How could AI replace physicians?

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Main topic: Tech
Short answer:
  • Detection of anomalies
  • Prediction of potential illness
  • The use of 3D printing and AI for the planning of surgical procedures
  • Constant surveillance of the patients

As of 2022, many of AI's potential implementations are still in their development and need to be studied and developed better before they can fulfill their promise of revolutionizing the practice of medicine in ways that have not been seen before. For the sake of improving healthcare delivery to the general population, medical practitioners also need to have an understanding of these advancements and become familiar with them.[1]

Artificial intelligence can be divided into two categories:

  • Supervised learning
  • Unsupervised learning

Training on datasets that are accessible and have desired outputs is necessary for supervised learning. This training is used to develop a model, which is then utilized for making forecasts about the future. Classification can be used to make predictions about the final class labels, with both positive and negative findings being taken into account. Classification algorithms are used in the process of developing a prediction model based on historical data as well as indicating classifications of data that are unlabeled.[2]

Unsupervised learning involves putting together clusters out of groupings of data points that are quite similar to one another. Each cluster is made up of data points that have a number of properties in common.[3]

Mukhisa Kituyi with Sophia - AI for Good Global Summit-2018

The medical domains in which artificial intelligence technology can surpass human expertise[edit]

Detection of anomalies[edit]

  • Software that analyses medical images is only one example of how humans might put artificial intelligence's ability to spot irregularities to use.
  • Deep learning (DL) models have the potential to identify cancerous tumors on the ultrasound pictures with the same level of precision as a radiologist.
  • The only significant difference is that the researchers were able to train their models in only a few minutes, while it took radiologists several years to perfect their diagnostic abilities.[4]

Prediction of potential illness[edit]

  • Artificial intelligence is used to anticipate illnesses based on the data that is available for patients.
  • For testing and medical diagnosis, clinicians and medical labs are necessary; nevertheless, artificial intelligence-based predictive technologies are utilized for early illness diagnosis.
  • The study of biological processes, specifically the methods of information processing used by the human brain, serves as the basis for the development of artificial intelligence. The capabilities of artificial intelligence include the ability to learn, analyze data, recognize patterns, make predictions, and make decisions.
  • A machine learning system that may predict acute kidney damage up to forty-eight hours before it actually occurs, providing medical professionals with sufficient lead time to avert the condition.[5]
Imaging biomarkers from AI federated learning, Source: University of Sydney

The use of 3D printing and AI for the planning of surgical procedures[edit]

Clinicians may get insights that are necessary for optimal surgical planning with the use of 3D anatomical models. The discipline of medicine as a whole has advanced substantially over the last several decades, and 3D printing has made its way to diverse applications in a variety of subspecialties within the field.

The capacity of 3DP to turn two-dimensional imagery of a patient, which was previously only accessible on a flat screen, into a tactile 3D model that can be enjoyed in real space, touched, physically manipulated, and even deformed is the primary benefit of this technology. [6]

In radiology, it is necessary to divide two-dimensional pictures into many different labeled sections. The procedure could take as long as 10 hours to complete. However, with the use of machine learning, Axial3D can reduce this time to only a few minutes.

Constant surveillance of the patients[edit]

AI constant surveillance

The use of artificial intelligence makes it possible to guarantee that a service is accessible at all times of the day and night and that it is provided with the same level of quality and uniformity over the course of the day. When doing repeated jobs, AI technologies will not experience fatigue or boredom in the same way that humans do.[7]

An Internet of Things (IoT) enabled health monitoring device has been created using machine learning models to track a patient's actions such as running, resting, movement, and exercising, as well as different vital signs during these practices such as body temperature and heart rate, as well as the patient's breathing pattern while engaging in these activities. To distinguish between the various actions performed by the patient, machine learning models were used.

AI technology can coordinate the distribution of data, analyze trends, develop data consistency, give predictions, and quantify uncertainties, all of which are necessary steps in the process of making the most accurate judgments regarding patients.

