Human-centered AI in healthcare: empowering patients and support persons in clinical decision-making
Artificial intelligence (AI) has emerged as a promising tool to enhance medical practice and improve patient outcomes. However, introducing AI in interactions between patients, support persons (SPs) and physicians may create real or perceived information asymmetries and may not always be well accepted by end-users. To ensure that AI contributes to patient empowerment rather than undermining it, there is a need to better understand how AI-based tools affect communication, trust and decision-making in clinical encounters. Research should focus on identifying how AI can support patients' autonomy, trust and acceptance, how it may strengthen the role of SPs and promote transparent and ethically sound care. With these findings, applying a human-centered design with established technology acceptance frameworks (e.g. TAM, UTAUT) will be crucial to guide evidence-based implementation. Only by involving patients, SPs and physicians in AI development can these technologies unfold their full potential to deliver equitable, interpretable and patient-centered healthcare.
Authors
- Roland Roller (ORCID: https://orcid.org/0000-0001-7900-4116)
- Bilgin Osmanodja (ORCID: https://orcid.org/0000-0002-8660-0722)
- Aljoscha Burchardt (ORCID: https://orcid.org/0000-0002-5117-5965)
- David Samhammer (ORCID: https://orcid.org/0000-0003-3111-9525)
- Anne Herrmann (ORCID: https://orcid.org/0000-0002-8855-4176)
- Sascha Eickmann
- Klemens Budde (ORCID: https://orcid.org/0000-0002-7929-5942)
- Peter Dabrock (ORCID: https://orcid.org/0000-0001-6928-9912)
- Sebastian Möller (ORCID: https://orcid.org/0000-0003-3057-0760)
- Max Tretter (ORCID: https://orcid.org/0000-0001-8067-247X)
- Zeineb Sassi (ORCID: https://orcid.org/0009-0004-9720-9528)
Institutions
- OTH Regensburg (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- University Hospital Regensburg (DE)
- German Research Centre for Artificial Intelligence (DE)
- Humboldt-Universität zu Berlin (DE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Technische Universität Berlin (DE)
- University of Regensburg (DE)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1186/s12911-025-03298-9
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.96
Funders
- Bundesministerium für Bildung und Forschung