Equity and Artificial Intelligence in Surgical Care: A Comprehensive Review of Current Challenges and Promising Solutions
Keywords:
Artificial intelligence, surgical care, equity, bias, patient outcomes, access to surgical services, ethical practices, implementation, implications, future directions.Abstract
The use of artificial intelligence (AI) has become a viable method for improving surgical care equity. In order to better understand the effects of AI in surgical settings, this paper focuses on five key areas: promising AI applications, bias-reduction tactics, and ethical AI implementation, effects on patient outcomes and access to surgical services, and future directions for equitable AI in surgical care. The first part of the article looks at the exciting uses of AI in surgery. It emphasizes how AI technology may boost decision-making, increase surgical precision, and improve patient care routes. Better surgical outcomes, individualised treatment plans, and streamlined procedures can all result from the incorporation of AI algorithms, which will ultimately help patients from a variety of groups. The solutions for reducing bias and fostering equity in AI-enabled surgical care are covered in more detail in the second part. In order to reduce biases, it emphasizes the value of diverse and representative datasets, algorithmic transparency, and fairness metrics. Healthcare disparities can be decreased by proactively addressing bias, and AI-enabled surgical care can help ensure fair outcomes for all patient populations. The final segment is devoted to removing obstacles in the way of deploying moral AI procedures in surgical settings. It places a strong emphasis on the necessity of open governance structures, informed consent procedures, privacy protection, accountability, and ongoing ethical assessment. Accountability is guaranteed through transparent governance systems, which also offer a way to address moral issues and potential biases. The implications of AI for patient outcomes and access to surgical services are covered in the fourth part. It emphasizes how AI technologies have the potential to enhance decision-making, improve surgical results, and streamline patient care routes. It also covers issues with bias, privacy, and ethics that must be taken into account to enable responsible and fair implementation. The fifth segment examines potential future directions and surgical care opportunities for egalitarian AI. Strong data infrastructure, advances in deep learning and machine learning, explainable AI, AI-driven surgical automation, tackling health disparities, and the creation of ethical and legal frameworks are some of the themes it highlights. These regions have enormous opportunity to improve patient outcomes and advance fair access to surgical care. Enhancing equity is made possible by the incorporation of AI in surgical care. Healthcare organizations can enhance surgical results, lower inequities, and guarantee equitable access to surgical services by utilizing AI technologies. To be responsible and equitable, a deployment must address bias, adhere to ethical standards, and take into account how AI is developing. To maximize the advantages of AI in surgical care while advancing equity and patient-centered care, future research and collaboration are crucial.
References
Soguero-Ruiz C, Hindberg K, Mora-Jimenez I, et al. Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods. J Biomed Inform. 2016; 61:87–96. [PubMed: 26980235]
DiPietro, R., Lea, C., Malpani, A., et al. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing; 2016. Recognizing surgical activities with recurrent neural networks; p. 551-558.
Zappella L, Béjar B, Hager G, et al. Surgical gesture classification from video and kinematic data. Medical image analysis. 2013; 17(7):732–745. [PubMed: 23706754]
Moustris GP, Hiridis SC, Deliparaschos KM, et al. Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature. Int J Med Robot. 2011; 7(4):375–92. [PubMed: 21815238]
Bellman, RE. Adaptive control processes: a guided tour. Princeton university press; 2015.
Rudin C, Dunson D, and Irizarry R, et al. Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society. American Statistical Association White Paper. 2014
Council, NR. Frontiers in massive data analysis. National Academies Press; 2013.
Jüni P, Altman DG, Egger M. Systematic reviews in health care: assessing the quality of controlled clinical trials. BMJ: British Medical Journal. 2001; 323(7303):42. [PubMed: 11440947]
Hopewell S, Loudon K, Clarke MJ, et al. Publication bias in clinical trials due to statistical significance or direction of trial results. The Cochrane Library. 2009
Murthy VH, Krumholz HM, Gross CP. Participation in cancer clinical trials: race-, sex-, and agebased disparities. Jama. 2004; 291(22):2720–2726. [PubMed: 15187053]
Chang AM, Mumma B, Sease KL, et al. Gender bias in cardiovascular testing persists after adjustment for presenting characteristics and cardiac risk. Academic Emergency Medicine. 2007; 14(7):599–605. [PubMed: 17538080]
Douglas PS, Ginsburg GS. The evaluation of chest pain in women. New England Journal of Medicine. 1996; 334(20):1311–1315. [PubMed: 8609950]
Wang, X., Peng, Y., Lu, L., et al. IEEE CVPR. Honolulu, HI: 2017. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases.
Ribeiro, MT., Singh, S., Guestrin, C. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2016. Why should i trust you? Explaining the predictions of any classifier; p. 1135-1144.
