Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening

Abstract

IMPORTANCEThe 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 PARTICIPANTSA 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 RELEVANCEIn 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.

Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research

“…Consistent with previous studies of public perspectives about health AI [16,[23][24][25], participants' support for health AI scenarios was linked to their perceived public benefit of the scenarios, with people being most supportive when they believed that AI could bring an important new capability to a problem beyond what humans could contribute. Each of three health AI research scenarios were viewed as being acceptable by most of the participants of the focus groups (Table 3).…”

“…Most published studies have focused on the views of patients who may not be representative because they stand to benefit from AI applications [16]. Further, most published studies have focused on computer vision health AI applications in radiology and dermatology, which represent only a small fraction of the potential applications of AI in health [23][24][25]. Additionally, there is a need to understand public perspectives versus patient perspectives, because health AI research may rely on large datasets that include information about people who do not have health conditions and/or do not stand to benefit directly from the research.…”

Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research

“…16 Further, most published studies have focused on computer vision health AI applications in radiology and dermatology, which represent only a small fraction of the potential applications of AI in health. [23][24][25] Additionally, there is a need to understand public perspectives versus patient perspectives, because health AI research may rely on large datasets that include information about people who do not have health conditions and/or do not stand to benefit directly from the research. Accordingly, the objective of this study was to learn more about how members of the general public perceive health data being used for AI research.…”

Assessment of the change in accuracy of an artificial intelligence algorithm for the detection of skin cancer in camera images following diversification and training

“…"(26) A recent small study has shown that dermatology patients are receptive to AI based diagnostic methods for detection of skin cancer within the context of a human physician-patient relationship. (27) This suggests that the reluctance to adopt AI based diagnostic aids is not related to negative patient attitudes. Lindsell et al go on to state that "Designing a useful AI tool in health care should begin with asking what system change the AI tool is expected to precipitate.…”