Artificial Intelligence has immense potential to improve healthcare delivery. The article describes why it is going to be a game-changer. - Vishal Ranjan

The milestones achieved by Artificial Intelligence (AI) have the world on its toes. Apart from all the industries, it has been touching, the modern healthcare industry has been receiving paramount importance. There has been an exemplary shift in the way patients are diagnosed by doctors because they now have a good amount of actionable data that can be put to good use.

AI is the game changer in the healthcare industry. As per the reports by Frost & Sullivan, the consulting firm, the healthcare AI market is likely to experience a compound annual growth rate of 40 per cent by 2021, and it can change healthcare out¬comes by 30-40 per cent and cut treatment costs in half.

According to an analysis conducted by Accenture, AI applications in healthcare can create $150 billion dollars in annual savings for the US healthcare economy by 2026.

How AI is Changing Healthcare

The decisions made by medical practitioners can now be augmented by the extra layer of AI over the data. Training the code using this data reduces the likelihood of errors in the field of healthcare.

Electronic Health Records

Electronic Health Records are basically digital patient chart including information from multiple hospital encounters contained in an account which can be accessed across hospitals and facilities. EHR has all information including the disease, type of medicine and tests prescribed, results of the tests etc. Most of this data is fed manual at some point of time. But we can change it using AI by doing data extraction from free text (documents) using AI. We can also capture the clinical data by natural language processing.

Medical Imaging Diagnostics

AI plays a major role in enabling intelligence in the radiology images obtained through scanning machines. X-rays, CT scans and MRIs tells us about the body’s inner workings. The diagnostic imaging team, the pathologist and the doctors can reach a unanimous decision on the mode of treatment, and the chances of overcoming hurdles are very high. By making use of deep learning algorithms, it is now possible to distinguish between cancerous and non-cancerous cells in a much more precise way.

The radiologists and surgeons can now zoom into the problem, and study accurately, and do something more than what the human eyes could do.

Virtual Health Assistance

It is likely to increase patient engagement to the next level through Intelligent Virtual Assistant (IVA) and Medical Virtual Assistant (MVA). Today medical support has gone beyond wearables by advising patients to not just handle their goals, but also to actually assist them look after their health like a real assistant would such as medication reminders, provide medical advice for common ailments or complaints, suggest diet and eating habits, reminder for medication refill, remind doctor appointments and manage bookings, allow virtual interaction with doctors, chatbots to provide primary health and many such.

Robotic Assistance

The AI assistant can immediately deliver information on the patient’s past and present health and make recommendations that would help in the diagnosis. Surgeries have become minimally invasive techniques whereby hospital stay is considerably reduced, and thereby recovery of the patient. There are surgical bots that make use of computer vision to do surgeries after calculating the measurements of the human body precisely.

And the best part of all, the AI assisted equipment monitor the patients and their health levels, after the doctors, nurses and care takers have gone to rest or sleep. Human limitations will never be a problem in generating commendable patient outcome.

Proactive Medical Care

In conventional medical treatment, the drift was to treat the patient after the disease is identified. Now with AI, reactive medical care became proactive medical care. In this kind of care, the patient’s comprehensive medical history is studied and high-risk markers for several diseases are emphasised. At risk patients are then monitored for any variation in their conditions, and if anything seems alarming, the app can suggest medical intervention.

Benefits of Incorporating AI in Healthcare

Predictive medical care: A developing treatment model wherein the patient data is reviewed constantly to check for any anomalies, trailed by suggestions of medical intervention.

Personalised medication: AI makes it likely for patients to have custom-made care based on their body composition and past medical history.

Better diagnosis: Fast research and cross-referencing of data leads to improved diagnosis of diseases. The data also comprises handwritten notes, geospatial and sensor data and test results. Environment (both human and natural) influences are also considered.

Advanced treatment plans: New treatment means are generated and familiarised, including robotic surgery, cell biology, eye drops to dissolve cataracts instead of eye surgeries, wound healing by printing skin cells, 3D printing, artificial pancreas to balance blood glucose levels and administer insulin, and many such.

Non-stop monitoring: Uninterrupted monitoring of patients would make sure of timely care and treatment and even reduced hospital stay for the patients. The AI based app can check for the patient’s health and important signs in case of critically sick patients before notifying for medical intervention.

Economical for both patient and medical care provider: AI can make healthcare both effective and inexpensive as it helps in guiding treatment choice, making precise diagnosis, helps the patients in taking better decisions concerning their health and makes important decisions in drug development.

Virinchi AI ML Healthcare

Virinchi is currently using Machine Learning (ML) to improve the patient care in Virinchi Hospitals. With the help of its effective HIS software, the hospitals collect the patient data and analyse it. They have developed and trained the software for that analysis, which is trained with years of data. This software keeps on training itself as the data keeps on adding. Also, the patients are categorised into different depending upon their health reports and response to various events.


Emergency Decision: Using the current data on patients, even the nurses can comment on the criticality of the patient which is predicted by the software which tells us about the future situation of the patient by studying his/her present vitals. It helps them to judge what kind of primary care does the patient need and tells them beforehand about the immediate challenges they might face resulting into more time to act on those challenges. Depending on the same, if the software predicts that the patient is going to be in a critical situation or need some specific care/type of doctor in some time, he/she can be shifted to a better equipped hospital.

Preventive Medicines: By using data, the Patient Similarity Index is decided which gives information about the relative similarities and differences in patients. Using PSI, such data are divided into various cohorts. The patients in these particular cohorts show particular symptoms at a certain age or react in a similar way. This categorisation also tells about their lifestyle. Depending on these predictions, the patients are advised medications/precautions and also advised for regular check-ups related to symptoms of those predictions.

Radiology: The data of certain X-rays and scans are collected and mapped the irregularities with physical irregularities after carefully analysing them. Then the code is trained with the data and make predictions on future X-rays and scans. These predictions help the radiologists to generate the reports which help the nurses to understand what kind of primary care does the patient needs and also assists the doctors. These kind of AI/ML predictions are very helpful in complicated brain deceases like Alzheimer’s etc.