Sussillo D, Barak O. Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural computation. 2013; 25(3):626–649. [PubMed: 23272922]
Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. Jama. 2017; 318(6):517–518. [PubMed: 28727867]
Sturm I, Lapuschkin S, Samek W, et al. Interpretable deep neural networks for single-trial EEG classification. Journal of neuroscience methods. 2016; 274:141–145. [PubMed: 27746229]
Pearl, J. Causality: Models, Reasoning and Inference. 2. Cambridge, UK: Cambridge University Press; 2009.
Wang D, Khosla A, Gargeya R, et al. Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718. 2016
Harvey C, Koubek R, Begat V, et al. Usability Evaluation of a Blood Glucose Monitoring System With a Spill-Resistant Vial, Easier Strip Handling, and Connectivity to a Mobile App: Improvement of Patient Convenience and Satisfaction. J Diabetes Sci Technol. 2016; 10(5):1136– 41. [PubMed: 27390222]
Hale C. Abbott launches AI-powered coronary OCT imaging system in Europe. https://www.fiercebiotech.com/medtech/ abbott-launches-ai-powered-coronary-oct-imaging-systemeurope. Updated April 27, 2021
Hodges BD. Learning from Dorothy Vaughan: artificial intelligence and the health professions. Med Educ. 2018; 52(1):11-13. doi:10.1111/medu.13350.
Bansal, T., Gunasekaran, K., Wang, T., Munkhdalai, T., & McCallum, A. (2021). Diverse distributions of self-supervised tasks for meta-learning in NLP. arXiv preprint arXiv:2111.01322
Dobrev D. Formal definition of artificial intelligence. Int J Inf Theories Appl. 2005; 12:277-285.
Zawacki-Richter O, Mar´ın VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? Int J Educ Technol High Educ. 2019; 1). Doi: 10.1186/ s41239-019-0171-0.
Akhter, A., & Shams, A. T. (2022). Identity Economics in Emily Brontë’s Wuthering Heights: An Empathetic Inquiry into Psychoanalysis. SCHOLARS: Journal of Arts & Humanities, 4(2), 74-80.
Rana, A., Reddy, A., Shrivastava, A., Verma, D., Ansari, M. S., & Singh, D. (2022, October). Secure and Smart Healthcare System using IoT and Deep Learning Models. In 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 915-922). IEEE.
Julian D, Smith R. Developing an intelligent tutoring system for robotic-assisted surgery instruction. Int J Med Robot. Dec 2019; 15(6):e2037. doi:10.1002/rcs.2037.
Baloul MS, Yeh VJ, Mukhtar F, et al. Video commentary & machine learning: tell me what you see, i tell you who you are. J Surg Educ. 2020. doi:10.1016/j.jsurg.2020.09.022.
Winkler-Schwartz A, Bissonnette V, Mirchi N, et al. Artificial intelligence in medical education: best practices using machine learning to assess surgical expertise in virtual reality simulation. J Surg Educ. 2019; 76(6):1681-1690. doi: 10.1016/j.jsurg.2019.05.015.
Shams, A. T., & Akter, S. (2022). Eco-Centric Versus Anthropocentric Approach in Literary Pedagogy: Inclusion of Non-Human Narratives as Teaching Social Justice.
Mirchi N, Bissonnette V, Ledwos N, et al. Artificial neural networks to assess virtual reality anterior cervical discectomy performance. Oper Neurosurg (Hagerstown). 2020; 19(1):65-75. doi:10.1093/ons/opz359.
Dell’Oglio P, Turri F, Larcher a et al (2019) Definition of a structured training curriculum for robotassisted radical cystectomy: a Delphi-consensus study led by the ERUS Educational Board. Eur Urol Suppl 18(1):e1116–e1119
Gillespie, A., Yirsaw, A., Kim, S., Wilson, K., McLaughlin, J., Madigan, M., ... & Baldwin, C. L. (2021). Gene characterization and expression of the γδ T cell co-receptor WC1 in sheep. Developmental & Comparative Immunology, 116, 103911.
Rashid, M. T., Zhang, D. Y., & Wang, D. (2019, December). Socialcar: A task allocation framework for social media driven vehicular network sensing systems. In 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) (pp. 125-130). IEEE.
Istasy, P., Lee, W. S., Iansavichene, A., Upshur, R., Gyawali, B., Burkell, J., ... & Chin-Yee, B. (2022). The impact of artificial intelligence on health equity in oncology: scoping review. Journal of medical Internet research, 24(11), e39748.
Bihorac, A. (2021). Equity and Artificial Intelligence in Surgical Car
Wang, J. X., Somani, S., Chen, J. H., Murray, S., & Sarkar, U. (2021). Health equity in artificial intelligence and primary care research: protocol for a scoping review. JMIR research protocols, 10(9), e27799.
Uche-Anya E, Anyane-Yeboa A, Berzin TM, Ghassemi M, May FP. Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity. Gut. 2022 Sep 1;71(9):1909-15
Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018). Artificial intelligence in surgery: promises and perils. Annals of surgery, 268(1), 70-76.
