iHealth: The ethics of artificial intelligence and big data in mental healthcare

Rubeis, Giovanni

doi:10.1016/j.invent.2022.100518

Cited by 41 publications

(18 citation statements)

References 53 publications

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“…Several ethical considerations arise when developing tools for highly personalized self-monitoring. First, the (sometimes qualitative) data are sensitive, and the privacy of the client should be well protected [ 105 ]. For PETRA, software and data transmission security are guaranteed by RoQua [ 38 ] and are in compliance with the GDPR.…”

Section: Discussionmentioning

confidence: 99%

“…This is especially relevant as only standardized diaries have been investigated in randomized controlled trials [ 26 , 27 , 29 - 31 ]. Extensive personalization options are clearly desired in clinical practice but may mean that effective components may differ across clients, which may complicate research into the effectiveness of personalized EMA [ 105 ]. Furthermore, future trials should not only solely focus on symptom improvement but also on more proximate outcomes relevant to the mechanisms of EMA, such as self-management and the therapeutic alliance [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

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A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application

Bos¹,

Klipstein²,

Emerencia³

et al. 2022

JMIR Ment Health

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Background Smartphone self-monitoring of mood, symptoms, and contextual factors through ecological momentary assessment (EMA) provides insights into the daily lives of people undergoing psychiatric treatment. Therefore, EMA has the potential to improve their care. To integrate EMA into treatment, a clinical tool that helps clients and clinicians create personalized EMA diaries and interpret the gathered data is needed. Objective This study aimed to develop a web-based application for personalized EMA in specialized psychiatric care in close collaboration with all stakeholders (ie, clients, clinicians, researchers, and software developers). Methods The participants were 52 clients with mood, anxiety, and psychotic disorders and 45 clinicians (psychiatrists, psychologists, and psychiatric nurses). We engaged them in interviews, focus groups, and usability sessions to determine the requirements for an EMA web application and repeatedly obtained feedback on iteratively improved high-fidelity EMA web application prototypes. We used human-centered design principles to determine important requirements for the web application and designed high-fidelity prototypes that were continuously re-evaluated and adapted. Results The iterative development process resulted in Personalized Treatment by Real-time Assessment (PETRA), which is a scientifically grounded web application for the integration of personalized EMA in Dutch clinical care. PETRA includes a decision aid to support clients and clinicians with constructing personalized EMA diaries, an EMA diary item repository, an SMS text message–based diary delivery system, and a feedback module for visualizing the gathered EMA data. PETRA is integrated into electronic health record systems to ensure ease of use and sustainable integration in clinical care and adheres to privacy regulations. Conclusions PETRA was built to fulfill the needs of clients and clinicians for a user-friendly and personalized EMA tool embedded in routine psychiatric care. PETRA is unique in this codevelopment process, its extensive but user-friendly personalization options, its integration into electronic health record systems, its transdiagnostic focus, and its strong scientific foundation in the design of EMA diaries and feedback. The clinical effectiveness of integrating personalized diaries via PETRA into care requires further research. As such, PETRA paves the way for a systematic investigation of the utility of personalized EMA for routine mental health care.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application

Bos¹,

Klipstein²,

Emerencia³

et al. 2022

JMIR Ment Health

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“…In the same vein, G. Rubeis [10], in 2022, addresses the ethical dimensions of integrating AI and big data into mental healthcare, a concept the author terms "iHealth". Rubeis points to the dual-edged sword of self-monitoring, ecological momentary assessment (EMA), and data mining, presenting a detailed ethical analysis of the potential of iHealth.…”

Section: Ethical Design and Use Of Ai In Healthcarementioning

confidence: 99%

Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate

Mohammad Amini,

Jesus,

Fanaei Sheikholeslami

et al. 2023

MAKE

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This study examines the ethical issues surrounding the use of Artificial Intelligence (AI) in healthcare, specifically nursing, under the European General Data Protection Regulation (GDPR). The analysis delves into how GDPR applies to healthcare AI projects, encompassing data collection and decision-making stages, to reveal the ethical implications at each step. A comprehensive review of the literature categorizes research investigations into three main categories: Ethical Considerations in AI; Practical Challenges and Solutions in AI Integration; and Legal and Policy Implications in AI. The analysis uncovers a significant research deficit in this field, with a particular focus on data owner rights and AI ethics within GDPR compliance. To address this gap, the study proposes new case studies that emphasize the importance of comprehending data owner rights and establishing ethical norms for AI use in medical applications, especially in nursing. This review makes a valuable contribution to the AI ethics debate and assists nursing and healthcare professionals in developing ethical AI practices. The insights provided help stakeholders navigate the intricate terrain of data protection, ethical considerations, and regulatory compliance in AI-driven healthcare. Lastly, the study introduces a case study of a real AI health-tech project named SENSOMATT, spotlighting GDPR and privacy issues.

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“…To date, documented cases of AI-related bias in mental health applications are less common than in health care generally. But, as AI-based mental health models are increasingly built and deployed, instances of bias in the mental health domain will likely increase (Rubeis, 2022). Importantly, the conceptualization and expression of emotion and mental health vary considerably across gender, race, ethnicity, and culture (Hareli et al, 2015; Office of the Surgeon General, 2001).…”

Section: An Emerging Field Of Research In Engineering: Fair-aware Aimentioning

confidence: 99%

A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health

Timmons

Duong

Fiallo

et al. 2022

Perspect Psychol Sci

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Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.

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iHealth: The ethics of artificial intelligence and big data in mental healthcare

Cited by 41 publications

References 53 publications

A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application

A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application

Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate

A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health

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