A qualitative study to explore opinions of Saudi Arabian radiologists concerning AI-based applications and their impact on the future of the radiology

Health Care Professionals' Concerns About Medical AI and Psychological Barriers and Strategies for Successful Implementation: Scoping Review

BACKGROUND

The rapid progress in the development of artificial intelligence (AI) is having a substantial impact on health care (HC) delivery and the physician-patient interaction.

OBJECTIVE

This scoping review aims to offer a thorough analysis of the current status of integrating AI into medical practice as well as the apprehensions expressed by HC professionals (HCPs) over its application.

METHODS

This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to examine articles that investigated the apprehensions of HCPs about medical AI. Following the application of inclusion and exclusion criteria, 32 of an initial 217 studies (14.7%) were selected for the final analysis. We aimed to develop an attitude range that accurately captured the unfavorable emotions of HCPs toward medical AI. We achieved this by selecting attitudes and ranking them on a scale that represented the degree of aversion, ranging from mild skepticism to intense fear. The ultimate depiction of the scale was as follows: skepticism, reluctance, anxiety, resistance, and fear.

RESULTS

In total, 3 themes were identified through the process of thematic analysis. National surveys performed among HCPs aimed to comprehensively analyze their current emotions, worries, and attitudes regarding the integration of AI in the medical industry. Research on technostress primarily focused on the psychological dimensions of adopting AI, examining the emotional reactions, fears, and difficulties experienced by HCPs when they encountered AI-powered technology. The high-level perspective category included studies that took a broad and comprehensive approach to evaluating overarching themes, trends, and implications related to the integration of AI technology in HC. We discovered 15 sources of attitudes, which we classified into 2 distinct groups: intrinsic and extrinsic. The intrinsic group focused on HCPs' inherent professional identity, encompassing their tasks and capacities. Conversely, the extrinsic group pertained to their patients and the influence of AI on patient care. Next, we examined the shared themes and made suggestions to potentially tackle the problems discovered. Ultimately, we analyzed the results in relation to the attitude scale, assessing the degree to which each attitude was portrayed.

CONCLUSIONS

The solution to addressing resistance toward medical AI appears to be centered on comprehensive education, the implementation of suitable legislation, and the delineation of roles. Addressing these issues may foster acceptance and optimize AI integration, enhancing HC delivery while maintaining ethical standards. Due to the current prominence and extensive research on regulation, we suggest that further research could be dedicated to education.

Artificial Intelligence in Radiology: What Is Its True Role at Present, and Where Is the Evidence?

The integration of artificial intelligence (AI) in radiology has brought about substantial advancements and transformative potential in diagnostic imaging practices. This study presents an overview of the current research on the application of AI in radiology, highlighting key insights from recent studies and surveys. These recent studies have explored the expected impact of AI, encompassing machine learning and deep learning, on the work volume of diagnostic radiologists. The present and future role of AI in radiology holds great promise for enhancing diagnostic capabilities, improving workflow efficiency, and ultimately, advancing patient care.

Medical Expectations of Physicians on AI Solutions in Daily Practice: Cross-Sectional Survey Study

Background

The use of artificial intelligence (AI) in medicine has been a trending subject in the past few years. Although not frequently used in daily practice yet, it brings along many expectations, doubts, and fears for physicians. Surveys can be used to help understand this situation.

Objective

This study aimed to explore the degree of knowledge, expectations, and fears on possible AI use by physicians in daily practice, according to sex and time since graduation.

Methods

An electronic survey was sent to physicians of a large hospital in Brazil, from August to September 2022.

Results

A total of 164 physicians responded to our survey. Overall, 54.3% (89/164) of physicians considered themselves to have an intermediate knowledge of AI, and 78.5% (128/163) believed that AI should be regulated by a governmental agency. If AI solutions were reliable, fast, and available, 77.9% (127/163) intended to frequently or always use AI for diagnosis (143/164, 87.2%), management (140/164, 85.4%), or exams interpretation (150/164, 91.5%), but their approvals for AI when used by other health professionals (85/163, 52.1%) or directly by patients (82/162, 50.6%) were not as high. The main benefit would be increasing the speed for diagnosis and management (106/163, 61.3%), and the worst issue would be to over rely on AI and lose medical skills (118/163, 72.4%). Physicians believed that AI would be useful (106/163, 65%), facilitate their work (140/153, 91.5%), not alter the number of appointments (80/162, 49.4%), not interfere in their financial gain (94/162, 58%), and not replace their jobs but be an additional source of information (104/162, 64.2%). In case of disagreement between AI and physicians, most (108/159, 67.9%) answered that a third opinion should be requested. Physicians with ≤10 years since graduation would adopt AI solutions more frequently than those with >20 years since graduation (P=.04), and female physicians were more receptive to other hospital staff using AI than male physicians (P=.008).

