Artificial intelligence in medicine: its working, potentials and challenges




Artificial intelligence, Neural networks, Machine learning, Deep learning, Biomedicine, Future of medicine


Artificial intelligence (AI) is the science and engineering of making intelligent machines to think and learn. The interest and involvement of AI in health care has expanded fast during the last decade. The use of AI based technologies has been integrated into medicine to raise the standard of patient care by accelerating processes and achieving greater accuracy in different clinical settings. The patient’s electronic records, pathology slides and different radiological images are nowadays assessed by AI technologies such as machine learning (ML) and deep learning (DL). This, in turn, has aided in the process of diagnosis and treatment of patients and increased   physicians’ capabilities. AI is poised to transform medical practice in future. AI can aid clinicians to make accurate remedy choices, minimize needless surgeries, benefit oncologists to enhance patient’s chemotherapy regimen etc. The aim of this review is to primarily develop fundamental knowledge and awareness of AI among the healthcare professionals. The article mainly deals with the basic mechanism of AI, the recent scientific developments and applications of AI along with its risks and challenges in clinical setup.  



Author Biography

Rajeev Goel, Department of Biophysics, Dr. R. P. Govt. Medical College, Kangra at Tanda, Himachal Pradesh, India

Professor & Head

Department of Biophysics


Peng Y, Zhang Y, Wang L. Artificial intelligence in biomedical engineering and informatics: an introduction and review. Artif Intell Med. 2010;48:71-3.

Rajaraman V. John McCarthy-Father of artificial intelligence. Resonance. 2014;19(3):198-207.

Turing A. Computing machinery and intelligence. Mind. 1950;LIX(236):433-60.

Eliacik E. AI’s invisible hand on daily life. 2022. Available at: Accessed on 26 Nov, 2022.

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Med. 2019;25(1):44-56.

Rong L, Rong Y, Peng Z. A review of medical artificial intelligence. Global Health J. 2020;4(2):42-5.

Rowley R. The relationship between evidence-based and data-driven medicine. Available at: Accessed on 26 Nov, 2022.

Computational intelligence: A logical approach. Oxford University Press, NY. Available at: Accessed on 6 November, 2022.

Machine learning: An artificial intelligence approach. Springer-Verlag. 1983. Available at: Accessed on 6 November, 2022.

Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks. 2015;61:85-117.

Holzinger A, Langs G, Denk H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowledge. 2019;9(4)e:1312.

Peng Y, Zhang Y, Wang L. Artificial intelligence in biomedical engineering and informatics: an introduction and review. Artif Intell Med. 2010;48(2-3):71-3.

Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. J Appl Biomed. 2013;11(2):47-58.

Walczak S, Cerpa N. Artificial neural network. Chapter in Encyclopedia of Physical Science and Technology (Third Edition) Academic Press. 2003;631-45.

Le Cun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-44.

MIT Press. Deep Learning. Available at: Accessed on 8 Nov, 2022.

Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nature Biomed. Eng 2018; 2(10):719–731

Loukas S What is Machine Learning: Supervised, Unsupervised, Semi-Supervised and Reinforcement learning methods. Available at: Accessed on 10 June, 2022.

Ayodele TO. Types of machine learning algorithms. In: Zhang Y, ed. New Advances in Machine Learning. In Tech Open. 2010;19-48.

Nasteski V. An overview of the supervised machine learning methods. Available at: Accessed on 1 Dec, 2022.

Yuan KC, Tsai LW, Lee KH. The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Intl. Jl. Medical Inform. 2020;141:104176.

Sendak MP, Ratliff W, Sarro D. Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study. JMIR Med Inform. 2020;8(7):e15182.

Rasmussen CE. Gaussian processes for Machine Learning. In: Goos G, Hartmins J eds. Lecture Notes in Computer Science, Springer: 2004;63-71.

Matheny ME, Whicher D, Israni ST. Artificial intelligence in health care. JAMA. 2020;323(6):509-10.

Park CW, Seo SW, Kang N. Artificial intelligence in health care: current applications and issues. J Kor Med Sci. 2020;35(42):e379.

