Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening: A Qualitative Study
Open accessAbstract
Importance: The use of artificial intelligence (AI) is expanding throughout the field of medicine. In dermatology, researchers are evaluating the potential for direct-to-patient and clinician decision-support AI tools to classify skin lesions. Although AI is poised to change how patients engage in health care, patient perspectives remain poorly understood. Objective: To explore how patients conceptualize AI and perceive the use of AI for skin cancer screening. Design, Setting, and Participants: A qualitative study using a grounded theory approach to semistructured interview analysis was conducted in general dermatology clinics at the Brigham and Women's Hospital and melanoma clinics at the Dana-Farber Cancer Institute. Forty-eight patients were enrolled. Each interview was independently coded by 2 researchers with interrater reliability measurement; reconciled codes were used to assess code frequency. The study was conducted from May 6 to July 8, 2019. Main Outcomes and Measures: Artificial intelligence concept, perceived benefits and risks of AI, strengths and weaknesses of AI, AI implementation, response to conflict between human and AI clinical decision-making, and recommendation for or against AI. Results: Of 48 patients enrolled, 26 participants (54%) were women; mean (SD) age was 53.3 (21.7) years. Sixteen patients (33%) had a history of melanoma, 16 patients (33%) had a history of nonmelanoma skin cancer only, and 16 patients (33%) had no history of skin cancer. Twenty-four patients were interviewed about a direct-to-patient AI tool and 24 patients were interviewed about a clinician decision-support AI tool. Interrater reliability ratings for the 2 coding teams were κ = 0.94 and κ = 0.89. Patients primarily conceptualized AI in terms of cognition. Increased diagnostic speed (29 participants [60%]) and health care access (29 [60%]) were the most commonly perceived benefits of AI for skin cancer screening; increased patient anxiety was the most commonly perceived risk (19 [40%]). Patients perceived both more accurate diagnosis (33 [69%]) and less accurate diagnosis (41 [85%]) to be the greatest strength and weakness of AI, respectively. The dominant theme that emerged was the importance of symbiosis between humans and AI (45 [94%]). Seeking biopsy was the most common response to conflict between human and AI clinical decision-making (32 [67%]). Overall, 36 patients (75%) would recommend AI to family members and friends. Conclusions and Relevance: In this qualitative study, patients appeared to be receptive to the use of AI for skin cancer screening if implemented in a manner that preserves the integrity of the human physician-patient relationship.
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No research has yet explored what information patients consider important or significant for making treatment decisions when AI is used in a clinical setting.
“Although the value of empirical research on patients’ views regarding information disclosure in the use of AI/ML in medicine has been recognized, 12 no research has yet explored what information patients consider important or significant for making treatment decisions when AI is used in a clinical setting.26–28”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [27]
There is greater acceptance of diagnostic AI if it is implemented in a way that maintains the integrity of the healthcare professional-patient relationship.
“the greater acceptance of diagnostic AI if it is implemented in a way that maintains the integrity of that relationship (Nelson et al., 2020)”
From Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts (2022) · cites this paper as Nelson et al., 2020
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- Authors
- Nelson, C. A., Pérez-Chada, L. M., Creadore, A., Li, S. J., Lo, K., Manjaly, P., Pournamdari, A. B., Tkachenko, E., Barbieri, J. S., Ko, J. M., Menon, A. V., Hartman, R. I., & Mostaghimi, A.
- Affiliations
- Yale University (US)
- Harvard Medical School, Department of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts
- Boston University (US)
- Brigham and Women's Hospital (US)
- University of Pennsylvania (US)
- Stanford Medicine (US)
- Corresponding author
- Mostaghimi, Arash
- ORCID
- Venue
- JAMA Dermatology
- Publisher
- American Medical Association (AMA)
- Published
- 2020-05-01
- Volume
- 156
- Issue
- 5
- Pages
- 501
- ISSN
- 2168-6068 (ISSN-L)
- PMC
- PMC7066525
- OpenAlex
- W3012449740
- Cited by
- 278
- References
- 18
- Citations by year
- 2026: 44 · 2025: 57 · 2024: 55 · 2023: 45 · 2022: 37 · 2021: 32 · 2020: 8
- FWCI
- 8.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Dermatology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR001863)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (T32AR007465)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence, skin cancer screening, patient perspectives, melanoma, non-melanoma skin cancer, clinician decision support, human-AI collaboration, patient anxiety, diagnostic accuracy, patient acceptance of AI, health care access, dermatology clinic, patient-provider communication, shared decision-making
- MeSH terms
- Adult, Aged, Artificial Intelligence, Biopsy, Female, Health Services Accessibility, Humans, Interviews as Topic, Male, Mass Screening, Melanoma, Middle Aged, Patient Acceptance of Health Care, Physician-Patient Relations, Skin Neoplasms, Reproducibility of Results, Observer Variation, Qualitative Research, Early Detection of Cancer, Grounded Theory
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