An Investigation Into The Role Of Artificial Intelligence In The Healthcare Industry
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Abstract
Healthcare systems are intricate and present challenges for all involved parties. However, artificial intelligence (AI) is making significant strides across various sectors, including healthcare, where it holds promise for enhancing patient care and overall quality of life. The rapid advancements in AI have the potential to change the landscape of healthcare by actively incorporating it into clinical practices. With its growing capability to turn complex and uncertain data into actionable—albeit imperfect—clinical decisions or recommendations, AI can significantly influence healthcare operations. In the dynamic interaction between humans and AI, trust emerges as a crucial element that affects how clinicians adopt and utilize these technologies. This paper investigates the role of clinicians as the primary users of AI systems in healthcare and discusses the factors that influence the trust between clinicians and AI.
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Ahmed Al Kuwaiti 1, Khalid Nazer ,Abdullah Al-Reedy , Shaher Al-Shehri , Afnan Al-Muhanna, Arun Vijay Subbarayalu, Dhoha Al Muhannaand Fahad A, Al-Muhanna 10,11 A Review of the Role of Artificial Intelligence in Health, J. Pers. Med. 2023, 13, 951. https://doi.org/10.3390/jpm13060951.
Snowdon A. Digital Health: A Framework for Healthcare Transformation. 2020. Available online: https://www.gs1ca.org/ documents/digital_health-affht.pdf (accessed on 23 January 2023).
Mistry C,Thakker U, Gupta R., Obaidat M.S., Tanwar, S., Kumar N., Rodrigues J.J.P.C. MedBlock: An AI-enabled and blockchain-driven medical healthcare system for COVID-19. In Proceedings of the IEEE International Conference Communication, Montreal, QC, Canada, 14–23 June 2021; pp. 1–6.].
Baowaly M.K, Lin C.C, Liu C.L, Chen K.T. Synthesizing electronic health records using improved generative adversarial networks. J. Am. Med. Inform. Assoc. 2018, 26: 228–241
AI Shakeel T., Habib S., Boulila W., Koubaa A., Javed A.R., Rizwan M., Gadekallu T.R., Sufiyan M. A survey on COVID-19 impact in the healthcare domain: Worldwide market implementation, applications, security and privacy issues, challenges and future prospects. Complex Intell. Syst. 2022, 9:1027–1058.
Lee S.M., Lee D. Opportunities and challenges for contactless healthcare services in the post-COVID-19 Era. Technol. Forecast. Soc. Chang. 2021, 167, 120712.
Carroll W.M. Digital health and new technologies. In Nursing and Informatics for the 21st Century Embracing a Digital World, 3rd ed.; Productivity Press: New York, NY, USA, 2022; pp. 29–48.
Snowdon A. Digital Health: A Framework for Healthcare Transformation. 2020. Available online: https://www.gs1ca.org/ documents/digital_health-affht.pdf (accessed on 23 January 2023).
Williams O.D. COVID-19 and Private Health: Market and Governance Failure. Development 2020, 63:181–190.
Tabriz A.A, Nouri E., Vu H.T., Nghiem V.T., Bettilyon B., Gholamhoseyni P., Kiapour N. What should accountable care organizations learn from the failure of health maintenance organizations? A theory based systematic review of the literature. Soc. Determ. Health 2017, 3:222–247.
Tabriz A.A., Nouri E., Vu H.T., Nghiem V.T., Bettilyon B., Gholamhoseyni P., Kiapour N. What should accountable care organizations learn from the failure of health maintenance organizations? A theory based systematic review of the literature. Soc. Determ. Health 2017, 3: 222–247.
Manas Dave and Neil Patel,.Artifcial intelligence in healthcare and education, British Dental Journal 234 (10) :2023
Nkosi Botha1, Cynthia E. Segbedzi , Victor K., Dumahasi, Samuel Maneen, Ruby V. Kodom , Ivy S. Tsedze , Lucy A. Akoto , Fortune S. Atsu, Obed U. Lasim5 and Edward W. Ansah1 Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety , Archives of Public Health 2024 82:188 , https://doi.org/10.1186/s13690-024-01414-1
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMAScR): Checklist and explanation. Ann Intern Med. 2018. https://doi.org/10.7326/M18-0850
Reddy S., Fox J., Purohit MP. Artificial intelligence-enabled healthcare delivery. J R Soc Med. 2019. https://doi.org/10.1177/0141076818815510.
Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, et al. Patient apprehensions about the use of artificial intelligence in healthcare. Npj Digit Med. 2021. https://doi.org/10.1038/s41746-021-00509-1.
Kerasidou A. Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare. Bull World Health Organisation. 2020. https:// doi.org/10.2471/BLT.19.237198.
Rubeis G. iHealth: the ethics of artificial intelligence and big data in mental healthcare. Internet Interventions. 2022. https://doi.org/10.1016/j. invent.2022.100518.
45. Solanki P, Grundy J, Hussain W. Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers. AI Ethics. 2023. https://doi. org/10.1007/s43681-022-00195-z.
Chen C, Ding S, Wang J. Digital health for aging populations. Nat Med. 2023. https://doi.org/10.1038/s41591-023-02391-8. 7.
Naik N, Hameed BMZ, Shetty DK, Swain D, Shah M, Paul R, et al. Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Front Surg. 2022. https://doi.org/10.3389/fsurg.2022.862322
Esteva A., Kuprel B., Novoa R.A, Ko J., Swetter S.M., Blau H.M. Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017, 542, 115–118.
Rajpurkar P., Irvin J., Zhu K., Yang B., Mehta H., Duan, T.,Ding D., Bagul A., Langlotz C., Shpanskaya K. Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv 2017, arXiv:1711.05225.
Bedi G., Carrillo F., Cecchi G.A., Slezak D.F.,Sigman M., Mota N.B., Ribeiro S., Javitt, D.C., Copelli, M., Corcoran C.M. Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schitzophrenia 2015, 1:15030.
IBM Research. IBM 5 With AI, Our Words Will Be a Window into Our Mental Health. 2017. Available online: https: //www.ibm.com/blogs/research/2017/1/ibm-5-in-5-our-words-will-be-the-windows-to-our-mental-health/ (accessed on 25 December 2022).
Chou C.Y., Hsu, D.-Y.; Chou, C.-H. Predicting the Onset of Diabetes with Machine Learning Methods. J. Pers. Med. 2023, 13, 406.
Gudigar, A.; Raghavendra, U.; Nayak, S.; Ooi, C.P.; Chan, W.Y.; Gangavarapu, M.R.; Dharmik, C.; Samanth, J.; Kadri, N.A.; Hasikin, K.; et al. Role of Artificial Intelligence in COVID-19 Detection. Sensors 2021, 21, 8045
Khanna, V.V.; Chadaga, K.; Sampathila, N.; Prabhu, S.; Chadaga, R.; Umakanth, S. Diagnosing COVID-19 using artificial intelligence: A comprehensive review. Netw. Model Anal Health Inf. Bioinforma 2022, 11, 25
He, K.; Gan, C.; Li, Z.; Rekik, I.; Yin, Z.; Ji, W.; Gao, Y.; Wang, Q.; Zhang, J.; Shen, D. Transformers in medical image analysis. Intell. Med. 2023, 3, 59–78.
Costa, G.S.S.; Paiva, A.C.; Junior, G.B.; Ferreira, M.M. COVID-19 automatic diagnosis with ct images using the novel transformer architecture. In Proceedings of the 21st Brazilian Symposium on Computing Applied to Health, Rio de Janeiro, Brazil, 15–18 June 2021; pp. 293–301.
59. van Tulder, G.; Tong, Y.; EMarchiori, E. Multi-view analysis of unregistered medical images using cross-view transformers. In Proceedings of the Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Part III 24, Strasbourg, France, 27 September–1 October 2021; Springer Nature: Basel, Switzerland, 2021; pp. 104–113.
0. Krishnan, K.S.; Krishnan, K.S. Vision transformer based COVID-19 detection using chest x-rays. In Proceedings of the 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 7–9 October 2021; pp. 644–648.
