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Background: As Parkinson disease (PD) rates increase, so does interest in finding new technological solutions for PD management. Despite substantial efforts to explore potential applications of artificial intelligence (AI) in PD management, research from the perspectives of people with PD on AI remains limited.

Objective: This study aims to explore the ethical considerations of AI in PD management from the perspective of people with PD.

Methods: A qualitative triangulation of 13 interviews and 2 focus groups (FGs) with a panel of expert-by-experience people with PD from 6 European countries was carried out using abductive thematic analysis. The 6 biomedical ethical principles conceptualized by Beauchamp and Childress guided the analysis. Participants varied in diagnosis, disease experiences, and technological backgrounds. A researcher with PD was involved from start to finish, providing valuable insights into data collection and analysis.

Results: Although optimistic that AI could enhance autonomy and beneficence through personalized, actionable insights for people with PD and their health care professionals, concerns arose over patient involvement, model accuracy and privacy, ethical injustices, and the psychological impact. Risk prediction, prognosis, and medication response were viewed differently in terms of potential value and ethical considerations, with risk prediction being perceived as the most ethically complex. To uphold autonomy, it was considered important for AI insights to be patient-accessible, and sensitive insights should be communicated by a health care professional who recognizes individual differences in desiring and responding to AI predictions.

Conclusions: While people with PD felt AI could personalize (self-)care and increase autonomy, concerns about psychological harm and widening inequalities highlight the importance of ethical safeguards. Our findings underscore the importance of AI integrations that prioritize individual needs, actively engage people with PD in the development, implementation, and interpretation of predictive AI, and establish guidelines to support health care professionals and minimize patient harm. Different forms of implementation and precautions should be taken for risk, progression, and medication response prediction.

Objectives To identify the lowest sensitivity and specificity that physicians and the general population consider acceptable for medical artificial intelligence (AI), relative to current human performance. Methods In a nationwide, cross-sectional survey in Sweden, 2025, random samples of 500 physicians and 500 adults from the general population were mailed a questionnaire presenting three vignettes (chest pain triage, sore throat triage, ECG myocardial infarction detection) with the corresponding human performance. Participants reported the maximum number of cases an AI should be allowed to miss or over-refer.

Results Response rates were 45% among physicians and 31% in the general population. Both groups demanded higher AI accuracy than the human benchmark for all cases. In the chest pain triage vignette, the nurse correctly referred 84 of 100 true emergencies; physicians required the AI to correctly refer 11 additional patients (95% sensitivity) and the general population demanded referral of 16 additional patients (100% sensitivity) (p<0.001 for both groups). Among 100 patients not requiring referral, the nurse would mistakenly refer 66. Both groups required the AI to reduce unnecessary referrals by 16 (50% specificity) (p<0.001). A similar pattern was observed in the other vignettes. Discussion The accuracy thresholds required by the respondents exceed the performance of many existing systems, although emerging AI research shows promise in narrowing the gap.

Conclusion Physicians and the general population require medical AI systems to outperform human clinicians. When implementing AI in healthcare settings, early engagement with both groups may be necessary to align expectations with real-world system performance.

Patients' use of generative AI (GenAI) independently from healthcare is increasing across diseases. Little is known about its use among people with Parkinson's disease (PwP). This exploratory convenience-sample, mixed-methods online survey (n = 149, 19 countries) explored PwP's use of GenAI. Among our respondents, 65% had used GenAI, of which 40% had used it for disease-specific inquiries. Qualitative analysis identified informational, interpretive, and preparational uses of GenAI. As PwP increasingly bring AI-assisted data to consultations, clinicians must now actively engage in discussions about these tools, to support shared decision-making, safety and transparency.

Background: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers, and health care professionals. With growing interest in digital and predictive artificial intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. Objective: This work aims to (1) identify digital practices for PD management among persons with PD, at-risk individuals, caregivers, and health care professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. Methods: An anonymous cross-sectional online survey of an exploratory nature was distributed (from December 2024 to October 2025) in 5 languages and completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. χ2 tests were performed to examine stakeholder differences in desired AI features. Results: Interest in predictive AI was high across stakeholder groups. Symptom tracking was the most desired feature (selected by more than 76% of the respondents), and personalized treatment recommendations came second for both persons with PD and health care professionals; however, stakeholder priorities diverged in other areas. Health care professionals rated improving patient and informal caregiver engagement as significantly more important than persons with PD did, χ21 (n=205)=34.78, P<.001, and Cramer V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most persons with PD did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. Conclusions: While predictive AI tools were viewed positively across groups, there were significant gaps in stakeholder preferences, highlighting the importance of tailored, context-aware design. Early diagnosis was not prioritized by persons with PD or health care professionals, likely reflecting the complexity of diagnosing PD in the absence of disease-modifying therapies. Coupled with the emphasis placed on preventive lifestyle guidance by persons with PD and those at risk, this highlights the importance of actionability in AI-based monitoring and prediction. Such actionability may also enhance perceived relevance and uptake, given that reported interest in digital health tools and self-tracking exceeded actual use. These findings offer early-stage insight to guide the development of future AI-based solutions for PD.

