About

Jodi Halpern, MD, PhD, holds a University Chair and is a Professor of Bioethics and Medical Humanities at the School of Public Health at UC Berkeley. Her work integrates psychiatry, philosophy, affective forecasting, and decision neuroscience to explore how individuals imagine and influence their own and others' future health possibilities. She is the author of the book 'From Detached Concern to Empathy: Humanizing Medical Practice,' which was recognized as a seminal work by the Journal of the American Medical Association. Her upcoming book, 'Remaking the Self in the Wake of Loss,' examines the role of empathy in post-traumatic growth. Dr. Halpern's research focuses on innovative technologies and ethics, and she co-founded and co-directs the Kavli Center for Ethics, Science and the Public, leading the Berkeley Group for the Ethics and Regulation of Innovative Technology. Her current projects include 'Engineering Empathy,' which investigates how AI-simulated empathy is transforming personal and therapeutic relationships, and policy initiatives to create guardrails protecting minors from risks associated with chatbots. She has advised legislative bodies, such as the California Senate, and has been featured in numerous media outlets. Recognized worldwide for her scholarship on empathic curiosity and societal divides, she received the 2024 Inaugural Lifetime Achievement Award in Empathy and Healthcare from The International Network for Empathy in Healthcare.

Research topics

• Computer Science
• Medicine
• Psychology
• Artificial Intelligence
• Political Science
• Psychiatry
• Social psychology
• Virology
• Engineering
• Human–computer interaction
• Nursing
• Management science
• Engineering ethics
• Risk analysis (engineering)
• Gerontology

Selected publications

• Novel approach to teaching empathic leadership using heuristics

  BMJ Leader · 2026-04-13

  articleOpen access

  INTRODUCTION: National Health Service (NHS) failures at Mid-Staffordshire, Shrewsbury and Telford, and East Kent reached the same conclusion: leaders who fail to listen and understand put patients at risk. The NHS response has been to promote compassionate leadership. Yet, compassion often entails emotional merging, which can blur boundaries and lead to fatigue. By contrast, empathic leadership and curiosity-driven perspective-taking, rather than emotional merging, offers a promising new way forward. Yet no practical method exists to teach empathic leadership. To fill this gap, we aimed to develop heuristics ('rules of thumb') for empathic leadership and create a course to teach them. METHODS: 21 healthcare leaders attended a structured workshop. Using established heuristic development and curriculum design methods, participants generated, refined and prioritised heuristics for empathic leadership, then co-designed a training course to teach them. RESULTS: The group produced 35 heuristics and prioritised 12, including 'Listen first, speak last', 'Say sorry', and 'Walk the shop floor'. A one-day interprofessional empathic leadership course was then co-designed, featuring experiential learning, role play and implementation planning. CONCLUSION: We identified and prioritized heuristics for empathic leadership and produced a course to teach them. The short course may support healthcare leaders to strengthen empathic leadership in practice.

  PublisherOA PDFDOI

• The Future of New Technology and Artificial Intelligence in Mental Health: Prospects, Challenges, and a Roadmap for Personalized Treatment

  2026-04-30

  articleOpen access

  Background. Artificial intelligence (AI) and related digital technologies are rapidly entering mental health care, psychosomatic medicine, and psychotherapy. Yet the clinical value of these tools cannot be judged by technical performance alone. The field is characterized by diagnostic heterogeneity, reliance on language and self-report, complex biopsychosocial causation, and substantial variability in response to interventions. Clinically meaningful progress therefore requires integration of biological markers with patients’ lived experience, personal history and context, alongside person-centered precision decision-making and intervention.Summary. This review examines how AI may contribute to mental health care across multiple levels, including diagnostic assessment, biomarker development, treatment selection, AI-enabled non-invasive neuromodulation, closed-loop adaptation, and emerging chatbot-based interventions. We argue that AI should support and improve, not replace, clinical assessments and care by integrating complex data into person-centered care, while raising challenges of validation, safety, equity, commodification and governance.Key Messages. AI can strengthen and improve clinically effective, context-sensitive, and person-centered mental health care. AI may enhance diagnosis, treatment selection, and therapeutic options while preserving the human-human therapeutic alliance. The future of AI-enabled mental health depends on combining computational precision with human judgment, ethical oversight, and clinical relevance to improve patient outcomes and quality of life. Keywords: Artificial intelligence; Mental health; Personalized treatment; Psychotherapy; Psychosomatic medicine

