Artificial Intelligence Applications in Healthcare Sector: Ethical and Legal Challenges

Emna Chikhaoui, Alanoud Alajmi, Souad Larabi-Marie-Sainte


Recently, artificial intelligence (AI) has been one of the hottest topics in the technological world. Although it is involved in many domains, it was recently involved in the healthcare sector. AI can be used for diagnostics, drug development, treatment personalization, gene editing, disease prediction, and many more. It helps to improve healthcare services by benefiting medical professionals, hospitals, and patients. Saudi Arabia has a particular interest in the healthcare sector, and it has a clear vision for the future, which points toward the development of AI-based technologies. Few studies investigated the use of AI in Saudi healthcare, and most of them focused on healthcare employees' perceptions. This study is beyond the focus of the existing works. It aims at: 1) presenting the main AI-based healthcare applications; 2) exploring the use of AI in the Saudi healthcare sector; 3) addressing their ethical and legal challenges, along with the policy questions in Saudi healthcare; 4) studying the benefits of these AI-based applications and the acceptance of professionals to use AI in daily practice; 5) introducing the new Personal Data Protection Law (PDPL) in Saudi Arabia; and 6) discussing the importance of AI to the future of Saudi healthcare. To this purpose, a survey was distributed among four main Saudi hospitals. The findings showed that AI should not only lead to better health but also save manpower and simplify the healthcare processes. The respondents agreed that AI helps reflect human intellectual competencies and pushes its limits.


Doi: 10.28991/ESJ-2022-06-04-05

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Artificial Intelligence; Machine Learning; Deep Learning; Healthcare; Privacy; Data Protection; Ethics; Data Transfer; Intellectual Property.


McCarthy, J. (2004). What is Artificial Intelligence?. Computer Science Department, Stanford University, California, United States. Available online: 2009/Old/IntelligentSystems_2005_2006/Documents/Symbolic/04_McCarthy_whatisai.pdf (accessed on January 2022).

Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harvard Journal of Law & Technology, 29(2). doi:10.2139/ssrn.2609777.

Larabi-Marie-Sainte, S., Aburahmah, L., Almohaini, R., & Saba, T. (2019). Current techniques for diabetes prediction: Review and case study. Applied Sciences, 9(21), 4604. doi:10.3390/app9214604.

Gallego, V., Naveiro, R., Roca, C., Ríos Insua, D., & Campillo, N. E. (2021). AI in drug development: a multidisciplinary perspective. Molecular Diversity, 25(3), 1461–1479. doi:10.1007/s11030-021-10266-8.

Fröhlich, H., Balling, R., Beerenwinkel, N., Kohlbacher, O., Kumar, S., Lengauer, T., Maathuis, M. H., Moreau, Y., Murphy, S. A., Przytycka, T. M., Rebhan, M., Röst, H., Schuppert, A., Schwab, M., Spang, R., Stekhoven, D., Sun, J., Weber, A., Ziemek, D., & Zupan, B. (2018). From hype to reality: Data science enabling personalized medicine. BMC Medicine, 16(1), 150. doi:10.1186/s12916-018-1122-7.

Marie-Sainte, S. L., Saba, T., Alsaleh, D., & Alamir Alotaibi, M. B. (2019). An improved strategy for predicting diagnosis, survivability, and recurrence of breast cancer. Journal of Computational and Theoretical Nanoscience, 16(9), 3705-3711. doi:10.1166/jctn.2019.8238.

Saba, T., Khan, M. A., Rehman, A., & Marie-Sainte, S. L. (2019). Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction. Journal of Medical Systems, 43(9). doi:10.1007/s10916-019-1413-3.

Iftikhar, S., Fatima, K., Rehman, A., Almazyad, A. S., & Saba, T. (2017). An evolution based hybrid approach for heart diseases classification and associated risk factors identification. Biomedical Research (India), 28(8), 3451–3455.

