Artificial IntelligenceHealth TechHealthcare

How Can AI Tackle Indian Healthcare Challenges


Indian Healthcare has unique challenges, and experts are using Artificial Intelligence to tackle those challenges.

India has gone through a digital revolution. Today almost every Indian has heard of UPI, which is a home-grown technology that transfers money from the bank directly to the recipient bank, with almost nil charges. But can India replicate the same success as the healthcare industry?

India is a very vast country with a population of over 1 billion. The amount of data it can provide to train an AI is enormous, which can be used to grow the medical industry at a rapid pace. Can India use AI to develop its healthcare system to cater to the various demographics, or it will fail in its attempt?

Opportunities in India

India has room for innovative, sustainable, and scalable healthcare technology to improve lives as there still prevails vast inequalities in healthcare distribution, a shortage of skilled healthcare clinicians and infrastructure, and low public spending on healthcare. Yet, in a country with a population of 1 billion people, many of whom now have access to the internet and smartphones, it is difficult to come up with more than a few examples of digital technologies that have had a big impact on healthcare outcomes or are extensively used.

Challenges in India

Many healthcare challenges are still in the early stages of technological development, each one with millions of potential beneficiaries in India. Antibiotic resistance, health insurance, communicable diseases such as malaria and tuberculosis, as well as developing diseases like diabetes are just a few of the many topics worth considering through the lens of today’s technology, connectivity, and artificial intelligence.

While AI has the potential to overtake several other technologies, digitalization is required for AI to be employed at any scale. Given that many Indian health centers still utilize paper medical records and radiology usually uses films (although this is changing rapidly). Although the rate of change is quick, statistics on the digitization of records, prescriptions, and radiography are difficult to come by.

How Can AI help tackle these challenges?

Artificial Intelligence revolutionises healthcare in the future because of its capacity to handle data quickly. They not only aid in discoveries, but they also assist doctors in detecting hidden/dormant indicators of diseases that may show in some patients. Having an AI-enabled system as a backup reduces medical errors and boosts medical practitioners’ efficiency. However, AI is not intended to replace humans, but rather to provide a neutral alternative. Because an AI system is programmed, it can only give a clinician its opinion on a diagnosis, treatments available, and potential risks and consequences for certain steps.

Today’s healthcare professionals are frequently inundated with too much data, and these sophisticated computer tools can help them sort through it all to find the important facts. For example, the perfect AI system could foresee the consequences of patients overusing antibiotics even before the doses are given. This has aided a growing country like India, whose demographic is primarily rural and whose reliance on medicine is at an all-time high.

Progress Made.

Dedicated infrastructure and technology for the unique difficulties of the Indian health ecosystem have also been seen in the last decade. Products for tuberculosis drug adherence tracking (one of India’s most serious public health challenges), low-cost vital parameter monitoring for use in primary care, and telemedicine systems that provide clinical expertise to places where doctors are few are among them. These are more advanced than artificial intelligence applications that have emerged in the last five years. AI applications comprise algorithms that evaluate chest X-rays as well as other radiology pictures, read ECGs and find abnormal patterns, autonomously scan pathology slides, and even examine fundus images for indicators of retinopathy. They are mostly used for screenings, monitoring, and diagnostic aid.


Experts view India as a “data source” for radiology and ophthalmology imaging, among other things. English-language reporting, privately-held healthcare institutions, and permissive privacy, data security, and ownership regulations are all contributing issues. Although data sharing may not always be a matter of concern, the lack of clarity and regulation surrounding it should set off alarms.

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