Evidence map›Paper›PMID 42190225›Full record

ArticleDermatology practical & conceptual2026

Patient Perspectives on the Use of Artificial Intelligence in Malignant Melanoma Diagnosis.

Kristian Lykke Lorentzen, Karoline Løvschall Mikkelsen, Louise Steenberg Boe, Gitte Susanne Rasmussen, Erika Geraldina Spaich, Kenneth Græbild Thomsen

Abstract read
In one paragraph

Article in Dermatology practical & conceptual, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Kristian Lykke LorentzenDepartment of Dermatology and Venereology, Odense University HospitalDenmark.
Karoline Løvschall MikkelsenFaculty of Medicine, Department of Health Science and Technology, Aalborg UniversityDenmark.
Louise Steenberg BoeFaculty of Medicine, Department of Health Science and Technology, Aalborg UniversityDenmark.
Gitte Susanne RasmussenDepartment of Dermatology and Venereology, Aarhus University HospitalDenmark.
Erika Geraldina SpaichFaculty of Medicine, Department of Health Science and Technology, Aalborg UniversityDenmark.
Kenneth Græbild ThomsenDepartment of Dermatology and Venereology, Aarhus University HospitalDenmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEarly diagnosis of malignant melanoma (MM) is challenged by an increased incidence and a shortage of dermatologists. This has raised expectations on the use of artificial intelligence (AI) for diagnosing MM. Implementation of AI-based diagnostic tools rely on diagnostic performance as well as on patient trust and acceptance. Therefore, understanding patients' perspectives is essential when developing trustworthy AI-based diagnostic tools.

objectivesThe aim of this study was to investigate perspectives and expectations of AI-based computer-aided diagnosis (AI-CAD) for MM among individuals at high risk of developing MM.

methodsAn inductive qualitative study using an interpretive description methodology was carried out. Ten semi-structured interviews incorporating vignettes to investigate expectations regarding trust in AI, physician-patient interaction, diagnostic efficiency, and data security were conducted.

resultsParticipants preferred AI being used as decision support for physicians. The interaction with a physician (human-in-the-loop) was deemed key to the experience of a proper diagnostic process, and participants recognized AI's potential to streamline the process.

conclusionsParticipants trust AI and recognize the potential of AI-CAD in the detection of MM. However, they emphasized the importance of human interaction as essential to a positive diagnostic experience. As trust is fundamental to safe implementation of new technologies in clinical practice, we recommend conducting post-market prospective studies employing a mixed methods approach. This will help ensure both patients' trust in the technology and physicians' confidence in its clinical utility.

Identifiers

PMID42190225
PMCPMC13211903

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