Elsayed, H. A. G., Galal, M. A., & Syed, L. (2017). HeartCare+: A Smart Heart Care Mobile Application for Framingham-Based Early Risk Prediction of Hard Coronary Heart Diseases in Middle East. Mobile Information Systems, 2017. doi:10.1155/2017/9369532.

Larabi-Marie-sainte, S., Alskireen, R., & Alhalawani, S. (2021). Emerging applications of bio-inspired algorithms in image segmentation. Electronics, 10(24), 3116. doi:10.3390/electronics10243116.

Moezzi, M., Shirbandi, K., Shahvandi, H. K., Arjmand, B., & Rahim, F. (2021). The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis. Informatics in medicine unlocked, 24, 100591. doi:10.1016/j.imu.2021.100591.

Padhy, S., Takkar, B., Chawla, R., & Kumar, A. (2019). Artificial intelligence in diabetic retinopathy: A natural step to the future. Indian Journal of Ophthalmology, 67(7), 1004–1009. doi:10.4103/ijo.IJO_1989_18.

Abdullah, R., & Fakieh, B. (2020). Health care employees’ perceptions of the use of artificial intelligence applications: Survey study. Journal of Medical Internet Research, 22(5), e17620. doi:10.2196/17620.

Alelyani, M., Alamri, S., Alqahtani, M. S., Musa, A., Almater, H., Alqahtani, N., Alshahrani, F., & Alelyani, S. (2021). Radiology community attitude in saudi arabia about the applications of artificial intelligence in radiology. Healthcare (Switzerland), 9(7). doi:10.3390/healthcare9070834.

Larabi-Marie-Sainte, S., & Ghouzali, S. (2020). Multi-objective particle swarm optimization-based feature selection for face recognition. Studies in Informatics and Control, 29(1), 99–109. doi:10.24846/v29i1y202010.

De-Arteaga, M., Herlands, W., Neill, D. B., & Dubrawski, A. (2018). Machine learning for the developing world. ACM Transactions on Management Information Systems, 9(2), 1–14. doi:10.1145/3210548.

Bini, S. A. (2018). Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? Journal of Arthroplasty, 33(8), 2358–2361. doi:10.1016/j.arth.2018.02.067.

Ongsulee, P. (2017, November). Artificial intelligence, machine learning and deep learning. 15th International Conference on ICT and Knowledge Engineering (ICT&KE), 1-6. IEEE. doi: 10.1109/ICTKE.2017.8259629.

Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. doi:10.1016/j.drudis.2020.10.010.

Lamanna, C., & Byrne, L. (2018). Should artificial intelligence augment medical decision making? The case for an autonomy algorithm. AMA journal of ethics, 20(9), 902-910. doi:10.1001/amajethics.2018.902.

Anderson, M., & Anderson, S. L. (2019). How should AI Be developed,validated and implemented in patient care? AMA Journal of Ethics, 21(2), 125–130. doi:10.1001/amajethics.2019.125.

Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Science, 14(1), 86–93. doi:10.1111/cts.12884.

Choi, H. S., Choe, J. Y., Kim, H., Han, J. W., Chi, Y. K., Kim, K., Hong, J., Kim, T., Kim, T. H., Yoon, S., & Kim, K. W. (2018). Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles. BMC Geriatrics, 18(1), 1-12. doi:10.1186/s12877-018-0915-z.

Weerakoon, W. M. N. B., Vasanthapriyan, S., & Ishanka, U. A. P. (2019). A Queuing Model for Outpatient Department to Reduce Unnecessary Waiting Times. 2019 IEEE 14th International Conference on Industrial and Information Systems: Engineering for Innovations for Industry 4.0, ICIIS 2019 - Proceedings, 203–208. doi:10.1109/ICIIS47346.2019.9063348.

Johannessen, K. A., & Alexandersen, N. (2018). Improving accessibility for outpatients in specialist clinics: Reducing long waiting times and waiting lists with a simple analytic approach. BMC Health Services Research, 18(1), 1–13. doi:10.1186/s12913-018-3635-3.