Hashimoto DA, Rosman G, Rus D. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70-6.

Burgess M. The NHS is trialling an AI chatbot to answer your medical questions. Wired. 2017. Available at: Accessed on 6 November, 2022.

Protrka R. The role of artificial intelligence in triage at the emergency department. Int J Integr Care. 2021;21(S1):A284:1-8.

Yokota H, Goto M, Bamba C. Reading efficiency can be improved by minor modification of assigned duties: a pilot study on a small team of general radiologists. Jpn J Radiol. 2017;35:262-8.

Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73-81.

Pratt MK. Artificial intelligence in primary care. Med Economics J. 2018;95(15):19.

Artificial Intelligence helps clinicians tell the complete story of the patient. Available at: Accessed on 6 November, 2022.

Industry leading CAPD that delivers unmatched Outcomes. Available at: Accessed on 6 November, 2022.

Kia deploys AI powered solution to screen foreign travelers for communicable diseases. Available at: Accessed on 6 November, 2022.

Philips Foundation deploys AI software in South Africa to detect and monitor COVID-19 using chest X-rays. Available at: Accessed on 6 November, 2022.

Murphy K, Habib SS, Zaidi SMA. Computer aided detection of tuberculosis on chest radiographs: an evaluation of the CAD4TB v6 system. Sci Rep. 2020;10:5492.

Yang Y, Yuan Y, Zhang G. Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals. Nat Med. 2022;1.

Adib F, Mao H, Kabelac Z. Smart homes that monitor breathing and heart rate. In Proc. of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015;837-46.

Popescu DM, Shade JK, Lai C. Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart. Nat Cardiovasc Res. 2022;1:334-43.

Galkin F, Kochetov K, Keller M. Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability. Aging. 2022;14(12):4935-58.

Sudhakar M, Rengaswamy R, Raman K. Multi-Omic data improve prediction of personalized tumor suppressors and oncogenes. Front Genet. 2022;13:854190.

Hughes JP, Rees S, Kalindjian SB. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239-49.

Ekins S. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater. 2019;18(5):435-41.

Nag S, Baidya ATK, Mandal A. Deep learning tools for advancing drug discovery and development. 3Biotech 2022;12:110.

Ho DSW, Schierding W, Wake M. Machine learning SNP based prediction for precision medicine. Front. Genet. 2019;10:267.

Saha S. Genomic data can predict miscarriage and IVF failure. 2022. Available at: Accessed on 6 November, 2022.

Jumper J, Evans R, Pritzel A. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583-9.

10 Best artificial intelligence companies that are advancing health care industry. 2022. Available at: Accessed on 6 November, 2022.

Savage N. Breaking into the black box of artificial intelligence. Nature. 2022.

Lakhani P, Sundaram B. Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574-82.

Mutasa S, Sun S, Ha R. Understanding artificial intelligence based radiology studies: What is overfitting? Clin Imaging. 2020;65:96-99.

Kohli A, Mahajan V, Seals K. Concepts in U.S. Food and Drug Administration regulation of artificial intelligence for medical imaging. Am J Roentgenol. 2019;213(4):886-8.

Coppola F, Faggioni L, Gabelloni M. Human, all too human? An all-around appraisal of the “Artificial Intelligence Revolution” in medical imaging. Front Psychol. 2021;12:710982.

Char DS, Shah NH, Magnus D. Implementing machine learning in health care-Addressing ethical challenges. N Engl J Med. 2018;378(11):981-3.

Paranjape K, Schinkel M, Panday RN. Introducing artificial intelligence training in medical education. JMIR Med Educ. 2019;5(2):e16048.

Finlayson SG, Bowers JD, Ito J. Adversarial attacks on medical machine learning. Science. 2019;363(6433):1287-9.

Sindhu V, Nivedha S, Prakash M. An empirical science research on bioinformatics in machine learning. J Mech Cont Math Sci. 2020;7:86-94.

Raj R. Supervised, unsupervised, and semi-supervised learning with real-life use case. Available at: Accessed on 6 November, 2022.






Review Articles