PHYSICIANS’ ATTITUDE TOWARDS ARTIFICIAL INTELLIGENCE IN MEDICINE, THEIR EXPECTATIONS AND CONCERNS: AN ONLINE MOBILE SURVEY
The application of artificial intelligence (AI) is on the rise in the healthcare industry. However, the study on the physicians’ perspectives is still lacking. The study aimed to examine physicians’ attitudes, expectations, and concerns regarding the application of AI in medicine. A cross-sectional study was conducted in October 2019 among physicians in a tertiary teaching hospital in Malaysia. The survey used a validated questionnaire from the literature, which covered: (1) socio-demographic profile; (2) attitude towards the application of AI; (3) expected application in medicine; and (4) possible risks of using AI. Comparison of the mean score between the groups using a t-test or one-way analysis of variance (ANOVA). A total of 112 physicians participated in the study: 64.3% from the clinical departments; 35.7% from the non-clinical specialties. The physicians from non-clinical departments had significantly higher mean attitude score (mean = 14.94 ± 3.12) compared to the clinical (person-oriented) departments (mean = 14.13 ± 3.10) and clinical (technique-oriented) departments (mean = 13.06 ± 2.88) (p = 0.033). The tech-savvy participants had a significantly higher mean attitude score (mean = 14.72 ± 3.55) than the non–tech-savvy participants (mean = 13.21 ± 2.46) (p = 0.01). There are differences in the expectations among the respondents and some concerns exist especially on the legal aspect of AI application in medicine. Proper training and orientation should precede its implementation and must be appropriate to the physicians’ needs for its utilization and sustainability.
Klaus Schwab. The Fourth Industrial Revolution. World Economic Forum. 2016.
Topol EJ. High-Performance Medicine: The Convergence of Human and Artificial Intelligence. Nature medicine. 2019;25(1): 44-56. doi: https://doi.org/10.1038/s41591-018-0300-7
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr 1;69:S36-40. doi: https://doi.org/10.1016/j.metabol.2017.01.011
Theofilatos K, Pavlopoulou N, Papasavvas C, et al. Predicting protein complexes from weighted protein–protein interaction graphs with a novel unsupervised methodology: evolutionary enhanced Markov clustering. Artificial intelligence in medicine. 2015 Mar 1;63(3):181-9. doi: https://doi.org/10.1016/j.artmed.2014.12.012
Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology. 2017 May 22;69(21):2657-64. doi: https://doi.org/10.1016/j.jacc.2017.03.571
Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. European journal of internal medicine. 2018; 48: e13–e14. doi: https://doi.org/10.1016/j.ejim.2017.06.017
Cornet G. Robot companions and ethics: A pragmatic approach of ethical design. Journal international de bioéthique. 2013;24(4):49-58. doi: https://doi.org/10.3917/jib.243.0049
Health Ministry Plans to Use Artificial Intelligence. The Star. March 13, 2019. https://www.thestar.com.my/news/nation/2019/03/13/health-ministry-plans-to-use-artificial-intelligence#CR3iDwxx1R0gLbtp.99. Accessed November 29, 2019.
Kamal B. A.I. can help improve patient outcomes. New Strait Times. November 17, 2018. https://www.nst.com.my/opinion/columnists/2018/11/432065/ai-can-help-improve-patient-outcomes. Accessed November 28, 2019.
Sullivan T. Half of hospitals to adopt artificial intelligence within 5 years. Healthcare IT News. April 11, 2017. https://www.healthcareitnews.com/news/half-hospitals-adopt-artificial-intelligence-within-5-years. Accessed on November 28, 2019.
Clark H. The roadmap to introducing AI and robotics in healthcare. Forbes Middle East. April 18, 2018. https://www.forbesmiddleeast.com/featured/special-editions/the-roadmap-to-introducing-ai-and-robotics-in-healthcare. Accessed on November 28, 2019.
Chui M, Bughin J, Hazan E, et al. Artificial intelligence the next digital frontier? McKinsey Global Institute; 2017.
