Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening: A Qualitative Study.
Adult
Aged
Artificial Intelligence
Biopsy
Early Detection of Cancer
/ methods
Female
Grounded Theory
Health Services Accessibility
Humans
Interviews as Topic
Male
Mass Screening
/ methods
Melanoma
/ diagnosis
Middle Aged
Observer Variation
Patient Acceptance of Health Care
Physician-Patient Relations
Qualitative Research
Reproducibility of Results
Skin Neoplasms
/ diagnosis
Journal
JAMA dermatology
ISSN: 2168-6084
Titre abrégé: JAMA Dermatol
Pays: United States
ID NLM: 101589530
Informations de publication
Date de publication:
01 05 2020
01 05 2020
Historique:
pubmed:
12
3
2020
medline:
30
12
2020
entrez:
12
3
2020
Statut:
ppublish
Résumé
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. 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.
Identifiants
pubmed: 32159733
pii: 2762711
doi: 10.1001/jamadermatol.2019.5014
pmc: PMC7066525
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
501-512Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : NIAMS NIH HHS
ID : T32 AR007465
Pays : United States
Commentaires et corrections
Type : CommentIn
Références
Br Dent J. 2008 Mar 22;204(6):291-5
pubmed: 18356873
JAMA Dermatol. 2018 Dec 1;154(12):1383-1384
pubmed: 30140854
NPJ Digit Med. 2019 Jun 14;2:53
pubmed: 31304399
Nature. 2017 Jun 28;546(7660):686
pubmed: 28658222
JAMA Dermatol. 2018 Nov 1;154(11):1247-1248
pubmed: 30073260
Int J Dermatol. 2018 Aug;57(8):1015-1016
pubmed: 29873395
J Invest Dermatol. 2018 Jul;138(7):1529-1538
pubmed: 29428356
Int J Qual Health Care. 2007 Dec;19(6):349-57
pubmed: 17872937
J Adv Nurs. 2016 Dec;72(12):2954-2965
pubmed: 27221824
Ann Oncol. 2018 Aug 1;29(8):1836-1842
pubmed: 29846502
J Am Coll Radiol. 2018 Mar;15(3 Pt B):580-586
pubmed: 29402532
J Am Acad Dermatol. 2019 Oct;81(4):998-1000
pubmed: 31247221
Fam Med. 2005 May;37(5):360-3
pubmed: 15883903
JAMA Dermatol. 2019 Aug 01;155(8):914-921
pubmed: 31090868
Nat Med. 2019 Jan;25(1):44-56
pubmed: 30617339
Psychol Health. 2019 Jul;34(7):828-849
pubmed: 30822146