Knowledge and attitudes towards artificial intelligence in imaging: a look at the quantitative survey literature

Bhandari, Abhishta; Purchuri, Sai Navya; Sharma, Chinmay; Ibrahim, Mohammed; Prior, Marita

doi:10.1016/j.clinimag.2021.08.004

Cited by 14 publications

(13 citation statements)

References 23 publications

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“…Implementation of AI in radiology has the potential to improve clinical practice by optimising efficacy and efficiency in evaluating images 6 . Most literature surrounding perspectives on AI use in radiology revolve around radiology consultants and trainees, radiographers, medical students or patients 7,8 . Non‐radiologist clinicians are another key stakeholder in the discussion around integrating AI in radiology reporting as they will be referring their patients for imaging and work closely with radiologists as part of furthering patients' interest at multidisciplinary meetings.…”

Section: Discussionmentioning

confidence: 99%

“…6 Most literature surrounding perspectives on AI use in radiology revolve around radiology consultants and trainees, radiographers, medical students or patients. 7,8 Non-radiologist clinicians are another key stakeholder in the discussion around integrating AI in radiology reporting as they will be referring their patients for imaging and work closely with radiologists as part of furthering patients' interest at multidisciplinary meetings. Therefore, we strived to What is your comfort with privacy and security with a radiologist model of reporting?…”

Section: Discussionmentioning

confidence: 99%

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Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports

et al. 2022

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Introduction: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perception of radiologists, radiographers, medical students and patients on AI use in image reporting has been explored, there is limited literature on nonradiologist clinicians' opinion on this topic. Method: Single-centre online survey targeting non-radiologist medical staff conducted from May to August 2021 at a tertiary referral hospital in Melbourne, Australia. Survey questions revolved around clinicians' level of comfort acting on AI-generated reports with varying levels of radiologist involvement and scan complexity, opinion on medicolegal responsibility for erroneous AI-issued reports and perception of data privacy and security. Results: Eighty-eight responses were collected, including 47.9% of consultants. Non-radiologist clinicians across all seniorities and specialties felt significantly less comfortable acting on AI-issued reports compared with radiologist-issued reports (mean comfort radiologist 6.44/7, mean comfort AI 3.35/7, P < 0.001) but felt equally comfortable with an AI-hybrid model of care (mean comfort hybrid 6.38/7, P = 0.676). Non-radiologist clinicians believed that medicolegal responsibility with errors in AI-issued reports mostly lay with hospitals or health service providers (65.9%) and radiologists (54.5%). Regarding data privacy and security, non-radiologist clinicians felt significantly less comfortable with AI issuing image reports instead of radiologists (P < 0.001). Conclusion: A hybrid AI-generated radiologist-confirmed method of image reporting may be the ideal way of integrating AI into clinical practice based on the perception of our referring non-radiologist medical colleagues. Formal guidelines on medicolegal responsibility and data privacy should be established prior to utilising AI in the clinical setting.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports

et al. 2022

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“…Indeed, there is a growing fear of becoming redundant, shared by numerous professionals, including such diverse workers as those in the transportation industry, accountants and healthcare professionals. This fear may be amplified by the vast number and high impact of publications discussing the possibility that humans could be deemed replaceable by machines 17,18 . Sonographers, radiologists and ultrasound experts are no exception 19 .…”

Section: Drukkermentioning

confidence: 99%

Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

Drukker

2022

Ultrasound in Obstet & Gyne

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“…It is believed to reduce the burden of physicians as they can focus on establishing a diagnosis and updating their knowledge as opposed to recalling what they know or interpreting digital data [2]. Therefore, in the long run, it can reduce medical cost and increase e ciency of hectic medical departments [7].…”

Section: Introductionmentioning

confidence: 99%

Medical Students’ Attitude towards Artificial Intelligence in Medicine: A National Multicenter Survey

Yaynishet,

Nasir,

Hailu

et al. 2024

Preprint

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1.1 Objectives The rapid advancement of Artificial Intelligence (AI) is leading to its integration into various fields, including medicine, which is at the forefront of this development. There are numerous perspectives on the future role of AI in medicine, with varying levels of positivity or negativity. In this study, we aimed to assess undergraduate medical students' attitudes towards integrating AI in medicine, to evaluate their need for AI training, identify misconceptions about AI, and explore its impact on their choice of specialization 1.2 Methods To assess the attitudes of medical students towards the integration of AI in medicine, a web-based multicenter survey was conducted across both public and private medical institutes in Ethiopia over a three-month period from June to August 2022. The survey questionnaire included questions on the students’ familiarity with AI, perceptions of AI in medicine, and the need for AI training among medical students. Group comparison was tested by chi-square test for categorical responses and Mann-Whitney test for ordinal rating scale responses. 1.3 Results Of the 1002 respondents, 68.3% were unaware of AI's role in medicine despite over two-thirds (69%) considering themselves tech-savvy; however, a significant majority recognized AI's applications in daily life, particularly through social media (77.6%) and mainstream media (44.9%). Most students (86.9%) believed AI would improve medicine, and notably, 84.9% advocated for its inclusion in medical training, reflecting a strong sentiment that AI education should be a part of the medical curriculum. Over half of the participants (56.8%) felt that AI would positively impact their choice of medical specialty, yet there was a split opinion about the emphasis on AI in developing countries like Ethiopia, with 41.7% preferring not to prioritize it. Students interested in specialties influenced by AI were more inclined to perceive AI as beneficial to medicine, suggesting a correlation between their specialty interests and their views on AI's role in healthcare. 1.4 Conclusions The research findings indicate that while many students surveyed had limited awareness and basic understanding of medical AI, the majority recognized its significant potential in medicine. Most were not concerned about AI replacing them in the future and agreed that AI will enhance medicine and should be integrated into the medical curriculum.

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Knowledge and attitudes towards artificial intelligence in imaging: a look at the quantitative survey literature

Cited by 14 publications

References 23 publications

Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports

Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports

Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

Medical Students’ Attitude towards Artificial Intelligence in Medicine: A National Multicenter Survey

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