. 2021 Mar 4;11(1):5193.

doi: 10.1038/s41598-021-84698-5.

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

Jane Scheetz^#¹, Philip Rothschild^#^{1

2}, Myra McGuinness^{1

3}, Xavier Hadoux¹, H Peter Soyer^{4

5}, Monika Janda⁶, James J J Condon^{7

8}, Luke Oakden-Rayner^{7

8}, Lyle J Palmer^{7

8}, Stuart Keel¹, Peter van Wijngaarden^{9

10}

Affiliations

¹ Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia.
² Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
³ Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
⁴ Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia.
⁵ Dermatology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia.
⁶ Centre for Health Services Research, The University of Queensland, Brisbane, Australia.
⁷ School of Public Health, University of Adelaide, Adelaide, Australia.
⁸ Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia.
⁹ Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia. peterv@unimelb.edu.au.
¹⁰ Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia. peterv@unimelb.edu.au.

^# Contributed equally.

PMID: 33664367
PMCID: PMC7933437
DOI: 10.1038/s41598-021-84698-5

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

Jane Scheetz et al. Sci Rep. 2021.

. 2021 Mar 4;11(1):5193.

doi: 10.1038/s41598-021-84698-5.

Authors

Affiliations

¹ Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia.
² Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
³ Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
⁴ Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia.
⁵ Dermatology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia.
⁶ Centre for Health Services Research, The University of Queensland, Brisbane, Australia.
⁷ School of Public Health, University of Adelaide, Adelaide, Australia.
⁸ Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia.
⁹ Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia. peterv@unimelb.edu.au.
¹⁰ Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia. peterv@unimelb.edu.au.

^# Contributed equally.

PMID: 33664367
PMCID: PMC7933437
DOI: 10.1038/s41598-021-84698-5

Abstract

Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June-August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.

PubMed Disclaimer

Conflict of interest statement

The authors declare the following competing interests: HPS is a shareholder of MoleMap NZ Limited and e-derm consult GmbH and undertakes regular teledermatological reporting for both companies. HPS is a Medical Consultant for Canfield Scientific Inc., MetaOptima and Revenio Research Oy and also a Medical Advisor for First Derm. HPS holds an NHMRC MRFF Next Generation Clinical Researchers Program Practitioner Fellowship (APP1137127). The authors declare that there are no other competing interests. The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian Government.

Figures

**Figure 1**
Self-reported knowledge of artificial intelligence and its application in the respondent’s specialty, relative to peers in that field.

**Figure 2**
Current frequency of artificial intelligence use in clinical practice.

**Figure 3**
Levels of agreement with the statement “the field of [your specialty] will improve with the introduction of artificial intelligence”.

**Figure 4**
Perceived length of time before artificial intelligence has a noticeable impact on [your specialty].

**Figure 5**
Estimated impact of artificial intelligence on workforce needs within (< 10 years) and beyond (> 10 years) the next decade for each specialty group.

**Figure 6**
100% stacked bars showing the acceptable level of error for an AI tool used for (A) disease screening and (B) clinical decision support.

**Figure 7**
Radar plot showing the highest scoring responses for the greatest perceived advantages of the use of artificial intelligence. Responses were selected from a list of set choices. Plot axes represent the average ranks for all respondents, with higher scores indicating a higher ranking/stronger preference.

**Figure 8**
Radar plot showing the highest scoring responses for the perceived concerns or drawbacks of the use of artificial intelligence. Responses were selected from a list of set choices. Plot axes represent the average ranks for all respondents, with higher scores indicating a higher ranking/stronger preference.

See this image and copyright information in PMC

Cited by

Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks.
Daniyal M, Qureshi M, Marzo RR, Aljuaid M, Shahid D. Daniyal M, et al. BMC Health Serv Res. 2024 May 9;24(1):587. doi: 10.1186/s12913-024-10928-x. BMC Health Serv Res. 2024. PMID: 38725039 Free PMC article.
Knowledge, interest and perspectives on Artificial Intelligence in Neurosurgery. A global survey.
Boaro A, Mezzalira E, Siddi F, Bagattini C, Gabrovsky N, Marchesini N, Broekman M, Sala F; EANS ETIN Task Force. Boaro A, et al. Brain Spine. 2024 Dec 9;5:104156. doi: 10.1016/j.bas.2024.104156. eCollection 2025. Brain Spine. 2024. PMID: 39802868 Free PMC article.
Medical and Paramedical Students' Perspectives on Artificial Intelligence in a Low- and Middle-Income Country: A Cross-Sectional Study.
Mousavi Baigi SF, Sarbaz M, Darroudi A, Kimiafar K. Mousavi Baigi SF, et al. Health Sci Rep. 2025 Aug 4;8(8):e71046. doi: 10.1002/hsr2.71046. eCollection 2025 Aug. Health Sci Rep. 2025. PMID: 40761605 Free PMC article.
Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey.
Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, Ren R, Qiao Y, Xue P, Jiang Y. Chen M, et al. Front Med (Lausanne). 2022 Aug 31;9:990604. doi: 10.3389/fmed.2022.990604. eCollection 2022. Front Med (Lausanne). 2022. PMID: 36117979 Free PMC article.
Understanding Prospective Physicians' Intention to Use Artificial Intelligence in Their Future Medical Practice: Configurational Analysis.
Wagner G, Raymond L, Paré G. Wagner G, et al. JMIR Med Educ. 2023 Mar 22;9:e45631. doi: 10.2196/45631. JMIR Med Educ. 2023. PMID: 36947121 Free PMC article.

See all "Cited by" articles

References

1. Haenssle HA, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 2018;29(8):1836–1842. doi: 10.1093/annonc/mdy166. - DOI - PubMed
1. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. Npj Dig. Med. 2018;1(1):1–8. doi: 10.1038/s41746-017-0008-y. - DOI - PMC - PubMed
1. Li Z, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41(12):2509–2516. doi: 10.2337/dc18-0147. - DOI - PubMed
1. Ting DSW, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017;318(22):2211–2223. doi: 10.1001/jama.2017.18152. - DOI - PMC - PubMed
1. Li Z, He Y, Keel S, Meng W, Chang RT, He MJO. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125(8):1199–1206. doi: 10.1016/j.ophtha.2018.01.023. - DOI - PubMed

LinkOut - more resources

Full Text Sources
Other Literature Sources
- scite Smart Citations

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

Affiliations

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

LinkOut - more resources

Full Text Sources

Other Literature Sources