The impact of artificial intelligence in medicine on the future role of the physician

The impact of artificial intelligence in medicine on the future role of the physician

Table of Contents


The practice of medicine is changing with the development of new Artificial Intelligence (AI) methods of machine learning. Coupled with rapid improvements in computer processing, these AI-based systems are already improving the accuracy and efficiency of diagnosis and treatment across various specializations. The increasing focus of AI in radiology has led to some experts suggesting that someday AI may even replace radiologists. These suggestions raise the question of whether AI-based systems will eventually replace physicians in some specializations or will augment the role of physicians without actually replacing them. To assess the impact on physicians this research seeks to better understand this technology and how it is transforming medicine. To that end this paper researches the role of AI-based systems in performing medical work in specializations including radiology, pathology, ophthalmology, and cardiology. It concludes that AI-based systems will augment physicians and are unlikely to replace the traditional physician-patient relationship.


Artificial intelligence, Machine learning, Impact, Medicine, Physician, Deep learning, Radiology, Pathology, Opthalmology, Oncology, Cardiology


The term ‘‘Artificial Intelligence’’ (AI) was first coined by John McCarthy for a conference on the subject held at Dartmouth in 1956 as ‘‘the science and engineering of making intelligent machines’’ (Society for the Study of Artificial Intelligence and Simulation of Behavior, 2018). After a period of reduced funding and interest in AI research, also referred to as the AI winter (Crevier, 1993), optimism in AI has generally increased since the low point in the early 1990s. Artificial intelligence (AI) is an important field of computer science that seeks to create complex machines with characteristics of human intelligence. We can think of this concept as ‘‘General AI,’’ which has machines that can think and reason and even see and hear like humans (Copeland, 2016). This concept which can be seen in movies like Star Wars (think C-3PO, a droid programmed for etiquette and protocol) is not something we can achieve at this time. However, what is achievable at this time falls under the concept of ‘‘Narrow AI’’ where technologies exist to perform specific tasks as well as, or better than, humans can (Copeland, 2016). Examples of such narrow AI include speech recognition, facial recognition, etc. These technologies exhibit certain facets of human intelligence. Such intelligence is derived from AI techniques known as machine learning and deep learning which have improved performance in areas such as image classification, text analysis, speech, and facial recognition with a range of promising applications such as autonomous vehicles, natural language processing, and in medicine.

AI is poised to play an increasingly prominent role in medicine and healthcare because of advances in computing power, learning algorithms, and the availability of large datasets (big data) sourced from medical records and wearable health monitors. The health care market for AI is increasing at a rate of 40% and is expected to reach $6.6 billion by 2021 (Frost & Sullivan, 2016). Computing power is increasing rapidly due, in part, to the wide availability of Graphics Processor Units that make parallel processing even faster and the availability of seemingly infinite computing resources on-demand in the cloud. Big data is also well supported by practically endless storage in the cloud. Learning algorithms are becoming more precise and accurate as they interact with training data, allowing newer insights into diagnostics, treatment options, and patient outcomes (Bresnick, 2018b). The flood of health care data is helping push the development of new AI applications that promise to improve the efficiency and effectiveness of patient care. Healthcare-related big data is available from sources such as Electronic Medical Records (EMR) and wearable health trackers, which can be analyzed in new ways. The rise of AI in the era of big data can assist physicians in improving the quality of patient care and provide radiologists with tools for improving the accuracy and efficiency of diagnosis and treatment. AI is well-suited to handle repetitive work processes, managing large amounts of data, and can provide another layer of decision support to mitigate errors. The research firm Frost & Sullivan estimates that AI has the potential to improve patient outcomes by 30% to 40% while reducing treatment costs by up to 50% (Hsieh, 2017a).

Experts predict AI to have a significant impact in diverse areas of health care such as chronic disease management and clinical decision making (Bresnick, 2016). While still in the early stages of adoption, AI algorithms are showing promise in specializations such as radiology, pathology, ophthalmology, and cardiology (Hsieh, 2017a). This progress raises a thought-provoking question. Will AI at some point displace certain physicians such as radiologists or will it help make them more effective or will it be a bit of both? This research looks at the potential uses of AI in medicine and considers the possibility of AI replacing certain physicians or at least supplementing the role of physicians. The rest of this paper is organized as follows. A survey of the literature is provided in the ‘Literature Survey’. ‘Artificial Intelligence vs. Machine Learning vs. Deep Learning’ provides a discussion on AI, Machine Learning, and Deep Learning and how they relate to each other. ‘Promise of AI in Modern Medicine’ discusses the promise of AI in medicine across various specialties such as radiology, pathology, cardiology, and ophthalmology. ‘Assessing the Impact of AI on Physicians’ assesses the impact of AI on physicians. Conclusions are provided in ‘Conclusions’.


The avalanche of medical data in the form of clinical, genomic, and imaging data is only likely to accelerate as precision and personalize medicine matures. Consequently, for the foreseeable future medicine in the future medicine is likely to be even more data-dependent with the synergy between medicine and AI technology becoming more pronounced. In recognition of this important trend in modern medicine, medical schools are strengthening their emerging technology curricula. New courses are being offered by medical schools in technology infrastructure, ML, DL, and data management alongside their biology classes (Dyche, 2018).

AI will support the future needs of medicine by analyzing the vast amounts and various forms of data that patients and healthcare institutions record in every moment. AI is likely to support and augment physicians by taking away the routine parts of a physician’s work hopefully enabling the physician to spend more precious time with their patients, improving the human touch. While AI is unlikely to replace physicians in the foreseeable future, it is incumbent on medical professionals to learn both the fundamentals of AI technology as well as how AI-based solutions can help them at work in providing better outcomes to their patients. Or, it might come to pass that physicians who use AI might replace physicians who are unable to do so.

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The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

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FULL Paper PDF file:

The impact of artificial intelligence in medicine on the future role of the physician




Abhimanyu S. Ahuja Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, United States of America




The impact of artificial intelligence in medicine on the future role of the physician

Publish in

PeerJ 7:e7702



PDF reference and original file: Click here

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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