Perceptions of Artificial Intelligence in Medicine: A Survey of Ophthalmologists, Dermatologists, Radiologists and Radiation Oncologists
51 Pages Posted: 25 Jun 2020More...
Background: The use of artificial intelligence for medical image analysis has the potential to improve the provision of healthcare. Successful uptake of these new technologies will be critically influenced by the knowledge and attitudes of clinicians.
Methods: Online multiple choice and open-ended questions were used to survey members of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand to assess their perceptions about the use and impact of artificial intelligence in their respective fields.
Findings: A total of 632 respondents completed the survey (305 ophthalmology, 230 radiology/radiation oncology and 97 dermatology). Respondents from the different professional groups were comparable for key demographic characteristics. The majority (n= 449, 71%) of respondents thought that artificial intelligence would improve their field of medicine, and that it would be less than 5 years until a noticeable impact was seen (n= 379, 60%). The majority of respondents also believed that medical workforce needs would be impacted ‘somewhat’ or ‘to a great extent’ by the technology within the next decade (n= 542, 85%). Improved access to disease screening and reduced time spent on monotonous tasks were the top potential advantages of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest disadvantages of the technology. Education was identified as a priority for the future preparedness of clinicians.
Interpretation: This survey highlights parallels between the perceptions of ophthalmologists, radiologists/radiation oncologists and dermatologists in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as a valuable medical technology that will have wide-ranging impacts on healthcare.
Funding Statement: The authors received no funding for this study. The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian Government.
Declaration of Interests: 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.
Ethics Approval Statement: This prospective anonymous online survey was approved by the Royal Victorian Eye and Ear Hospital Melbourne Human Research Ethics Committee (HREC 18-1408HL). Electronic informed consent was obtained online prior to survey commencement.
Keywords: ophthalmology; radiology; radiation oncology; dermatology; artificial intelligence; deep learning; machine learning; perceptions
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