Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. The MoveONParkinson project aims to enhance exercise engagement among people with Parkinson’s Disease (PwPD) in the Portuguese context through the ONParkinson digital platform, which provides mobile and web interfaces. While the broader MoveONParkinson project has been previously described from a health-focused perspective, this study specifically focuses on the development and integration of an AI-driven conversational agent (CA) for the Portuguese language, called PANDORA, within the mobile interface of the solution to assist and motivate PwPD in their exercise routines. PANDORA (Parkinson Assistant in Natural Dialogue and Oriented by Rules and Assessments), designed based on Self-Determination Theory (SDT), addresses the psychological needs of autonomy, competence, and relatedness. A preliminary study involving 20 PwPD, 10 caregivers, and 5 healthcare professionals informed the design requirements for PANDORA. The development process involved four main phases: (1) Design of the Chatbot’s Motivation Model, (2) Design and implementation of the conversational agent, (3) Technical Performance Evaluation, and (4) User Experience Evaluation. Technical Performance Evaluation, conducted with three physiotherapists, assessed domain coverage, coherence response capacity, and dialog management capacity, achieving 100% accuracy in domain coverage and coherence response capacity and 89% in dialog management capacity. The User Experience Study involved eight PwPD users recruited from Portuguese healthcare units performing predefined tasks, with user satisfaction scores ranging from 4.2 to 4.9 on a five-point Likert scale. The findings indicate that integrating a conversational agent with motivational cues tends to increase patient engagement. However, further studies are required to determine PANDORA’s impact on exercise engagement in PwPD.

Background: Parkinson disease (PD) is the fastest-growing neurodegenerative disorder in the world, with prevalence expected to exceed 12 million by 2040, which poses significant health care and societal challenges. Artificial intelligence (AI) systems and wearable sensors hold potential for PD diagnosis, personalized symptom monitoring, and progression prediction. Nonetheless, ethical AI adoption requires several core principles, including user trust, transparency, fairness, and human oversight. Objective: This study aims to explore and synthesize the perspectives of diverse stakeholders, such as individuals living with PD, health care professionals, AI experts, and bioethicists. The aim was to guide the development of AI-driven digital health solutions, emphasizing transparency, data security, fairness, and bias mitigation while ensuring robust human oversight. These efforts are part of the broader Artificial Intelligence-Based Parkinson’s Disease Risk Assessment and Prognosis (AI-PROGNOSIS) European project, dedicated to advancing ethical and effective AI applications in PD diagnosis and management. Methods: An exploratory qualitative approach, based on 2 datasets constructed from cocreation workshops, engaged key stakeholders with diverse expertise to gather insights, ensuring a broad range of perspectives and enriching the thematic analysis. A total of 24 participants participated in the cocreation workshops, including 11 (46%) people with PD, 6 (25%) health care professionals, 3 (13%) AI technical experts, 1 (4%) bioethics expert, and 3 (13%) facilitators. Using a semistructured guide, key aspects of the discussion centered on trust, fairness, explainability, autonomy, and the psychological impact of AI in PD care. Results: Thematic analysis of the cocreation workshop transcripts identified 5 key main themes, each explored through various corresponding subthemes. AI trust and security (theme 1) was highlighted, focusing on data safety and the accuracy and reliability of the AI systems. AI transparency and education (theme 2) emphasized the need for educational initiatives and the importance of transparency and explainability of AI technologies. AI bias (theme 3) was identified as a critical theme, addressing issues of bias and fairness and ensuring equitable access to AI-driven health care solutions. Human oversight (theme 4) stressed the significance of AI-human collaboration and the essential role of human review in AI processes. Finally, AI’s psychological impact (theme 5) examined the emotional impact of AI on patients and how AI is perceived in the context of PD care. Conclusions: Our findings underline the importance of implementing robust security measures, developing transparent and explainable AI models, reinforcing bias mitigation and reduction strategies and equitable access to treatment, integrating human oversight, and considering the psychological impact of AI-assisted health care. These insights provide actionable guidance for developing trustworthy and effective AI-driven digital PD diagnosis and management solutions.

The growing emphasis on trustworthy artificial intelligence (AI) in health care reflects a shift away from models optimized for predictive performance toward governable and auditable systems that can be adopted and sustained in clinical practice. Nonetheless, many clinical AI applications continue to privilege technical performance while underaddressing ethical, regulatory, and societal considerations, leading to concerns around robustness, transparency, and clinical adoption. To address this, governance frameworks such as the Assessment List for Trustworthy Artificial Intelligence (ALTAI) have been proposed to operationalize trust-related requirements across the AI lifecycle. However, evidence on the practical use of these frameworks remains limited. In this Viewpoint, we describe the application of ALTAI as a procedural governance framework within the Horizon Europe AI-PROGNOSIS project, which aims to support Parkinson disease diagnosis and care through predictive models and digital biomarkers derived from everyday devices. The seven ALTAI requirements (ie, human agency and oversight; technical robustness and safety; privacy and data governance; transparency; diversity, nondiscrimination, and fairness; societal and environmental well-being; and accountability) were mapped to key stages of the AI lifecycle within the project, including design and specification, data preparation, model development and validation, user interface and user experience deployment, external prospective validation, and overarching management and workflow. To examine how these requirements were perceived in practice, we conducted a structured internal survey among AI developers and data scientists involved in the AI-PROGNOSIS project (n=10). Participants rated the relevance of the 17 ALTAI subdomains using a three-point prioritization scale. Technical accuracy, data governance, and privacy were consistently rated as highly relevant, whereas societal impact received the lowest prioritization. This pattern reflects a documented tension in AI development, where technical teams tend to deprioritize broader societal concerns under delivery and performance constraints. Nonetheless, this work should be interpreted as a context-specific case study rather than a validation of ALTAI. The small sample size and project-specific setting limit generalizability, and these findings should not be considered as representative of broader clinical AI development. Overall, by making prioritization gaps explicit and embedding multidisciplinary review across lifecycle checkpoints, this case study illustrates how structured governance frameworks can surface implementation tensions and support accountable AI development. While these approaches do not resolve all of the aforementioned challenges, they provide practical guidance for integrating trust-related considerations into clinical AI projects.

BACKGROUND Parkinson’s disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with Parkinson’s (PwP), informal caregivers, and healthcare 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 PwP, at‑risk individuals, caregivers, and healthcare 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 exploratory nature was distributed (from Dec. 2024 to Oct. 2025) in five languages. It was completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. Chi-square 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 >76% of respondents); however, stakeholder priorities diverged in other areas. Healthcare professionals rated improving patient and informal caregiver engagement as significantly more important than PwP did, χ²(1, N=205)=34.78, p<.001, Cramer’s V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most PwP did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. CONCLUSIONS While AI tools were viewed positively across groups, there were significant gaps in current usage. Stakeholder-specific preferences, including informal caregiver engagement and preventive lifestyle guidance, highlight the importance of tailored design. These findings offer early-stage insight to guide development of future AI-based solutions for PD.

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. χ ² 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, χ ² ₁ (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.