Patients' Attitudes Towards the Use of AI-Based Decision Aids for Breast Cancer Treatment Decision-Making: A Qualitative Study

Abstract Objectives: While AI has the potential to transform cancer care, there has been limited progress in incorporating AI tools into clinical practice. As healthcare providers work towards enhancing patient satisfaction and care quality, understanding patients' attitudes towards AI is crucial to facilitate the adoption of these tools in clinical settings. Despite this, few studies have explored patients' views on AI-based decision aids. The aim of this research is to explore the perceptions of cancer patients towards the use of AI-powered decision aids in medical decision-making. Methods: To explore the patient perspective on AI-based decision aids, the study conducted 12 semi-structured interviews with former breast cancer patients recruited through the Dutch Breast Cancer Association (BVN). The interviews covered a range of topics such as treatment recommendations, side effect prediction, survival, and recurrence. After transcription, the interviews were analyzed using thematic analysis to identify recurring themes and relevant quotes associated with each theme. The study analyzed the patients' responses in three primary domains: their familiarity with AI, the use of AI in various scenarios related to outcomes, and a comparison of AI and MD. Results: Patients' familiarity with AI was found to vary depending on their demographics, with younger and highly educated patients demonstrating a better understanding of AI. Generally, patients had a positive attitude towards AI when used for less critical scenarios such as side effects and treatment recommendations. However, when it came to more severe cases like the prediction of survival and recurrence after treatment, patients were hesitant to trust AI. The participants identified trust as a crucial factor affecting their willingness to use AI, with most of them being positive towards using AI only if they had the chance to consult with an MD. Despite the recognition of the human nature of MDs and their potential to make errors, patients still trusted them more than AI. Participants’ reluctance to accept AI was also partly attributed to the belief that AI cannot consider individuals' unique circumstances, making it more suitable for the average population. Moreover, lack of health literacy and digital skills, as well as ambiguity about accountability in case of errors, were identified as barriers to the adoption of AI in healthcare. Conclusion: This qualitative study sheds light on the perceptions of former breast cancer patients in the Netherlands regarding the use of AI in medical decision-making. The findings suggest that patients are generally open to the idea of utilizing AI-based programs to aid in decision-making, but have reservations about using them in high-stakes situations like survival and recurrence predictions. To address these concerns, the study highlights the significance of increasing awareness and understanding of AI's potential in personalized medicine, and creating educational resources for various health areas. Collaboration between healthcare providers, systems, and AI developers is essential, as well as well-defined protocols for accountability and liability in cases of patient harm. Future research should aim to diversify the patient population and provide an accurate representation of the AI program's capabilities to prevent misinterpretation.