JMIR Publications

ABSTRACT

Background:

The rapidly progressing developments in artificial intelligence (AI) will enable its widespread use in radiological diagnostics in the near future. Patients will then be confronted with findings generated with the help of AI. Understanding patients' perspectives on the use of this technology is one of the key factors for its successful implementation.

Objective:

The objective of this qualitative study was to gain insight into patients' reasoning about opportunities and risks of using AI in radiological diagnostics, to identify prerequisites for its acceptance, and to identify aspects that can promote trust in AI diagnosis, especially in scenarios of high personal concern.

Methods:

A total of seven focus groups were carried out with 34 patients (44% female) with an age between 23 and 85 years (mean 49.06, SD 17.08) recruited using purposive sampling strategies. Each focus group was audiotaped, transcribed, and analyzed using the method of structured qualitative content analysis.

Results:

Study findings show that patients are open to the use of AI in radiological diagnostics. The basic prerequisites for this are a) scientific evidence of safe outcomes which are more accurate and faster than those without AI, b) recognizable added value in patient care, c) transparency in the use of AI and disclosure to the patient, d) comprehensive, binding measures for quality assurance, and e) the use of AI solely to support the physician. However, the results indicate that further criteria are important for patients to be willing to choose a radiologist who uses AI and to trust AI diagnosis. In situations where they are personally affected, patients fear that doctors will place too much trust in the AI result and that the doctor-patient relationship will become dehumanized. Therefore, the physician's abilities and functions that inspire trust from the patient's perspective must come into play. These include a) an independent diagnosis by the physician that takes into account the clinical context but also the individuality of the patient, b) a comprehensible explanation of the pros and cons of using AI for patients and clear communication of AI output, c) a humane and empathetic physician-patient relationship that shows that the physician continues to feel responsible for the patient.

Conclusions:

The results of the study underscore that a high quality of the entire "socio-technical" system is an essential prerequisite for patient acceptance of the use of AI in radiological diagnostics and for trust in AI diagnosis. The further development of AI performance must go hand in hand with the creation of framework conditions for its use that meet patients' expectations of the role of the physician and ensure a trust-building physician-patient relationship. The study provides valuable insights into how such integration of AI into radiological practice can be achieved.