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Systematic review to understand users perspectives on AI-enabled decision aids to inform shared decision making.
Nehal Hassan1, Robert Slight2, Kweku Bimpong3
1School of Pharmacy / Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom. nehal.hassan@newcastle.ac.uk.
NPJ Digital Medicine
|November 21, 2024
Summary
Artificial intelligence (AI)-enabled decision aids enhance patient-clinician shared decision-making by offering personalized recommendations. Patients found AI aids user-friendly, improving treatment adherence, while clinicians noted concerns about data currency and treatment accuracy.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Clinical Decision Support
Background:
- Shared decision-making (SDM) is crucial for patient-centered care.
- AI-enabled decision aids offer personalized recommendations to support SDM.
- Understanding user perceptions of these AI tools is essential for effective implementation.
Purpose of the Study:
- To systematically review user perceptions of AI-enabled decision aids in healthcare.
- To identify benefits and challenges associated with AI tool usage in SDM.
- To inform the development and integration of AI decision aids.
Main Methods:
- Systematic review of 26 articles using PRISMA guidelines.
- Searches conducted across four databases.
- Narrative synthesis of qualitative and quantitative data on user perceptions.
Main Results:
- Patients reported AI aids as easy to understand and user-friendly, increasing treatment adherence.
- Clinicians raised concerns regarding information timeliness and potential for inappropriate treatment.
- Both patients and clinicians acknowledged challenges like bias and usability issues.
Conclusions:
- AI-enabled decision aids show promise in enhancing SDM and patient engagement.
- Addressing clinician concerns and potential biases is critical for successful AI adoption.
- Further research should focus on optimizing AI tool design and validation for clinical practice.
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