Yale School of Medicine, Department of Dermatology, New Haven, Connecticut.
Harvard Medical School, Department of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts.
JAMA Dermatol. 2020 May 1;156(5):501-512. doi: 10.1001/jamadermatol.2019.5014.
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.
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.
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.
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.
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.
人工智能(AI)在医学领域的应用正在不断扩大。在皮肤病学领域,研究人员正在评估直接面向患者和临床医生的决策支持 AI 工具在分类皮肤病变方面的潜力。尽管人工智能有望改变患者参与医疗保健的方式,但患者的观点仍未得到充分理解。
探讨患者如何理解 AI 并感知 AI 在皮肤癌筛查中的应用。
设计、设置和参与者:在布莱根妇女医院的普通皮肤科诊所和丹娜-法伯癌症研究所的黑色素瘤诊所进行了一项使用扎根理论方法对半结构化访谈分析的定性研究。共纳入 48 名患者。每位患者的访谈均由 2 位研究人员独立进行编码,并进行了研究者间可靠性测量;对协调后的编码进行了评估,以确定编码的频率。该研究于 2019 年 5 月 6 日至 7 月 8 日进行。
AI 概念、AI 的预期收益和风险、AI 的优缺点、AI 的实施、人类与 AI 临床决策之间冲突的应对以及对 AI 的支持或反对。
在纳入的 48 名患者中,26 名参与者(54%)为女性;平均(SD)年龄为 53.3(21.7)岁。16 名患者(33%)有黑色素瘤病史,16 名患者(33%)有非黑色素瘤皮肤癌病史,16 名患者(33%)无皮肤癌病史。24 名患者接受了关于直接面向患者的 AI 工具的访谈,24 名患者接受了关于临床医生决策支持 AI 工具的访谈。2 个编码团队的研究者间可靠性评分分别为κ=0.94 和 κ=0.89。患者主要根据认知来理解 AI。提高诊断速度(29 名参与者[60%])和获得医疗保健(29 名参与者[60%])被认为是 AI 用于皮肤癌筛查的最常见预期收益;增加患者焦虑是最常见的风险(19 名参与者[40%])。患者认为更准确的诊断(33 名参与者[69%])和不太准确的诊断(41 名参与者[85%])分别是 AI 的最大优势和劣势。一个突出的主题是人类与 AI 共生的重要性(45 名参与者[94%])。在人类与 AI 临床决策之间发生冲突时,最常见的反应是寻求活检(32 名参与者[67%])。总的来说,36 名参与者(75%)会向家人和朋友推荐 AI。
在这项定性研究中,如果以保护医患关系完整性的方式实施,患者似乎愿意接受 AI 用于皮肤癌筛查。