Abstract
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. At present, the
biopsychosocial model is not well prepared for this.
When adopting machine learning technology in clinical practice, new skills and expertise will be required from physicians. They should be able to weigh and explain algorithms supported decisions to their patients. Moreover,
new research should be designed in such a way that data will be suited for machine learning and can be integrated with existing databases in order to increase their size and scope.
Conclusion: Currently, the biopsychosocial model is not well prepared to accommodate the possibilities of personalized medicine. Adaptations are needed to deal with the highly individual aspects of the patient's disease.
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. At present, the
biopsychosocial model is not well prepared for this.
When adopting machine learning technology in clinical practice, new skills and expertise will be required from physicians. They should be able to weigh and explain algorithms supported decisions to their patients. Moreover,
new research should be designed in such a way that data will be suited for machine learning and can be integrated with existing databases in order to increase their size and scope.
Conclusion: Currently, the biopsychosocial model is not well prepared to accommodate the possibilities of personalized medicine. Adaptations are needed to deal with the highly individual aspects of the patient's disease.
| Original language | English |
|---|---|
| Article number | 111207 |
| Number of pages | 4 |
| Journal | Journal of Psychosomatic Research |
| Volume | 168 |
| Early online date | 13 Mar 2023 |
| DOIs | |
| Publication status | Published - 1 May 2023 |