. Wang, S.-H.; Wu, X.; Zhang, Y.-D.; Tang, C.; Zhang, X. Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization. Int. J. Comput. Intell. Syst. 2020, 13, 1332–1344.
Baig M.M., GholamHosseini H., Moqeem A.A., Mirza F., Lindén, M. A Systematic Review of Wearable Patient Monitoring Systems–Current Challenges and Opportunities for Clinical Adoption. J. Med. Syst. 2017, 41, 115.
Kim J., Campbell A.S., Wang J. Wearable non-invasive epidermal glucose sensors: A review. Talanta 2018;177:163–170.
Academy of Royal Medical Colleges. Artificial Intelligence in Healthcare. Available online:https://www.aomrc.org.uk/wpcontent/uploads/2019/01/Artificial_intelligence_in_healthcare_0119.
O mara-Eves A., Thomas, J., McNaught J., Miwa M. Ananiadou S. Using text mining for study identification in systematic reviews: A systematic review of current approaches. Syst. Rev. 4(5): 2015.
Weissler E.H., Naumann T., Andersson T., Ranganath R., Elemento O., Luo Y. Freitag D.F., Benoit J., Hughes M.C., Khan F. The role of machine learning in clinical research: Transforming the future of evidence generation. Trials 2021;22: 1–15.
Arora A. Generative adversarial networks and synthetic patient data: Current challenges and future perspectives. Futur. Healthcare J. 2022; 9:190–193
Suh, I.; McKinney, T.; Siu, K.-C. Current Perspective of Metaverse Application in Medical Education, Research and Patient Care. Virtual Worlds 2023, 2, 115–128
Javaid M., Haleem A., Singh R.P. ChatGPT for healthcare services: An emerging stage for an innovative perspective. Bench Council Trans. Benchmarks Stand. Eval. 2023;3:100105
Khan R.A., Jawaid M., Khan A.R., Sajjad M. ChatGPT-Reshaping medical education and clinical management. Pak. J. Med. Sci. 2023; 39:7653.
Davenport, T.; Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthcare J. 2019, 6, 94–98.
Davenport T.H., Hongsermeier T., Mc Cord, K.A. Using AI to Improve Electronic Health Records. Harvard Business Review. 2018. Available online: https://hbr.org/2018/12/using-ai-to-improve-electronic-health-records.
Volpp K.; Mohta, S. Improved Engagement Leads to Better Outcomes, But Better Tools Are Needed. Insights Report. NEJM Catalyst. 2016. Available online: https://catalyst.nejm.org/patient-engagementreport-improved-engagement-leads-betteroutcomes-better-toolsneeded (accessed on 15 January 2023)
Hukunda, B., Rau A., Upadhyay P. Reimaging Healthcare Opportunities with Artificial Intelligence. Infosys Navigate Your Next. 2018. Available online: https://www.infosys.com/industries/healthcare/ featuresopinions/Documents/reimagininghealthcare-opportunities.pdf (accessed on 10 January 2023).
Anderson D. Artificial Intelligence and Applications in PM&R. Am. J. Phys. Med. Rehabil. 2019;98:e128–e129.
Luxton D.D., Riek L.D. Artificial intelligence and robotics in rehabilitation. In Handbook of Rehabilitation Psychology; Brenner, L.A., Reid-Arndt, S.A., Elliott, T.R., Frank, R.G., Caplan, B., Eds.; American Psychological Association: Washington, DC, USA, 2019:507–520.
Goldzweig C.L., Orshansky, G., Paige N.M., Towfigh A.A., Haggstrom D.A., Miake-Lye I., Beroes J.M., Shekelle P.G. Electronic Patient Portals: Evidence on Health Outcomes, Satisfaction, Efficiency, and Attitudes. Ann. Intern. Med. 2013;159: 677–687.
