General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study

Buck, Christoph; Doctor, Eileen; Hennrich, Jasmin; Jöhnk, Jan; Eymann, Torsten

doi:10.2196/28916

Cited by 57 publications

(97 citation statements)

References 80 publications

(106 reference statements)

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“…For example, 75% of 487 pathologists from 59 countries were enthusiastic about the progress of AI ( 11 ); 77% of 1271 dermatologists from 92 countries agreed that AI would improve dermatologic practice ( 19 ). Similar positive opinions also existed among radiologists ( 22 , 23 , 25 , 29 , 33 , 39 , 47 , 48 , 53 , 55 , 61 ), gastroenterologists ( 24 , 35 ), general practitioners ( 28 , 62 ), psychiatrists ( 59 , 63 ), ophthalmologists ( 23 , 50 ). Additionally, in 14 studies reporting use intentionality, more than 60% respondents in 10 (71.43%) studies were willing to incorporate AI into their clinical practice ( 17 , 21 , 26 , 34 , 36 , 44 , 49 , 55 , 56 , 61 ).…”

Section: Resultsmentioning

confidence: 52%

“…Thirty-five included studies mentioned the knowledge level of physicians or medical students on clinical AI, of which 26 (74.29%) showed that participants lacked basic knowledge ( 16 – 19 , 23 , 25 , 26 , 28 – 46 ). Many physicians felt that the current training and educational tools, provided by their departments, were inadequate ( 47 , 48 ).…”

Section: Resultsmentioning

confidence: 99%

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Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey

Chen

Zhang²,

Cai³

et al. 2022

Front. Med.

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BackgroundArtificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance.Materials and methodsWe conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world.ResultsSixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10–30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes.ConclusionMost physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.

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Section: Resultsmentioning

confidence: 52%

Section: Resultsmentioning

confidence: 99%

Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey

Chen

Zhang²,

Cai³

et al. 2022

Front. Med.

View full text Add to dashboard Cite

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“…One uses an online survey to poll healthcare practitioners in image-driven medical disciplines 5 . Another interviews 18 general practitioners on their views on AI 6 . Optimism and concern emerged from these works.…”

Section: Plain Language Summarymentioning

confidence: 99%

Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit

et al. 2022

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Background Despite apparent promise and the availability of numerous examples in the literature, machine learning models are rarely used in practice in ICU units. This mismatch suggests that there are poorly understood barriers preventing uptake, which we aim to identify. Methods We begin with a qualitative study with 29 interviews of 40 Intensive Care Unit-, hospital- and MedTech company staff members. As a follow-up to the study, we attempt to quantify some of the technical issues raised. To perform experiments we selected two models based on criteria such as medical relevance. Using these models we measure the loss of performance in predictive models due to drift over time, change of available patient features, scarceness of data, and deploying a model in a different context to the one it was built in. Results The qualitative study confirms our assumptions on the potential of AI-driven analytics for patient care, as well as showing the prevalence and type of technical blocking factors that are responsible for its slow uptake. The experiments confirm that each of these issues can cause important loss of predictive model performance, depending on the model and the issue. Conclusions Based on the qualitative study and quantitative experiments we conclude that more research on practical solutions to enable AI-driven innovation in Intensive Care Units is needed. Furthermore, the general poor situation with respect to public, usable implementations of predictive models would appear to limit the possibilities for both the scientific repeatability of the underlying research and the transfer of this research into practice.

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“…Whilst we did not directly assess PCPs attitudes towards our e-triage tool, other studies have examined their attitude towards AI and telemedicine more generally. 36 , 37 One study highlights some existential anxiety from PCPs towards AI and also feeling threated by AI technology, however, another study found PCP's largely have positive intentions to use telemedicine and e-triage tools in the future. 36 Whilst this reluctance could pose some difficulties in implementing our chatbot tool, Pikkemaat et al suggest that empowering PCP's self-efficacy towards the use of digital tools is key.…”

Section: Discussionmentioning

confidence: 99%

An automated, web-based triage tool may optimise referral pathways in elective orthopaedic surgery: A proof-of-concept study

et al. 2023

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Introduction Knee pain is caused by various pathologies, making evaluation in primary-care challenging. Subsequently, an over-reliance on imaging, such as radiographs and MRI exists. Electronic-triage tools represent an innovative solution to this problem. The aims of this study were to establish the magnitude of unnecessary knee imaging prior to orthopaedic surgeon referral, and ascertain whether an e-triage tool outperforms existing clinical pathways to recommend correct imaging. Methods Patients ≥18 years presenting with knee pain treated with arthroscopy or arthroplasty at a single academic hospital between 2015 and 2020 were retrospectively identified. The timing and appropriateness of imaging were assessed according to national guidelines, and classified as ‘necessary’, ‘unnecessary’ or ‘required MRI’. Based on an eDelphi consensus study, a symptom-based e-triage tool was developed and piloted to preliminarily diagnose five common knee pathologies and suggest appropriate imaging. Results 1462 patients were identified. 17.2% ( n = 132) of arthroplasty patients received an ‘unnecessary MRI’, 27.6% ( n = 192) of arthroscopy patients did not have a ‘necessary MRI’, requiring follow-up. Forty-one patients trialled the e-triage pilot (mean age: 58.4 years, 58.5% female). Preliminary diagnoses were available for 33 patients. The e-triage tool correctly identified three of the four knee pathologies (one pathology did not present). 79.2% ( n = 19) of participants would use the tool again. Conclusion A substantial number of knee pain patients receive incorrect imaging, incurring delays and unnecessary costs. A symptom-based e-triage tool was developed, with promising performance and user feedback. With refinement using larger datasets, this tool has the potential to improve wait-times, referral quality and reduce cost.

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General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study

Cited by 57 publications

References 80 publications

Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey

Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey

Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit

An automated, web-based triage tool may optimise referral pathways in elective orthopaedic surgery: A proof-of-concept study

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