Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method

Abstract

Background Advanced analytics, such as artificial intelligence (AI), increasingly gain relevance in medicine. However, patients’ responses to the involvement of AI in the care process remains largely unclear. The study aims to explore whether individuals were more likely to follow a recommendation when a physician used AI in the diagnostic process considering a highly (vs. less) severe disease compared to when the physician did not use AI or when AI fully replaced the physician. Methods Participants from the USA (n = 452) were randomly assigned to a hypothetical scenario where they imagined that they received a treatment recommendation after a skin cancer diagnosis (high vs. low severity) from a physician, a physician using AI, or an automated AI tool. They then indicated their intention to follow the recommendation. Regression analyses were used to test hypotheses. Beta coefficients (ß) describe the nature and strength of relationships between predictors and outcome variables; confidence intervals [CI] excluding zero indicate significant mediation effects. Results The total effects reveal the inferiority of automated AI (ß = .47, p = .001 vs. physician; ß = .49, p = .001 vs. physician using AI). Two pathways increase intention to follow the recommendation. When a physician performs the assessment (vs. automated AI), the perception that the physician is real and present (a concept called social presence) is high, which increases intention to follow the recommendation (ß = .22, 95% CI [.09; 0.39]). When AI performs the assessment (vs. physician only), perceived innovativeness of the method is high, which increases intention to follow the recommendation (ß = .15, 95% CI [− .28; − .04]). When physicians use AI, social presence does not decrease and perceived innovativeness increases. Conclusion Pairing AI with a physician in medical diagnosis and treatment in a hypothetical scenario using topical therapy and oral medication as treatment recommendations leads to a higher intention to follow the recommendation than AI on its own. The findings might help develop practice guidelines for cases where AI involvement benefits outweigh risks, such as using AI in pathology and radiology, to enable augmented human intelligence and inform physicians about diagnoses and treatments.

Artificial intelligence in (gastrointestinal) healthcare: patients’ and physicians’ perspectives

“…Human interaction in addition to AI use was considered critical for the experience of high-quality care 22 . The importance of human interactions is further supported by evidence showing that patients' compliance was higher for physicians and for physicians using AI compared to an AI-system alone 8 . This so called augmented intelligence emphasizes that AI enhances or assists human intelligence rather than replacing it, expressing the importance of symbiosis between humans and AI 16,23,24 .…”

“…Examples within gastrointestinal (GI) endoscopy include detection and classification of colorectal lesions 3 , differentiation between superficial and deep invasive colorectal cancer 4 , disease severity scoring of inflammatory bowel diseases 5 , localizing blind spots during esophagogastroduodenoscopy 6 , and detecting Barrett's neoplasia 7 . Some of these AI-systems diagnose diseases with expert-level accuracy or even outperform human experts [7][8][9] .AI-based systems can also be used in personalized healthcare 10 . Labovitz et al (2017) showed that AI is helpful in improving compliance to therapy 11 .…”

The medical profession transformed by artificial intelligence: Qualitative study

“…The concept of social presence can explain this result. 51 Experts believe that technological singularity—a future point in time at which computers will exhibit superhuman intelligence—cannot be achieved without empathy in healthcare, leading to the development of concepts like artificial empathy or empathy-driven digital tools. 47,52,53 Personalized healthcare is only possible if it is responsive to patient needs, with an emphasis on the physician's perception of (past) medical history and physical examination.…”

The Development of a Proof-of-Concept Physician-Driven Informatics Consult System for the Individualized Treatment of Patients with Orthopaedic Conditions

“…(50) Furthermore, there is hesitancy among patients and physicians to use computer-based recommender systems that lack the art of human element of medicine and intend to base clinical decisions on computer algorithms. (51,52) Physicians possess accumulated clinical wisdom on the patient characteristics that are relevant when making treatment decisions. (19,53,54) Because of this unique knowledge, physicians are better positioned to phenotype or de ne reference classes of patients.…”

“…(7,13,21,(23)(24)(25) Physicians possess accumulated clinical wisdom on the patient characteristics that are relevant when making treatment decisions. 19,51,52 Because of this unique knowledge, we believe physicians are best positioned to de ne reference classes of similar patients. ICSCEDIT takes a physician-driven approach to refence class selection.…”