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The Right Choice: AI to drive the future of Indian healthcare according to GOI budget

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Emphasis on artificial intelligence (AI) for healthcare proves that the Government is serious about deep technology states Dr Vidur Mahajan, Head of Research & Development, Centre for Advanced Research in Imaging, Neurosciences & Genomics (CARING)

There is no debating the fact that India is grossly under-served as far as access, affordability and quality of healthcare services is concerned. With the Ayushman Bharat scheme kicking in last year, the Government attempted to solve the ‘affordability’ component, leaving access and quality of healthcare services to be solved by those providing the services. Not only did Nirmala Sitharaman, Finance Minister, Government of India announce an increase in the health budget by 10 per cent (taking it to Rs 70,000 crore), but also stated that artificial intelligence will play a key part in helping India achieve its healthcare goals, especially for Ayushman Bharat. This is truly visionary, and the fact that such a statement was part of her speech proves that the Government is now willing to walk the talk.

Below are a series of applications from Radiology, that are low hanging fruits to start implementing.

Tuberculosis screening using AI: Prime Minister, Narendra Modi has set a strong goal of eradicating tuberculosis from India by 2025. This monstrous goal is achievable only if a strong screening programme for TB, that involves active case finding. A simple X-ray of the chest has proven to be highly effective in finding patients who might have active tuberculosis, helping in the active case finding process. But unfortunately, since radiologists, who report these X-rays, are few, it becomes impossible to get such X-rays reported on time and patients are typically lost. Then comes AI – deep learning based algorithms can not only detect signs of tuberculosis in chest X-rays with a high degree of confidence, but can also do so in a few seconds! Patients can be instantaneously informed about the possibility of them having TB, and appropriate further action can be taken immediately. The Government already has thousands of X-rays scanners installed across the country – AI can be instantly deployed there and TB screening can be initiated – this is already happening in some parts of the country – Rajasthan and Chennai are two immediate examples.

Automatic reporting of scans: Significant progress is being made in the domain of automatic report generation for radiology scans by artificial intelligence. In a paper our group presented at RSNA 2019 (which also won the AuntMinnie’s Roadies Award for most viewed AI paper) we showed that AI generated text reports for chest X-rays were at par with the reports written by radiologists. This was a milestone because such artificial intelligence algorithms combine the diagnosis of disease, along with the description of the findings themselves. In our country, where unfortunately even in the largest Government hospitals, many scans go unreported, AI can step in and start reporting such scans providing much needed high-quality reports to patients in the Government sector.

Real-time quality monitoring: The dictum that ‘high-quality comes at high-cost’ is being challenged by the advent of AI. It is now possible for AI to ‘double check’ radiologists reports – whether for CT scans, X-rays or even MRI scans – and flag reports where there is a disconnect between what the AI and the radiologists say. Conventionally, radiologists would be required to review a certain sub-set of all reports, leading to incomplete / inadequate quality control – now with AI, every scan can be double-read!

Cancer screening: Whether it is automatically reading chest CT scans to screen for lung cancer, or automatically reporting mammography scans to look for early signs of breast cancer, or even automatically analysing colposcopic images for cervical cancer screening – AI is transforming the entire landscape of cancer screening. As the algorithms improve, and gain more traction, we will see an explosion in screening for tests. This will be driven almost entirely by a massive reduction in cost, which would lead to increased access to these technologies for patients, most importantly without any reduction in quality.

There are several other ways in which AI will come into diagnostics, especially imaging, and transform the care continuum, but as I mentioned, these are low hanging fruit which can literally be implemented today. While the AI algorithms exist today, either as open source tools or as commercially available products, but the limiting factor will be availability of a technology platform – how does the X-ray machine in the primary health centre, which may not have internet, get access to these algorithms? That is the sweet-spot in which we operate – last mile connectivity between the AI provider and the AI user, and we believe that for the Government’s well-intended move of investing in AI for healthcare for Ayushman Bharat to be successful, it is essential to have a unifying connecting platform, such as CARPL – the CARING Analytics Platform.

Again, I laud the Government for having such foresight, and am eager to play a role in the adoption of advanced technologies for the advancement of medical care across the country.

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