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Management of Patient and Physician Preferences and Explanations for Participatory Evaluation of Treatment with an Ethical Seal

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 13897)


Clinical practice guidelines (CPGs) suffer from several limitations, including limited patient participation. APPRAISE-RS is a methodology for generating treatment recommendations that overcomes this limitation by enabling both patients and clinicians to express their personal preferences about the treatment outcomes. However, patient, and clinical preferences are treated with equal importance, while it seems reasonable/fair to give more importance to clinicians’ preferences as they have more experience on the matter. In this work we present APPRAISE-RS-E, which considers different ponderations when including users’ preferences based on their experience for the generation of treatment recommendations. Moreover, since users are involved in the decision loop, an explanation of the recommendations is provided. Finally, as APPRAISE-RS-E uses AI methods, it has been evaluated using a set of principles and observable indicators, getting an ethical seal that informs users about the ethical issues involved. The experiments have been carried out in the field of attention deficit hyperactivity disorder (ADHD).


  • Computerized clinical practice guidelines
  • Clinical decision support systems
  • Ethical AI
  • Explainable AI
  • Participatory medicine
  • ADHD

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This work received joint funding from the European Regional Development Fund (ERDF), the Spanish Ministry of the Economy, Industry and Competitiveness (MINECO), the Carlos III Research Institute (grant no. PI19/00375), and received support from the Generalitat de Catalunya 2021 SGR 01125.

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Correspondence to Oscar Raya .

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Raya, O., Castells, X., Ramírez, D., López, B. (2023). Management of Patient and Physician Preferences and Explanations for Participatory Evaluation of Treatment with an Ethical Seal. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34343-8

  • Online ISBN: 978-3-031-34344-5

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