  PublisherDOI

• Use of mobile health technology in the assessment of statin-associated muscle symptoms: A pilot feasibility study

  Journal of clinical lipidology · 2026-01-13

  articleOpen access

  BACKGROUND: Statins are the cornerstone pharmacotherapy for lowering low-density lipoprotein cholesterol (LDL-C) and reducing risk of atherosclerotic cardiovascular disease (ASCVD). However, statin initiation and adherence are limited by statin-associated muscle symptoms (SAMS). Mobile health (mHealth) tools offer novel ways to assess real-time symptom data and facilitate shared decision-making. OBJECTIVE: To assess the feasibility and usability of an automated, text-message-based SAMS symptom-tracking platform to longitudinally track SAMS in patients who previously experienced muscle symptoms while on statin therapy. METHODS: We enrolled 19 patients and recorded baseline demographics, statin history, and technology use via an online survey. Quantitative SAMS scores were gathered via daily text messages, and qualitative symptom data were collected in weekly text messages. Upon study completion, participants reported study perceptions via an online exit survey. RESULTS: A total of 18 patients collected data, and 15 patients completed the 90-day study protocol. The response rate to text messages was 91.5% (92.2% for daily SAMS messages, 86.0% for weekly muscle symptom prompts). Overall reported statin adherence was 70%. Nine patients reported intolerable muscle symptoms at least once during the study, and intolerable symptom scores represented 3% of all reported symptom scores. Patients reported overall satisfaction with the study and found the SAMS scores helpful. CONCLUSIONS: Patients with previously reported SAMS successfully engaged with a text-message-based symptom-tracking platform with high interactivity and acceptability. This study demonstrates the feasibility and usability of an automated mHealth platform for longitudinally tracking SAMS in patients on statin therapy, and may provide a means for improving shared decision making to tailor statin therapy and improve adherence.

  PublisherDOI

• The Future of New Technology and Artificial Intelligence in Mental Health: Prospects, Challenges, and a Roadmap for Personalized Treatment

  PsyArXiv (OSF Preprints) · 2026-04-30

  preprintOpen access

  Background. Artificial intelligence (AI) and related digital technologies are rapidly entering mental health care, psychosomatic medicine, and psychotherapy. Yet the clinical value of these tools cannot be judged by technical performance alone. The field is characterized by diagnostic heterogeneity, reliance on language and self-report, complex biopsychosocial causation, and substantial variability in response to interventions. Clinically meaningful progress therefore requires integration of biological markers with patients’ lived experience, personal history and context, alongside person-centered precision decision-making and intervention. Summary. This review examines how AI may contribute to mental health care across multiple levels, including diagnostic assessment, biomarker development, treatment selection, AI-enabled non-invasive neuromodulation, closed-loop adaptation, and emerging chatbot-based interventions. We argue that AI should support and improve, not replace, clinical assessments and care by integrating complex data into person-centered care, while raising challenges of validation, safety, equity, commodification and governance. Key Messages. AI can strengthen and improve clinically effective, context-sensitive, and person-centered mental health care. AI may enhance diagnosis, treatment selection, and therapeutic options while preserving the human-human therapeutic alliance. The future of AI-enabled mental health depends on combining computational precision with human judgment, ethical oversight, and clinical relevance to improve patient outcomes and quality of life. Keywords: Artificial intelligence; Mental health; Personalized treatment; Psychotherapy; Psychosomatic medicine

  Publisher

• Proposed model of empathic leadership to address crises in contemporary management

  BMJ Leader · 2025-08-01

  article
  PublisherDOI

• Participant Evaluation of Blockchain-Enhanced Women’s Health Research Apps: Mixed Methods Experimental Study