Conclusions

Physicians were shown to have good expectations regarding the use of AI in medicine when they apply it themselves, but not when used by others. They also intend to use it, as long as it was approved by a regulatory agency. Although there was hope for a beneficial impact of AI on health care, it also brings specific concerns.

The barriers, motives, perceptions, and attitudes toward research among radiology practitioners and interns in Saudi Arabia: a cross-sectional study

Background

Research and the use of evidence-based practices are imperative to the advancement of diagnostic imaging modalities. The aim of this study was to assess the perceptions and attitudes of radiology practitioners (i.e., Technicians, Technologists or Specialists, and Senior Specialists) and interns in King Abdulaziz Medical Cities (KAMCs), Kingdom of Saudi Arabia, toward research, and to explore the various barriers and obstacles that hinder their research efforts.

Methods

A cross-sectional descriptive investigation was carried out from December 2022 to March 2023 among 112-KAMCs' radiology practitioners and interns, using previously developed and validated questionnaire comprised of five distinct sections, each serving a specific purpose, and with a non-probability convenient sampling technique. Descriptive statistics were generated for participants' demographics, and chi-square and fisher's exact tests were used to examine the association between participants' demographics and their involvement in research.

Results

Among the 137 KAMCs' radiology practitioners and interns who were invited to participate, 112 responded and completed the questionnaire, resulting in an overall response rate of 81.75%. Radiology practitioners and interns from various medical imaging subspecialties were found to be involved in research to the extent of 83%, with nearly half (40.9%) of them have had publications, and 53.3% of these publications being either cross-sectional studies or retrospective clinical studies. A lack of time (66.1%), a lack of a professional supervisor support program (50.9%), and deficiency in research skills (45.5%) were common obstacles that may impede the participants' ability to conduct research. The most common motives for participants to conduct research were the desire to improve their resumes (69.6%), get accepted into postgraduate radiology programs (58%), and improve their research skills (52.7%).

Conclusion

KAMCs' radiology practitioners and interns have a positive attitude toward performing research. Despite the high percentage (83%) of those involved in research, the number of publications remains low. A crucial step to advancing the profession's evidence base is engaging radiology practitioners and interns in research and encouraging radiology practitioner-led research. The study findings can serve as a valuable basis for designing developmental programs aimed at overcoming research obstacles among healthcare professionals in Saudi Arabia.

The Use of Artificial Intelligence in Medical Imaging: A Nationwide Pilot Survey of Trainees in Saudi Arabia

Artificial intelligence is dramatically transforming medical imaging. In Saudi Arabia, there are a lack of studies assessing the level of artificial intelligence use and reliably determining the perceived impact of artificial intelligence on the radiology workflow and the profession. We assessed the levels of artificial intelligence use among radiology trainees and correlated the perceived impact of artificial intelligence on the workflow and profession with the behavioral intention to use artificial intelligence. This cross-sectional study enrolled radiology trainees from Saudi Arabia, and a 5-part-structured questionnaire was disseminated. The items concerning the perceived impact of artificial intelligence on the radiology workflow conformed to the six-step standard workflow in radiology, which includes ordering and scheduling, protocoling and acquisition, image interpretation, reporting, communication, and billing. We included 98 participants. Few used artificial intelligence in routine practice (7%). The perceived impact of artificial intelligence on the radiology workflow was at a considerable level in all radiology workflow steps (range, 3.64−3.97 out of 5). Behavioral intention to use artificial intelligence was linearly correlated with the perceptions of its impact on the radiology workflow and on the profession (p < 0.001). Artificial intelligence is used at a low level in radiology. The perceived impact of artificial intelligence on radiology workflow and the profession is correlated to an increase in behavioral intention to use artificial intelligence. Thus, increasing awareness about the positive impact of artificial intelligence can improve its adoption.