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

doi:10.5826/dpc.1602a6758

Patient Perspectives On The Use Of Artificial Intelligence In Malignant Melanoma Diagnosis

Authors

Kristian Lykke Lorentzen Department of Dermatology and Venereology, Odense University Hospital, Denmark
Karoline Løvschall Mikkelsen Faculty of Medicine, Department of Health Science and Technology, Aalborg University, Denmark
Louise Steenberg Boe Faculty of Medicine, Department of Health Science and Technology, Aalborg University, Denmark
Gitte Susanne Rasmussen Department of Dermatology and Venereology, Aarhus University Hospital, Denmark
Erika Geraldina Spaich Faculty of Medicine, Department of Health Science and Technology, Aalborg University, Denmark
Kenneth Græbild Thomsen Department of Dermatology and Venereology, Aarhus University Hospital, Denmark

Keywords:

Artificial intelligence, Malignant Melanoma, dermatology, Patient perspectives, skin cancer

Abstract

Introduction: Early 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.

Objectives: The 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.

Methods: An 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.

Results: Participants 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.

Conclusions: Participants 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.

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