Will the biopsychosocial model of medicine survive in the age of artificial intelligence and machine learning?

Sep 17, 2026

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Background: the biomedical model of medicine was replaced by the biopsychosocial model to better accommodate psychological and social aspects of illness. The rise of machine learning techniques enables prospects for truly personalized medicine, creating new challenges for existing medical models. Aim: explore the implications of personalized medicine for the biopsychosocial model. Results and conclusion: predictive machine-learning models can integrate diverse data and individual risk and protective factors, but the biopsychosocial model is not yet well prepared and requires adaptations for highly individualized disease aspects and physician decision support.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Abstract

Introduction

Biomedical model to biopsychosocial model

Challenges from AI and machine learning

Methods

Results

When adopting machine learning in clinical practice

Conclusion

Will the biopsychosocial model of medicine survive in the age of artificial intelligence and machine learning? Citation for published version (APA): Leentjens, A. F. G. , & Smith, S. L. (2023) . Will the biopsychosocial model of medicine survive in the age of artificial intelligence and machine learning? Journal of Psychosomatic Research, 168(1), Article 111207. [https://doi.org/10.1016/j.jpsychores.2023.111207](https://doi.org/10.1016/j.jpsychores.2023.111207) Document status and date: Published: 01/05/2023 DOI: 10.1016/j.jpsychores.2023.111207 Document Version: Publisher's PDF, also known as Version of record Document license: Taverne Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement: [www.umlib.nl/taverne-license](www.umlib.nl/taverne-license) Take down policy If you believe that this document breaches copyright please contact us at: [repository@maastrichtuniversity.nl](repository@maastrichtuniversity.nl) providing details and we will investigate your claim. Download date: 24 Oct. 2024 Journal of Psychosomatic Research 168 (2023) 111207 Contents lists available at ScienceDirect Journal of Psychosomatic Research journal [homepage:](homepage: www.elsevier.com/locate/jpsychores)[ www.elsevier.com/locate/jpsychores](homepage: www.elsevier.com/locate/jpsychores) EAPM Paper Will the biopsychosocial model of medicine survive in the age of artificial intelligence and machine learning?☆ A R T I C L E I N F O Keywords Biopsychosocial model Psychosomatic Artificial intelligence Machine learning Predictive model A B S T R A C T Background: the biomedical model of medicine was replaced by the biopsychosocial model in order to better accommodate psychological and social aspects of illness. The introduction of machine learning techniques provides the perspective of truly personalized medicine. This poses new challenges to our medical model. Aim: to explore the implications of personalized medicine for the biopsychosocial model. Methods: scholarly reflection. Results: The ability of machine learning technology to integrate a wide diversity of data makes it possible to develop predictive models for presentation, course and treatment response in individual patients. Such models are based on individual risk factors and protective factors that may have diverging influences in different individuals. In a medical model adjusted to accommodate the possibilities of personalized medicine, it should be possible to highlight the importance and impact of each single factor in each individual patient

FAQs

Why was the biopsychosocial model adopted in medicine?

The biomedical model was replaced to better accommodate psychological and social aspects of illness.

What does machine learning enable for personalized medicine in this article?

It enables predictive models that integrate diverse data to forecast presentation, course, and treatment response in individual patients using risk and protective factors.

What limitations are identified for the biopsychosocial model with machine learning–driven personalized medicine?

The biopsychosocial model is not well prepared; adaptations are needed to address highly individual aspects of patients' diseases, and clinicians must be able to weigh and explain algorithm-supported decisions.

Sep 17, 2026

Upload by Liam