The technology of remote patient monitoring makes it possible for medical professionals to rapidly conduct clinical diagnostics and prescribe treatments to patients without requiring the patients to present themselves at the hospital physically. The progression of infectious illnesses may also be tracked with the aid of AI, which can even predict the effects and outcomes of these diseases in the future.[8]

Why AI can't fully replace physicians[edit]

  • A technology is not capable of feeling empathy in the same way that a person is. It is not an issue of technology; instead, it is a question of how empathy should be interpreted. It is not so much a matter of thinking that a subsequent level of machine development may learn to have empathy already as it is by asserting that human experience cannot ever be recreated in its entirety.[9]
  • Robots are unable to cope with insufficient data, whereas machine learning models must be taught using actual examples. The more information they are provided with, the better their performance will be. Robots have not yet been programmed to handle situations in which there is insufficient data. And here is where humans come in, with their innate senses and their ability to think creatively outside the box.[10]
  • An environment that is consistent and predictable is optimal for the performance of AI systems. They can examine gigabytes of data in order to uncover trends, find "invisible" abnormalities on CT images, and locate movement in a patient ward. But what about more complicated jobs that need a certain order of steps to be completed?[11]

The field of medicine stands to gain a great deal from the use of artificial intelligence. It is able to automate operations that are repetitive and time-consuming and can swiftly handle large volumes of data. It is also accurate and accessible around the clock. On the other hand, human physicians are unrivaled in their capacities for empathy, inventiveness, and non-linear thinking. It is difficult to conceive of what advances may be made in medical treatment if physicians and AI can be combined."[12]


  1. Davenport, Thomas; Kalakota, Ravi (2019). "The potential for artificial intelligence in healthcare". Future Healthcare Journal. 6 (2): 94–98. doi:10.7861/futurehosp.6-2-94. ISSN 2514-6645. PMC 6616181. PMID 31363513.
  2. "Supervised vs. Unsupervised Learning: What's the Difference?". Retrieved 2022-10-30.
  3. Mishra, Sanatan (2017-05-21). "Unsupervised Learning and Data Clustering". Medium. Retrieved 2022-10-30.
  4. "Anomaly detection powered by AI". Dynatrace. Retrieved 2022-10-30.
  5. Tabata, Rena Christina. "Council Post: How AI Could Predict Medical Conditions And Revive The Healthcare System". Forbes. Retrieved 2022-10-30.
  6. Meyer-Szary, Jarosław; Luis, Marlon Souza; Mikulski, Szymon; Patel, Agastya; Schulz, Finn; Tretiakow, Dmitry; Fercho, Justyna; Jaguszewska, Kinga; Frankiewicz, Mikołaj; Pawłowska, Ewa; Targoński, Radosław (11 March 2022). "The Role of 3D Printing in Planning Complex Medical Procedures and Training of Medical Professionals—Cross-Sectional Multispecialty Review". International Journal of Environmental Research and Public Health. 19 (6): 3331. doi:10.3390/ijerph19063331. ISSN 1660-4601.
  7. "5 Forces for the Future: Artificial intelligence powers clinical surveillance". 7 October 2020.
  8. Taiwo, Olutosin; Ezugwu, Absalom E. (2020). "Smart healthcare support for remote patient monitoring during covid-19 quarantine". Informatics in Medicine Unlocked. 20: 100428. doi:10.1016/j.imu.2020.100428. ISSN 2352-9148. PMC 7490242. PMID 32953970.
  9. "Can a machine have empathy?". Retrieved 2022-10-30.
  10. Allen, Darrell M. West and John R. (2018-04-24). "How artificial intelligence is transforming the world". Brookings. Retrieved 2022-10-30.
  11. Razzak, Muhammad Imran; Imran, Muhammad; Xu, Guandong (2019-03-16). "Big data analytics for preventive medicine". Neural Computing and Applications. 32 (9): 4417–4451. doi:10.1007/s00521-019-04095-y. ISSN 0941-0643. PMC 7088441. PMID 32205918.
  12. "Pros & Cons of Artificial Intelligence in Medicine". College of Computing & Informatics. 2021-08-17. Retrieved 2022-10-30.