Sinsky C.A., Willard-Grace R., Schutzbank, A.M., Margolius D., Bodenheimer T. In Search of Joy in Practice: A Report of 23 High-Functioning Primary Care Practices. Ann. Fam. Med. 2013; 11:272–278
Aggarwal R., Ganvir S.S. Artificial intelligence in physiotherapy. Physiother. J. Indian Assoc. Physiother. 2021; 15:55
Sharma A., Lin I.W., Miner A.S., Atkins D.C., Althoff T. Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nat. Mach. Intell. 2023; 5:46–57
Wani S.U.D., Khan N.A., Thakur G., Gautam S.P., Ali M., Alam P., Alshehri S., Ghoneim M.M., Shakeel F. Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce. Healthcare 2022;10: 608.
Berg, S. Nudge Theory Explored to Boost Medication Adherence. Chicago: American Medical Association. 2018. Available online: www.ama-assn.org/delivering-care/patient-support-advocacy/nudge-theory-exploredboost-medication-adherence.
Davenport T.H., Hongsermeier T., Mc Cord, K.A. Using AI to Improve Electronic Health Records. Harvard Business Review. 2018. Available online: https://hbr.org/2018/12/using-ai-to-improve-electronic-health-records
Li Y., Rao S., Solaresn J.R.A. Hassaine A., Ramakrishnan R., Canoy D., Zhu Y. Rahimi K., Salimi-Khorshidi G. BEHRT: Transformer for Electronic Health Records. Sci. Rep. 2020;10:7155.
Nuffield Council on Bioethics. Artificial Intelligence (AI) in Healthcare and Research. Nuffield Council on Bioethics. 2018. Available online: https://www.nuffieldbioethics.org/assets/pdfs/Artificial-Intelligence-AI-in-healthcare-and-research.pdf.
Caruana R., Loun Y., Gehrken J., Koch, P., Sturm M. Elhadad N. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia, 10 August 2015; ACM Press: Sydney, NSW, Australia, 2015; 1721–1730
Tiwari R. Explainable AI (XAI) and its Applications in Building Trust and Understanding in AI Decision Making. International J. Sci. Res. Eng. Manag. 2023;7:1–13.
Alvarez-Melis D., Jaakkola T.S. Towards robust interpretability with self-explaining neural networks. arXiv 2018, arXiv:1806.07538.
Giuste F., Shi W., Zhu Y., Naren T., Isgut M., Sha Y. Tong, L.Gupte M.,Wang M.D. Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review. IEEE Rev. Biomed. Eng. 2022;16:5–21.
Jadhav S., Deng G., Zawin M., Kaufman A.E. COVID-view: Diagnosis of COVID-19 using Chest CT. IEEE Trans. Vis. Comput. Graph. 2021;28:227–237. [C
Reddy S., Allan S., Coghlan S., Cooper P. A. governance model for the application of AI in health care. J. Am. Med. Inform. Assoc. 2019, 27, 491–497.
World Health Organization. Ethics and Governance of Artificial Intelligence for Health: WHO Guidance; World Health Organization: Geneva, Switzerland, 2021; p. 150. Available online: https://www.who.int/publications/i/item/9789240029200
Marwaha J.S., Landman A.B., Brat G.A., Dunn T., Gordon W.J. Deploying digital health tools within large, complex health systems: Key considerations for adoption and implementation. NPJ Digit. Med. 2022;5:13
Liao F., Adelaine S., Afshar M., Patterson B.W. Governance of Clinical AI applications to facilitate safe and equitable deployment in a large health system: Key elements and early successes. Front. Digit. Health 2022;4: 931439.
Brown R. Challenges to Successful AI Implementation in Healthcare. Data Science Central. 2022. Available online: https: //www.datasciencecentral.com/challenges-to-successful-ai-implementation-in-healthcare
Tachkov K., Zemplenyi A., Kamusheva M., Dimitrova M., Siirtolab P., Pontén J. Nemeth B., Kalo Z., Petrova G. Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries. Front. Public Health 2022;10:921226.
Marcus G., Deep learning: A Critical Appraisal. arXiv 2018. Available online: https://arxiv.org/abs/1801.00631
Kelly C.J, Karthikesalingam, A., Suleyman M., Corrado G., King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019, 17, 195.
Khan B., Fatima H., Qureshi A., Kumar S., Hanan A., Hussain J., Abdullah S. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomed. Mater. Devices 2023, 1–8.