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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.

Objective

To examine UK general practitioners' (GPs) adoption of ambient artificial intelligence (AI) scribes and to assess user-reported error rates, workflow impact and consent practices in primary care.

Methods

We conducted a nationwide online mixed-methods survey of GPs recruited via Doctors.net.uk. Detailed analyses of use, errors and workflow impact focused on current users of ambient AI scribes.

Results

In August 2025, of 1003 respondents, 14% (n=141) reported current use of ambient AI scribes, 39% (n=396) intended to adopt them soon and 46% (n=466) had no plans to use them. Among users (n=141), Heidi Health predominated (86%). Most reported efficiency gains: 80% (n=112) reported reduced time spent on documentation and 70% (n=99) reduced cognitive load. Documentation quality was judged positively, with 55% (n=78) rating outputs as better than standard notes. Errors were common but usually minor: 32% (n=45) reported errors often/always, including 14% (n=20) with significant-to-critical implications. Errors were most frequent in multiparty consultations (38%), complex histories (35%) and non-English encounters (31%). Consent practices varied: 63% (n=89) routinely sought consent, with <= 10% of patients declining. Free-text responses (21% of users) highlighted benefits for workflow, alongside concerns about accuracy, ethics and system integration.

Discussion

Findings suggest that ambient AI scribes deliver meaningful efficiency gains and improved perceived documentation quality, but introduce non-trivial risks related to accuracy, equity and medicolegal accountability. The uneven performance in complex and multilingual consultations raises particular concerns about potential exacerbation of healthcare disparities.

Conclusion

Ambient AI scribes are already in use across UK primary care. Proactive regulation, consistent consent practices and independent evaluation, including patient perspectives, are urgently needed to ensure safe, equitable and sustainable implementation.

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.

Background: Patients' digital access to their personal health data is becoming increasingly common worldwide. However, medical documentation often contains technical language and sensitive information, which can lead to potential misunderstandings and distress among patients. These issues may be particularly impactful in mental health contexts. Large language models (LLMs) offer a promising approach by transforming clinician-generated health notes into language that is more patient-centered, nonmedicalized, and empathetic. However, risks related to accuracy and clinical safety have not been adequately investigated in psychiatry.

Objective: This study aimed to qualitatively analyze the errors introduced by LLMs when transforming notes written by psychiatrists into patient-facing formats. It also highlights the implications for clinical communication and patient safety.

Methods: Clinical notes (n=63) written by 19 psychiatrists in an outpatient treatment setting were collected, anonymized, and translated from German to English by humans. OpenAI GPT-3.5 Turbo was used to develop a preprompt that transformed these notes into a patient-centered, lay-readable form through an iterative process. Three psychiatrists qualitatively analyzed the LLM-revised documentation using Kuckartz content analysis. They compared the preconversion and postconversion notes to systematically identify and categorize LLM-induced errors.

Results: Five categories of clinically relevant errors were identified: (1) clinical misinterpretations, particularly in critical assessments such as suicidality, where nuanced terminology was oversimplified or inaccurately represented; (2) attribution errors, where behaviors or roles within family dynamics or interactions were incorrectly attributed to different individuals; (3) content distortion errors, which were characterized by speculative additions, emotional exaggerations, and inappropriate contextual assumptions; (4) abbreviation and terminology errors, which resulted from inaccurate expansions of medical abbreviations and terms; and (5) structural and syntax errors, which resulted in ambiguity, particularly when the original notes were brief or bulleted. Despite significant improvements in the readability and overall linguistic fluency of the converted notes, these errors occurred.

Conclusions: LLMs have the potential to transform psychiatric notes into patient-friendly formats. However, critical errors remain prevalent and can impair clinical judgment, understanding of patient circumstances, clarity of medication regimens, and interpretation of clinical observations. To safely integrate artificial intelligence-generated documentation into psychiatric care, clinician oversight and targeted model refinement are essential. Future research should explore strategies to mitigate these errors, assess their comprehensive clinical impact, and incorporate patient and provider perspectives to ensure robust implementation.

Digital Health Platforms (DHPs) have expanded rapidly, offering new possibilities for healthcare access, monitoring, education, and treatment across diverse contexts. Despite their potential, DHPs risk reinforcing existing health inequities due to unequal access to technology, limited digital or health literacy, and poor consideration of users’ social contexts. Several health frameworks, including the World Health Organisation, emphasise that digital health (DH) must be ethical, safe, inclusive, and aligned with the social determinants of health. Within this landscape, women’s healthcare remains markedly under-researched, receiving a disproportionately small share of DH investment, contributing to persistent gaps in care quality and raising concerns about bias in women-focused DHPs. This study examined how graphic user interface design in women-focused DHPs functions as a mechanism of mediation and control, reinforcing norms and stereotypes and shaping subjectivities. The study adopts an abductive approach, applying critical discourse analysis and feminist and queer theories to a corpus of women-focused DHPs identified through a survey of peer-reviewed research databases. The study analyses how dominant aesthetics, often characterised by pink colours, infantilised illustrations, and care-oriented imagery, reinforce normative femininity, gender stereotypes, and power hierarchies. By integrating critical discourse analysis and feminist and queer theoretical lenses, the study examines how visual interfaces reveal intersectional gaps in representation and proposes alternative approaches that expand inclusivity and diversity within DH.

Background: While awareness of Fibromyalgia (FM) has evolved across healthcare and social settings, individuals with FM continue reporting poor understanding of their condition in the everyday.Objective To explore the lived experiences and subjective illness narratives of individuals with FM, focusing on personal understandings of FM, outlook on healthcare, and perceived attitudes of surrounding peers.

Methods: We carried out four online focus groups with individuals diagnosed with FM. Participants were recruited from a pool of patients in a pain management centre in the Southwest of England, UK, that previously took part in a FM management course, 'Body Reprogramming Course' (BRC). Discussion topics included the personal understanding of FM (illness identity, timeline, causes, consequences, and controllability), history with FM diagnosis and treatment, attitudes of family and friends towards FM, as well as personal attitude towards medication. Audio recordings from focus groups were transcribed and analysed by two primary coders, using a reflexive thematic analysis approach. Codes were iteratively discussed and refined with two additional coders.

Results: 20 individuals with FM took part in four focus groups, aged between 25 and 67 years. Analysis of the data revealed 5 overarching themes and 10-subthemes related to lived experiences of FM: 1) The individual journey; 2) FM in healthcare; 3) FM in social context; 4) FM in personal context; 5) Experiences with BRC. Although participants felt that there has been some positive societal shift towards FM, most discussions concerned personal struggles with conveying to family and healthcare professionals the impact of the condition. Participants described a significant emotional toll of dealing with hidden struggles. Many expressed disappointment with prior care, describing it as fragmented, and some expressed strong opposition to medication-based treatments. Reflecting on the BRC, participants liked the coping strategies and the developed connections.

Conclusions: While participants recognised some positive changes in others' understanding of FM, the overwhelming sentiment was that FM remains overall poorly received and supported by peers and healthcare professionals. Participants in the BRC described their experience of FM as part of a journey toward a higher purpose, linking their illness to personal growth.