras
    School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.
  • Lee-Anne Slater
    Department of Interventional Neuroradiology, Monash Health, Melbourne, Victoria, Australia.
  • Anousha Yazdabadi
    School of Medicine, Deakin University, Australia. Electronic address: anosha.yazdabadi@deakin.edu.au.
  • Justin Moore
    Department of Neurosurgery, Monash Health, Melbourne, Victoria, Australia.
  • Jeremy Russell
    Department of Neurosurgery, Austin Health, Heidelberg, Victoria, Australia.
  • Paul Smith
    Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.
  • Ronil V Chandra
    5 Interventional Neuroradiology Service, Monash Imaging, Monash Health, Clayton, Australia.
  • Mark Brooks
    5 Interventional Neuroradiology Service, Monash Imaging, Monash Health, Clayton, Australia.
  • Ashu Jhamb
    Department of Radiology, St Vincent's Hospital Melbourne Pty Ltd, Fitzroy, Victoria, Australia.
  • Winston Chong
    Monash Imaging and Department of Surgery, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
  • Julian Maingard
    School of Medicine, Deakin University Faculty of Health, Burwood, Victoria, Australia.
  • Hamed Asadi
    Neurointerventional Service, Department of Radiology, Beaumont Hospital, Dublin, Ireland; School of Medicine, Faculty of Health, Deakin University, Waurn Ponds, Australia. Electronic address: asadi.hamed@gmail.com.

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