Risks and benefits of dermatological machine learning health care applications-an overview and ethical analysis

Visual data are particularly amenable for machine learning techniques. With clinical photography established for skin surveillance and documentation purposes as well as progress checks, dermatology is an ideal field for the development and application of emerging machine learning health care applications (ML-HCAs). To date, several ML-HCAs have detected malignant skin lesions on par with experts or found overlooked visual patterns that correlate with certain dermatological diseases. However, it is well established that ML-HCAs come with ethical and social implications.

Currently, there is a lack of research that establishes model design, training, usage and regulation of such technologies sufficient to ensure ethically and socially responsible development and clinical translation, specifically within the field of dermatology. With this paper, we aim to give an overview of currently discussed ethical issues relating to dermatological ML-HCAs.

On the basis of a thematic, keyword-based literature search, we performed an ethical analysis against established frameworks of biomedical ethics. We combined our results with current, relevant normative machine learning ethics literature to identify the status quo of the ethics of ML-HCAs in dermatology. We describe the benefits and risks of dermatological ML-HCAs that are currently being developed for clinical purposes.

The potential benefits range from better patient outcomes to better knowledge accessibility to decreasing health care disparities, that is, standards of care between different population groups. The risks associated with ML-HCAs range from confidentiality issues to individual patient outcomes as well as the exacerbation of prevalent health care disparities. We discuss the practical implications for all stages of dermatological ML-HCA development.

We found that ML-HCAs present stakeholder-specific risks for patients, health care professionals and society, which need to be considered separately. The discipline lacks sufficient biomedical ethics research that could standardize the approach to ML-HCA model design, training, use and regulation of such technologies.