Artificial intelligence (AI) has garnered tremendous attention in health care, and many hope that AI can enhance our health system’s ability to care for people with chronic and degenerative conditions, including Parkinson’s Disease (PD). This paper reports the themes and lessons derived from a qualitative study with people living with PD, family caregivers, and health care providers regarding the ethical dimensions of using AI to monitor, assess, and predict PD symptoms and progression. Thematic analysis identified ethical concerns at four intersecting (...) levels: personal, interpersonal, professional/institutional, and societal levels. Reflecting on potential benefits of predictive algorithms that can continuously collect and process longitudinal data, participants expressed a desire for more timely, ongoing, and accurate information that could enhance management of day-to-day fluctuations and facilitate clinical and personal care as their disease progresses. Nonetheless, they voiced concerns about intersecting ethical questions around evolving illness identities, familial and professional care relationships, privacy, and data ownership/governance. The multi-layer analysis provides a helpful way to understand the ethics of using AI in monitoring and managing PD and other chronic/degenerative conditions. (shrink)

Artificial intelligence (AI) technologies in medicine are gradually changing biomedical research and patient care. High expectations and promises from novel AI applications aiming to positively impact society raise new ethical considerations for patients and caregivers who use these technologies. Based on a qualitative content analysis of semi-structured interviews and focus groups with healthcare professionals (HCPs), patients, and family members of patients with Parkinson’s Disease (PD), the present study investigates participant views on the comparative benefits and problems of using human versus (...) AI predictive computer vision health monitoring, as well as participants’ ethical concerns regarding these technologies. Participants presumed that AI monitoring would enhance information sharing and treatment, but voiced concerns about data ownership, data protection, commercialization of patient data, and privacy at home. They highlighted that privacy issues at home and data security issues are often linked and should be investigated together. Findings may help technologists, HCPs, and policymakers determine how to incorporate stakeholders’ intersecting but divergent concerns into developing and implementing AI PD monitoring tools. (shrink)