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Updated: Jan 14, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
Published on: February 16, 2011
A qualitative study with patients, care-partners, clinicians, and bioethicists to identify ethical considerations of
John Y Rhee1,2, Paul Miller2, Zachary Tentor2
1Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Background:
The use of artificial intelligence (AI) in medicine has surged. Given the sensitive nature of palliative care, it is crucial to apply AI tools in a patient-centered and ethical manner.
Aim:
We sought to understand the ethical implications of implementing AI-based tools in palliative care from various stakeholders.
Design:
Using a framework of ethics of care, we conducted thematic analysis to identify emerging themes.
Setting/Participants:
We recruited and interviewed 22 participants: six patients, four care-partners, five clinicians, three bioethicists, and four clinician-bioethicists, recruited through a single hospital clinic for patients and care-partners and purposeful snowball sampling for clinicians and bioethicists based on their knowledge and interest in AI-based tools. The mean age of participants was 48 years old; 64% were male. Clinician participants had practiced for a mean of 11 years, and ethicists for 15 years. We aimed to recruit clinicians and bioethicists from multiple states and specialties.
Results:
From the interviews we extracted five main themes: (1) Primacy of the doctor-patient relationship over AI performance; (2) Humans have intuition and nuance that AI lacks; (3) Agreement about the importance of oversight of AI tools; (4) New AI technologies should include a process for patient education; and (5) AI increases efficiency, scalability, and a more unified approach to serious illness.
Conclusions:
When building and implementing AI-based tools, we recommend: establishing oversight committees; reflecting on the unique contributions of humans to care; proactively educating patients and contextualizing the tools; and ensuring data use is restricted to clinical care.
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