Oh S, Kim JH, Choi SW, et al. Physician Confidence in Artificial Intelligence: An Online Mobile Survey. Journal of medical Internet research. 2019; 21(3): e12422. doi: https://doi.org/10.2196/12422.
Birkett MA, Day SJ. Internal Pilot Studies for Estimating Sample Size. Statistics in medicine. 1994; 13(23‐24): 2455-2463. doi: https://doi.org/10.1002/sim.4780132309
Taber KS. The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education. 2018; 48(6): 1273-1296. doi: https://doi.org/10.1007/s11165-016-9602-2
Future Health Index. 2019. Transforming Healthcare Experiences - Exploring the Impact of Digital Health Technology on Healthcare Professionals and Patients. http://www.indiaenvironmentportal.org.in/files/file/Future_Health_Index_2019.pdf. Accessed on November 28, 2019].
Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer? Am J Med. 2018;Feb;131(2):129-133. doi: 10.1016/j.amjmed.2017.10.035.
World Health Organization. 2018. Global Health Ethics - Big Data and Artificial Intelligence. https://www.who.int/ethics/topics/big-data-artificial-intelligence/en/ Accessed on November 28, 2019.
Okonji PE. Use of computer assistive technologies by older people with sight impairment: Perceived state of access and considerations for adoption. British Journal of Visual Impairment. 2018;May;36(2):128-42. doi: https://doi.org/10.1177/0264619617752760
Enwald H, Kangas M, Keränen N, Korpelainen R, Huvila I, Jämsä T. Opinions and use of mobile information technology among older people in northern finland–preliminary results of a population based study. Proceedings of the Association for Information Science and Technology. 2016;53(1):1-5. doi: https://doi.org/10.1002/pra2.2016.14505301119
Schreder G, Smuc M, Siebenhandl K, Mayr E. Age and Computer Self-Efficacy in the Use of Digital Technologies: An Investigation of Prototypes for Public Self-Service Terminals. Proceedings of the Universal Access in Human-Computer Interaction. User and Context Diversity, LNCS. 2018; Volume 8010, pages 221– 230. Springer Berlin Heidelberg, Germany.
Deiner MS, Lietman TM, Porco TC. Uncertainties in Big Data When Using Internet Surveillance Tools and Social Media for Determining Patterns in Disease Incidence—Reply. JAMA ophthalmology. 2017 Apr 1;135(4):402-3. doi: 10.1001/jamaophthalmol.2017.0140
Benke KK. Uncertainties in big data when using Internet surveillance tools and social media for determining patterns in disease incidence. JAMA ophthalmology. 2017 Apr 1;135(4):402.doi: doi:10.1001/jamaophthalmol.2017.0138
Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216
Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018 Feb 22;172(5):1122-31. doi: https://doi.org/10.1016/j.cell.2018.02.010
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb;542(7639):115-8. doi: https://doi.org/10.1038/nature21056
Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, Huang CS, Shen D, Chen CM. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Scientific reports. 2016 Apr 15;6(1):1-3.doi: https://doi.org/10.1038/srep24454
Doraiswamy PM, Blease C, Bodner K. Artificial intelligence and the future of psychiatry: Insights from a global physician survey. Artificial Intelligence in Medicine. 2020 Jan 1;102:101753.3. doi: https://doi.org/10.1016/j.artmed.2019.101753
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine. 2019 17:195. doi: https://doi.org/10.1186/s12916-019-1426-2
Liang H, Tsui BY, Ni H, Valentim CC, Baxter SL, Liu G, Cai W, Kermany DS, Sun X, Chen J, He L. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature medicine. 2019 Mar;25(3):433-8. doi: https://doi.org/10.1038/s41591-018-0335-9
Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018; 1:18. doi: https://doi.org/10.1038/s41746-018-0029-1.
Stephen Hawking. Artificial intelligence could be the greatest disaster in human history. Independent. October 2016. https://www.independent.co.uk/news/people/stephen-hawking-artificial-intelligence-diaster-human-history-leverhulme-centre-cambridge-a7371106.html. Accessed November 28, 2019.