Cudney, E. A., Baru, R. A., Guardiola, I., Materla, T., Cahill, W., Phillips, R., Mutter, B., Warner, D., & Masek, C. (2019). A decision support simulation model for bed management in healthcare. International Journal of Health Care Quality Assurance, 32(2), 499–515. doi:10.1108/IJHCQA-10-2017-0186.

Kumar, A., Rahman, M., Trivedi, A. N., Resnik, L., Gozalo, P., & Mor, V. (2018). Comparing post-acute rehabilitation use, length of stay, and outcomes experienced by Medicare fee-for-service and Medicare Advantage beneficiaries with hip fracture in the United States: A secondary analysis of administrative data. PLoS Medicine, 15(6). doi:10.1371/journal.pmed.1002592.

Ahuja, A. S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. PeerJ, 7, e7702. doi:10.7717/peerj.7702.

Jyotiyana, M., Kesswani, N. (2020). Deep Learning and the Future of Biomedical Image Analysis. In: Dash, S., Acharya, B., Mittal, M., Abraham, A., Kelemen, A. (eds) Deep Learning Techniques for Biomedical and Health Informatics. Studies in Big Data, vol 68. Springer, Cham. doi:10.1007/978-3-030-33966-1_15.

Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy and Allied Technologies, 28(2), 73–81. doi:10.1080/13645706.2019.1575882.

Golden, J. A. (2017). Deep learning algorithms for detection of lymph node metastases from breast cancer helping artificial intelligence be seen. JAMA, 318(22), 2184–2186. doi:10.1001/jama.2017.14580.

Suh, Y. J., Jung, J., & Cho, B. J. (2020). Automated breast cancer detection in digital mammograms of various densities via deep learning. Journal of personalized medicine, 10(4), 211. doi:10.3390/jpm10040211.

Marie-Sainte, S. L., Saba, T., Alsaleh, D., & Alamir Alotaibi, M. B. (2019). An improved strategy for predicting diagnosis, survivability, and recurrence of breast cancer. Journal of Computational and Theoretical Nanoscience, 16(9), 3705-3711. doi:10.1166/jctn.2019.8238.

Dreyer, K. J., & Raymond Geis, J. (2017). When machines think: Radiology’s next frontier. Radiology, 285(3), 713–718. doi:10.1148/radiol.2017171183.

Sandhu, H. S., Eltanboly, A., Shalaby, A., Keynton, R. S., Schaal, S., & El-Baz, A. (2018). Automated diagnosis and grading of diabetic retinopathy using optical coherence tomography. Investigative Ophthalmology and Visual Science, 59(7), 3155–3160. doi:10.1167/iovs.17-23677.

Laaki, H., Miche, Y., & Tammi, K. (2019). Prototyping a Digital Twin for Real Time Remote Control over Mobile Networks: Application of Remote Surgery. IEEE Access, 7, 20235–20336. doi:10.1109/ACCESS.2019.2897018.

Hidar, T., Kalam, A. A. El, Benhadou, S., & Mounnan, O. (2021). Using Blockchain based Authentication Solution for the Remote Surgery in Tactile Internet. International Journal of Advanced Computer Science and Applications, 12(2), 277–281. doi:10.14569/IJACSA.2021.0120235.

Papa, A., Mital, M., Pisano, P., & Del Giudice, M. (2020). E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technological Forecasting and Social Change, 153, 119226. doi:10.1016/j.techfore.2018.02.018.

Kuziemsky, C., Maeder, A. J., John, O., Gogia, S. B., Basu, A., Meher, S., & Ito, M. (2019). Role of Artificial Intelligence within the Telehealth Domain. Yearbook of Medical Informatics, 28(1), 35–40. doi:10.1055/s-0039-1677897.

