Knowledge Update

Emerging Applications of AI in Medicine

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An emerging technical field called artificial intelligence (AI) investigates, develops, and applies the theory, approach, method, and system for replicating, enhancing, and expanding human intellect. The "father of artificial intelligence," physicist Alan Turing, initially proposed the idea in 1950; he created the "Turing test" and claimed that AI was more sophisticated than the human brain but similar to it (Mintz and Brodie, 2019; Kaul et al., 2020).

Deep learning, a new generation of AI technology has evolved in recent years. This subset of computer algorithms can automatically gain knowledge from and then artificially and independently conclude the data. Various neural networks, including the deep belief network, convolutional neural network, long- and short-term memory network, etc., are included in deep learning (Kulikowski, 2019). According to AI research, the output-input ratio in medicine is more promising than it is in other industries (Mintz and Brodie, 2019). The marriage of artificial intelligence with medicine transforms the conventional medical model and creates a breakthrough advancement. 

Medical diagnosis with AI

The time needed to make a diagnosis and the diagnostic effectiveness can both be dramatically increased when a doctor uses AI to diagnose a patient with a specific sickness or condition. By examining clinical radiology data like X-rays, CT scans, MRIs, pathology, and exams using ultrasonography, endoscopy, and biochemistry for relevant bodily indications in humans, the technology can replace the inefficient conventional medical model, to provide conclusions that are prompt and reliable, especially for complex diagnoses. Because AI can resolve problems so quickly, doctors may create a more intentional and acceptable treatment plan depending on the patient's situation.

Use of AI in Medical Treatment

In surgery: AI in surgical procedures has gained traction as AI technology advances. PUMA560, Probot, AESOP, Robodoc, and Acrobat were helpful surgical assistants in the modern period. With sharper vision, more practical and precise operation, even remote operation, and other benefits, these technologies represent a tremendous invention unmatched in human history and reduce the amount of surgical intervention. Through this innovative technology, formerly challenging minimally invasive techniques can now be used to undertake complex surgical operations (Stefano, 2017).

Perioperative period: This is the time frame surrounding the full procedure, from the time the patient is receiving surgical care until recovery. It is divided into three sections: the preoperative phase, the surgical period, and the postoperative recovery period. Numerous successes with the use of AI technologies have been made across the entire perioperative period. These applications are observed in anesthesiology assistance, three-dimensional printing (3DP) and rehabilitation assistance (Peng-ran et al., 2021).

AI in Drug Production

Due to the lengthy process involved in producing drugs according to the conventional model—which includes clinical trials, testing, and promotion, drug ingredient design studies, functional target studies, and performance tests—new medications may not always perform as well as anticipated even after extensive research. But as AI has advanced recently, it has altered the healthcare sector's traditional drug industry and made it easier to find and assemble new drugs (Bajorath et al., 2020; Brown et al., 2020). The novelty and quality of medicines from the AI generation have also reached new heights as they have continuously evolved. Clinical trial procedures have been accelerated, and the time and expense of research and development have dropped owing to predictive AI algorithms with vaccine formulation (Russo et al., 2020; Zhavoronkov, 2020).


Scientists are already using AI to develop pharmaceuticals, customize therapies, and even modify genes more effectively. But this is only the start. We can utilize AI to assist us to identify important patterns that we can use to make precise, economical decisions in complex analytical procedures the more we digitize and combine our medical data.


Bajorath J, Kearnes S, Walters WP. (2020). Artificial Intelligence in Drug Discovery: Into the Great Wide Open. J Med Chem, 63(16):8651-8652.

Brown N, Ertl P, Lewis R. (2020). Artificial intelligence in chemistry and drug design. J Comput Aided Mol Des, 34(7):709-715.

Kaul V, Enslin S, Gross SA. (2020). The history of artificial intelligence in medicine. Gastrointest Endosc, 92(4):807-812

Kulikowski CA. (2019). Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art - with Reflections on Present AIM Challenges. Yearb Med Inform, 28(1):249-256

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

Peng-ran, L., Lin, L., Jia-yao, Z., Tong-tong, H., Song-xiang, L. and Zhe-wei, Y. (2021). Application of Artificial Intelligence in Medicine: An Overview. Current Medical Science 41(6):1105-1115.

Russo G, Reche P, Pennisi M. (2020). The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opin Drug Discov, 1-15

Stefano GB. (2017). Robotic Surgery: Fast Forward to Telemedicine. Med Sci Monit, 23:1856

Zhavoronkov A. (2020). Medicinal Chemists versus Machines Challenge: What Will It Take to Adopt and Advance Artificial Intelligence for Drug Discovery? J Chem Inf Model, 60(6):2657-2659.


Miracle Uwa Livinus is a Lecturer II in the Department of Biochemistry, School of Science and Information Studies at Skyline University Nigeria. He has M.Sc in Nutritional Biochemistry from Nasarawa State University

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