Understanding the Acceptability of Artificial Intelligence as a Support for Healthcare Providers in the Diagnosis of Prostate Cancer - the Patient at the Heart of His Care

1. Introduction 1.1. Acceptability of artificial intelligence 1.1.1. Artificial intelligence in healthcare Healthcare professionals are facing a multitude of growing and more and more complex challenges. The nature of health problems is changing, workloads are increasing, which leads to a more difficult management in care and requires also a constant update of knowledge. At the same time, the implementation of artificial intelligence (AI) tools in healthcare is exploding and offers a bright opportunity to meet the needs of the evolving medical sector.

Many studies have explored the feasibility of clinical AI by evaluating, among other things, the technical performance of AI systems. However, the technical nature of AI development is not the only challenge facing the implementation of clinical AI. Indeed, studies have shown that the acceptability of clinical AI by healthcare professionals and patients impacts the effective adoption of AI by these users. Ignoring this parameter could lead to a waste of resources, by not taking advantage of the available AI systems.

The acceptability of clinical AI by patients and healthcare professionals has been studied in different contexts but most studies concern the acceptability of AI in healthcare in general. This subject has already been systematically summarised and shows in majority good acceptability of AI. On the other hand, studies in specific health contexts are rarer and the AI acceptability in these contexts cannot be defined by simply extrapolating the AI acceptability in the healthcare. Indeed, the acceptability of AI differs according to the context (disease diagnosed, severity of the disease, consequences of the AI's decision, complexity of decision-making) but also to the tasks that the AI performs (diagnosis, choice of treatment, prognosis). In addition, AI acceptability factors differ depending on the population studied (patients, healthcare professionals, researchers and healthcare managers) due to differences in needs, preferences and the context of use. It is therefore important to study the AI acceptability into the precise context to which the study refers.

1.1.2. Acceptability of AI in the prostate cancer diagnosis process The implementation of AI tools in the prostate cancer diagnosis (PCa) process will improve information from medical images and creation of predictive models. This will represent a significant advance in optimizing diagnosis of PCa and predicting its aggressiveness, with the final objective of personalizing treatment by adaptation to the biological characteristics of the tumour. This will help reduce the need for prostate biopsies, thus increasing the individuals' compliance and encouraging them to get tested before the appearance of symptoms. Moreover, early detection of PCa dramatically improves the treatment success rate.

Given the opportunity of the implementation of AI in the PCa diagnosis process, it is essential to study the acceptability of this implementation for patients and healthcare professionals who would the final users of AI in the PCa context, and the conditions for this implementation. Although some studies have examined this acceptability, research in this area remains limited and requires further investigation.