Peng, X., Long, G., Shen, T., Wang, S., & Jiang, J. (2021). Self-attention Enhanced Patient Journey Understanding in Healthcare System. Lecture Notes in Computer Science, 719–735. doi:10.1007/978-3-030-67664-3_43.

Price, I. I., & Nicholson, W. (2017). Artificial intelligence in health care: applications and legal issues. 14 SciTech Lawyer, 14(1), University of Michigan, Michigan, United Stated.

Karliuk, M. (2018). Ethical and Legal Issues in Artificial Intelligence. International and Social Impacts of Artificial Intelligence Technologies, Working Paper, Russian International Affairs Council (RIAC): NPMP, Moscow, Russia, 43-49.

Khisamova, Z. I., Begishev, I. R., & Gaifutdinov, R. R. (2019). On methods to legal regulation of artificial intelligence in the world. International Journal of Innovative Technology and Exploring Engineering, 9(1), 5159–5162. doi:10.35940/ijitee.A9220.119119.

Čerka, P., Grigiene, J., & Sirbikyte, G. (2015). Liability for damages caused by artificial intelligence. Computer Law and Security Review, 31(3), 376–389. doi:10.1016/j.clsr.2015.03.008.

Samarkandi, A. (2006). Status of medical liability claims in Saudi Arabia. Annals of Saudi Medicine, 26(2), 87–91. doi:10.5144/0256-4947.2006.87.

Kingston, J. K. (2016, December). Artificial intelligence and legal liability. International conference on innovative techniques and applications of artificial intelligence, 269-279. Springer, Cham. doi:10.1007/978-3-319-47175-4_20.

Terzi, D. S., Terzi, R., & Sagiroglu, S. (2016). A survey on security and privacy issues in big data. 2015 10th International Conference for Internet Technology and Secured Transactions, ICITST2015, 202–207. doi:10.1109/ICITST.2015.7412089.

Zhu, Tianqing, Dayong Ye, Wei Wang, Wanlei Zhou, and Philip Yu. “More than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence.” IEEE Transactions on Knowledge and Data Engineering (2021): 1–1. doi:10.1109/tkde.2020.3014246.

Almeida, F. (2018). Big data: Concept, potentialities and vulnerabilities. Emerging Science Journal, 2(1), 1–10. doi:10.28991/esj-2018-01123.

Su, G. (2018). Unemployment in the AI age. AI Matters, 3(4), 35–43. doi:10.1145/3175502.3175511.

Ping, H., & Yao ying, G. (2018). Comprehensive View on the Effect of Artificial Intelligence on Employment. Multidisciplinary Inclusive Education Management and Legal Services, 1(1), 32–35. doi:10.26480/ismiemls.01.2018.32.35.

Bruun, E. P., & Duka, A. (2018). Artificial intelligence, jobs and the future of work: Racing with the machines. Basic Income Studies, 13(2). doi:10.1515/bis-2018-0018.

Nilsson, N. J. (1985). Artificial intelligence, employment, and income. Human Systems Management, 5(2), 123–135. doi:10.3233/hsm-1985-5205.

Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019). Artificial intelligence, bias and clinical safety. BMJ Quality and Safety, 28(3), 231–237. doi:10.1136/bmjqs-2018-008370.

Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491–497. doi:10.1093/jamia/ocz192.

Parish, J. M. (2015). The Patient Will See You Now: The Future of Medicine is in Your Hands (Editor. Eric T.). Basic Books: New York. Journal of Clinical Sleep Medicine, 11(06), 689–690. doi:10.5664/jcsm.4788.

Osoba, O. A., & Welser IV, W. (2017). An intelligence in our image: The risks of bias and errors in artificial intelligence. Rand Corporation, Santa Monica, United States. doi:10.7249/RR1744.

Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, 295–336. doi:10.1016/B978-0-12-818438-7.00012-5.

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DOI: 10.28991/ESJ-2022-06-04-05


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