Young people are among the most intensive users of digital and generative artificial intelligence (GenAI)-enabled mental health tools, yet they remain underrepresented in the research and design processes that shape these technologies. Although participatory approaches such as co-design and patient and public involvement are widely endorsed as best practices, youth involvement in digital youth mental health (DYMH) research is often inconsistent, superficial, or limited to late-stage consultation. This participation gap risks producing interventions that are misaligned with young people's lived experiences, priorities, and vulnerabilities, particularly in the context of rapidly evolving and scalable GenAI systems. This Viewpoint aims to reexamine the underlying drivers of the participation gap in DYMH research; clarify how participation is conceptualized and implemented across disciplines; and propose concrete, actionable recommendations to support more meaningful and consistent youth involvement across the research life cycle. We draw on interdisciplinary literature from digital mental health, human-computer interaction, child-computer interaction, and health research policy. Our Viewpoint integrates conceptual frameworks (eg, Lundy's model of participation), existing reviews of co-design practices, and emerging evidence on GenAI in mental health. We adopt a life cycle-oriented perspective to examine how youth participation is distributed across stages of research and development, including problem formulation, design, implementation, and evaluation. We identify 3 interrelated drivers of the participation gap. First, conceptual and linguistic fragmentation obscures what participation entails in practice, with terms such as co-design, participatory design, user-centered design, and patient and public involvement used inconsistently across disciplines. Second, youth involvement is uneven across the research life cycle, with participation often concentrated in early ideation or usability testing but largely absent from upstream decision-making and downstream evaluation. Third, institutional barriers-including ethics review processes, consent requirements, funding constraints, and adult-centric research norms-systematically limit meaningful youth partnership. These challenges are amplified in the context of GenAI, where opaque "black box" systems, simulated therapeutic interactions, and rapid deployment cycles introduce distinct risks if youth perspectives are not integrated. We propose a set of minimum expectations to address these gaps, including explicit specification of participatory models, life cycle mapping of youth involvement, reporting of youth influence on decisions, dedicated funding for participation, proportional ethics frameworks, and mechanisms for youth-informed governance of GenAI systems. Closing the participation gap in DYMH research is both an ethical imperative and a practical necessity. Moving beyond aspirational commitments requires embedding youth participation as a standard, technologies, failure to do so risks producing interventions that are scalable but not safe, credible, or responsive to the needs of young people.

Background: Large language model (LLM)-based chatbots are rapidly being repurposed as patient-facing digital health tools. Their interactive, adaptive, and seemingly empathic behavior can heighten engagement and expectancy-nonspecific factors that complicate causal inference. Yet, comparator strategies in LLM trials are inconsistently defined and often undermatched (eg, minimal education vs highly engaging chatbots), risking biased effect estimates and poor reproducibility. Objective: The aim of this study was to systematically identify and categorize the control conditions used in interventional studies of LLM-based, patient-facing digital health interventions and to evaluate their methodological appropriateness. Secondary aims are to describe variability by health domain and study design and to explore whether control type/quality relates to the direction of reported effects. Methods: This protocol follows PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) and is registered in PROSPERO. Eligible studies are interventional designs that evaluate LLM-based, patient-facing digital health interventions; any control condition is eligible (including no control, waitlist, treatment-as-usual, attention/education, active comparator, or sham digital control). We will search PubMed, PsycINFO, CENTRAL, CINAHL, and Scopus for records from January 1, 2023, onward. All records will be managed and screened in Rayyan by 2 independent reviewers. Dual, independent data extraction will target study context, intervention details, and control-arm characteristics (typology, rationale, matching to nonspecifics, blinding, reporting). No formal risk-of-bias assessments are planned, as the focus is on meta-research. Results: At submission, the protocol is registered in PROSPERO and has received no specific funding. Scoping searches are complete; full screening and extraction have not yet commenced. Conclusions: This review will provide an empirical map of control practices in LLM chatbot trials and guidance for designing better-matched comparators, supporting more valid and interpretable evaluations as LLMs diffuse into patient care. Trial Registration: PROSPERO CRD420251246148; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251246148 International Registered Report Identifier (IRRID): PRR1-10.2196/90507

In countries where patients can access the electronic health record (EHR), they enhance its accuracy by requesting corrections of EHR errors (EHRrors). This study compared national legislative frameworks in Sweden, the UK, and Germany governing patient rights to changing, adding, and deleting EHR information against General Data Protection Regulation (GDPR) and the forthcoming European Health Data Space (EHDS). We found that while national laws largely mirrored GDPR, they offered different additional mechanisms to EHRror management. Importantly, the EHDS positions the rectification process in the digital health service, opening the possibility for in-EHR patient input, which may further patients’ contribution to maintaining accurate clinical documentation and enhance patient agency.