– Ed

Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input.

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. Cognitive computing is a subfield of AI that strives for a natural, human-like interaction
with machines.

Medical artificial intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict the results.

Every year, nearly 440,000 Americans die due to medical errors.
According to different medical research reports, hospital errors are one of the leading causes of a patient’s death. (Ref: A New, Evidence-based Estimate of Patient Harms Associated with Hospital Care James, John T. PhD – Journal of patient safety)

A total of 3 832 patients died last year in Gauteng public hospitals as a result of 10 741 serious adverse events (SAEs), which largely arise from avoidable medical negligence. This information was disclosed by Gauteng Health MEC Bandile Masuku in a written reply to DA Gauteng health spokesperson Jack Bloom’s questions in the Gauteng legislature. As per the healthcare report, nearly 86% of the mistakes in the healthcare industry are preventable.

Now, AI promises innovation in the healthcare system for a better future. A study by Frost & Sullivan states the AI can significantly augment the quality of healthcare by 30% to 40%.


AI makes an educated decision based on all the data it has been over. Be it medical research paper, theories, journals, online books, or anything – it gathers information from everywhere. It does its research in a matter of seconds. And then it provides an educated decision as per the patient’s health-related data. Thus, the chances of medical errors get reduced. AI assists in the medical field to diminish the casualties and errors mentioned above. Simultaneously, it cuts the treatment costs in half. So, yes! We need AI in Healthcare.

While diagnosing a patient, getting doctors to consider suggestions from an AI-built system can be a bit difficult. Based on medical expertise, knowledge, intuition, problem-solving skills, and experience, doctors make decisions. An AI deciding the best-suited deal for you in Amazon is not the same thing as suggesting the best medical action for a patient! It is not so unnatural for people to think of AI as a threat to the doctors. This is why we need to introduce AI literacy in the medical field. Thus, people can think of it as a blessing to the medical sector.

Medical Imaging Analysis

Using AI in radiology is another significant impact of AI in healthcare industry. The diagnosis processes become seamless in this way. With medical imaging analysis, examining medical images like CT scans, X-rays, MRI, etc. becomes easier. Also, it can provide feedback on what a human eye can miss. So, medical imaging analysis reduces the chances of errors, and makes the diagnosis process more effective and accurate.

Diabetic retinopathy

It is estimated by the World Health Organization (WHO) that 80% of all vision impairment can be prevented or cured. Early diagnoses of ocular disease is key to the prognosis.
The problem is the cost of professional intervention, as well as the comparative shortage of skills. AI software systems now exists that can diagnose retinal pathologies, which will radically reduce cost and spread the service far and wide. All that is required is a fundus photo (of back of the eye), taken by ancillary staff. The photo is mailed to a central service unit where the diagnoses is made swiftly by means of AI. This technology paves the way for NGOs and governmental screenings to become far more effective and reduce cost dramatically.


AI is already helping us to diagnose diseases more efficiently, develop drugs, personalise treatments, and even edit genes. But this is just the beginning. The more we digitise and unify our medical data, the more we can use AI to help us find valuable patterns – patterns we can use to make accurate, cost-effective decisions in complex analytical processes.

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