  JMIR mhealth and uhealth · 2025-03-25 · 1 citations

  articleOpen access

  BACKGROUND: Blockchain technology has capabilities that can transform how sensitive personal health data are safeguarded, shared, and accessed in digital health research. Women's health data are considered especially sensitive, given the privacy and safety risks associated with their unauthorized disclosure. These risks may affect research participation. Using a privacy-by-design approach, we developed 2 app-based women's health research study prototypes for user evaluation and assessed how blockchain may impact participation. OBJECTIVE: This study aims to seek the perspectives of women to understand whether applications of blockchain technology in app-based digital research would affect their decision to participate and contribute sensitive personal health data. METHODS: A convergent, mixed methods, experimental design was used to evaluate participant perceptions and attitudes toward using 2 app-based women's health research study prototypes with blockchain features. Prototype A was based on the status quo ResearchKit framework and had extensive electronic informed consent, while prototype B minimized study onboarding requirements and had no informed consent; the mechanisms of how the contributed data flowed and were made pseudonymous were the same. User evaluations were carried out in February and March 2021 and consisted of a think-aloud protocol, a perception survey, and a semistructured interview. Findings were mapped to the technology acceptance model to guide interpretation. RESULTS: We recruited 16 representative female participants from 175 respondents. User evaluations revealed that while participants considered prototype B easier to use on intuitive navigation (theme 1) of specified tasks and comprehension (theme 2) of research procedures, prototype A trended toward being perceived more favorably than prototype B across most perception survey constructs, with an overall lower level of privacy concern (mean [SD]: 2.22 [1.10] vs 2.95 [1.29]) and perceived privacy risk (2.92 [1.46] vs 3.64 [1.73]) and higher level of perceived privacy (5.21 [1.26] vs 4.79 [1.47]), trust (5.46 [1.19] vs 4.76 [1.27]), and usability (67.81 [21.77] vs 64.84 [23.69]). Prototype B was perceived more favorably than prototype A with perceived control (4.92 [1.32] vs 4.89 [1.29]) and perceived ownership (5.18 [0.59] vs 5.01 [0.96]). These constructs, except for perceived ownership, were significantly correlated with behavioral intention to use the app (P<.05). Participants perceived the usefulness of these prototypes in relation to the value of research study to women's health field (theme 3), the value of research study to self (theme 4), and the value of blockchain features for participation (theme 5). CONCLUSIONS: This study provides nuanced insights into how blockchain applications in app-based research remain secondary in value to participants' expectations of health research, and hence their intention to participate and contribute data. However, with impending data privacy and security concerns, it remains prudent to understand how to best integrate blockchain technology in digital health research infrastructure.

  PublisherOA PDFDOI

• A Proposed Model of Empathic Leadership to Address Crises in Contemporary Management&amp;nbsp;

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• The Leicester empathy declaration: A model for implementing empathy in healthcare

  Patient Education and Counseling · 2024-08-10 · 2 citations

  letterOpen accessSenior author
  PublisherOA PDFDOI

• A Price Tag on Clinical Empathy? Factors Influencing Its Cost-Effectiveness

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• Does it matter if empathic AI has no empathy?

  Nature Machine Intelligence · 2024-05-15 · 23 citations

  article
  PublisherDOI

Frequent coauthors

• Elena Portacolone

  Institute on Aging

  35 shared

• Julene K. Johnson

  24 shared

• Kenneth E. Covinsky

  University of California, San Francisco

  19 shared

• Aleksa Owen

  University of Nevada, Reno

  7 shared

• Krista L. Harrison

  7 shared

• Ndola Prata

  Ocean Acoustical Services and Instrumentation Systems (United States)

  7 shared

• Sharon E. O’Hara

  St. James's Hospital

  6 shared

• Robert Rubinstein

  Langley Research Center

  6 shared

Awards & honors

• Greenwall Faculty Scholars Fellowship
• Rockefeller Fellowship at Princeton
• 2022 Guggenheim Award in Health and Medicine
• Inaugural Lifetime Achievement Award in Empathy and Healthca…