Patients and donors trust in data authorization for AI-based medical research

Area of Science:

• Biomedical research
• Public healthcare
• Artificial intelligence

Background:

• Artificial intelligence (AI) is increasingly used in biomedical research and public healthcare.
• Concerns about data security and potential leaks are rising.
• Patient and volunteer perspectives on AI data usage are crucial for ethical implementation.

Purpose of the Study:

• To investigate the perspectives of neurological donor patients (dNP), healthy biobank donors (dHV), and non-donor volunteers (ndHV) on using their data for AI-powered biomedical research.
• To assess willingness to authorize the use of clinical, biochemical, and genetic data for AI.
• To evaluate acceptance of AI in diagnostic and therapeutic settings and for AI training.

Main Methods:

• Surveyed 115 participants (21 dNP, 17 dHV, 77 ndHV) on digital literacy, data use authorization for AI, and AI acceptance in healthcare.
• Assessed willingness to share clinical, biochemical, and genetic data for AI training (supervised/unsupervised) within or outside the European data space.
• Correlated digital competence with risk awareness, optimism, and data sharing preferences.

Main Results:

• High digital literacy and optimism towards AI were observed across groups, with low awareness of AI risks.
• Neurological donor patients (dNP) showed higher concern regarding data misuse (65%) compared to healthy biobank donors (dHV) (48%) and non-donors (ndHV) (20%).
• High digital competence correlated with increased risk awareness and optimism. Most participants would share data, but ndHV were less willing, especially for genetic data and unsupervised AI training.

Conclusions:

• Willingness to authorize data use for AI in medical research is linked to digital competence, trust, and clinical engagement.
• Neurological donor patients (dNP) are more favorable towards authorizing data for AI in diagnostic and therapeutic decisions.
• Health policies must prioritize bridging the digital divide, strengthening doctor-patient relationships, and ensuring transparency in data use for AI adoption in healthcare.