Patients’ perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data

Abstract

Results of radiology imaging studies are not typically comprehensible to patients. With the advances in artificial intelligence (AI) technology in recent years, it is expected that AI technology can aid patients’ understanding of radiology imaging data. The aim of this study is to understand patients’ perceptions and acceptance of using AI technology to interpret their radiology reports. We conducted semi-structured interviews with 13 participants to elicit reflections pertaining to the use of AI technology in radiology report interpretation. A thematic analysis approach was employed to analyze the interview data. Participants have a generally positive attitude toward using AI-based systems to comprehend their radiology reports. AI is perceived to be particularly useful in seeking actionable information, confirming the doctor’s opinions, and preparing for the consultation. However, we also found various concerns related to the use of AI in this context, such as cyber-security, accuracy, and lack of empathy. Our results highlight the necessity of providing AI explanations to promote people’s trust and acceptance of AI. Designers of patient-centered AI systems should employ user-centered design approaches to address patients’ concerns. Such systems should also be designed to promote trust and deliver concerning health results in an empathetic manner to optimize the user experience.

Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review

“…In all, 58% (15/26) of studies highlighted the potential of AI to improve the efficiency of health care service delivery in terms of remote monitoring [ 28 ], providing health-related reminders [ 23 , 28 ], increasing the speed and accuracy of health care processes (eg, consultation wait time, triaging, diagnosis, and managing medication refills) [ 26 , 29 , 30 , 35 - 37 , 44 ], facilitating care team communications, improving care accountability (eg, regular check-ins and follow-ups for information gathering) [ 23 ], and taking over repetitive manual tasks (eg, scheduling, patient education, and vital signs monitoring) [ 27 ]. Some respondents also appreciated the use of AI to provide a second opinion to physicians’ diagnoses or evaluations [ 42 , 46 ]. Overall, 12% (3/26) of studies [ 24 , 34 , 45 ] discussed the potential cost-saving capacity of AI that influences AI acceptability, whereas 4% (1/26) mentioned that the provision of an AI service using IBM Watson caused patients to incur higher treatment costs that did not translate to profits for the hospital after factoring onboarding of the technology [ 40 ].…”

“…Of the 26 studies, 6 (23%) studies discussed the participants’ lack of trust in the maturity of AI technology in providing reliable and accurate information to support health-related predictions and recommendations [ 24 , 26 , 35 , 38 , 40 , 46 ]. This could be related to concerns over the lack of integration and synthesis of information from various sources, standardization of data collection, and the overall sustainability of AI-assisted health care service delivery [ 40 , 45 ].…”

“…However, 8% (2/26) of studies reported that respondents had similar trust in AI as compared with a human physician’s diagnoses [ 28 , 45 ]. Possible mitigation strategies include increasing system transparency and reporting system accuracies [ 26 , 46 ]. Only 8% (2/26) of studies represented the voices of health care and IT staff [ 35 , 40 , 49 ] ( Multimedia Appendix 3 ).…”

“…Age, IT skills, preference for talking to computers, perceived utility, positive attitude, and perceived trustworthiness were found to be associated with AI acceptability [25,26]. Australia and New Zealand [35] 4 (15) Canada [27,[36][37][38] 6 (23) China [22,32,33,39,40] 1 (4) France [41] 2 (8) India [24,42] 1 (4) Korea [48] 1 (4) Saudi Arabia [29] 1 (4) Switzerland [30] 5 (19) United Kingdom [23,26,31,43,44] 1 (4) United Kingdom, Cyprus, Australia, the Netherlands, Sweden, Spain, United States, and Canada [28] 3 (12) United States [25,45,46] Type of publication 24 (92) Journal papers [22][23][24][25][26][27][28][29][31][32][33][34][35][36][37]…”

“…This review also found the need to enhance the personalization and customizability of information provided by AI, the incorporation of empathy and personification in AI-based conversational agents, the user experience through better design and interconnectedness with other devices and systems, and the need to educate the public on AI capabilities. Concerning personalized health care, reports generated by AI should be integrated and explained in accordance with each individual’s demographic and clinical profile to facilitate self-management [ 46 ]. We also identified the need for AI to not only assist in the understanding of patients’ medical condition but also the provision of relevant treatment options and personalized recommendations with intuitive actions provided (eg, a button to call an ambulance when deemed necessary by the AI) [ 31 ].…”