The post pandemic time saw a huge demand for specialized healthcare services and this is augmented with smart healthcare choices which are fast and improved, also accessible to a whole of population.
This is further amplified by AI driven healthcare solutions which can leverage predictive analytics and AI driven. If we see the statistics in India, AI was leveraged mainly for early detection and ramification of diseases.
The ratio of patient to doctor in India is 1:1456, which is comparatively low considering the huge population and this is a staggering low figure of just 64 doctors available per 100,000 people.
To have an effective healthcare system which is fast efficient and accessible, implementing and integrating AI into healthcare systems requires an understanding of AI and that can be inculcated in national curricula for medical and public health students, both academic and practical. This will give a better understanding and knowledge on AI usage and for research purpose.
AI expenditure in India increased by over 109% in 2018, and expected to reach $11.78 billion by 2025, adding $1 trillion to India’s economy by 2035.
AI in Indian Healthcare system
The government is giving its best efforts to integrate AI in Indian healthcare through various research and policy body. Presently AI is integrated into diagnostic algorithms for screening for diseases ranging from cancer, diabetic retinopathy, to cardiovascular disease.
NITI Ayog, think tank to government of India, has been testing the application of AI in primary care for early detection of diabetes complications, and is currently validating the use of AI as a screening tool in eye care, by comparing its diagnostic accuracy with that of retina specialists.
Tata Medical Centre and the Indian Institute of Technology (IIT) India’s first de-identified cancer image bank that comprehensively Archive Images. AI-based tools can use high-quality de-identified images to enable machine learning models to detect biomarkers and improve outcomes for cancer research.
Microsoft’s AI Network for Healthcare and Apollo Hospitals are developing a machine learning model to better predict heart attack risk. Using clinical and lab data from over 400,000 patients, the AI solution can identify new risk factors and provide a heart risk score to patients without a detailed health check-up, enabling early disease detection.
Many private Healthcare Start-ups and companies are coming up with innovative healthcare approaches using AI and many have achieved results and few in initial stages of implementation. The AI market in India is growing and its forecasted that by 2028 AI is projected to grow USD 102.7 Billion by 2028, at a CAGR of 47.6%.
Few organizations that are bringing in AI to healthcare system:
Niramai Health Analytix founded by Geetha Manjunath and Nidhi Mathurhas has developed Thermalytix that uses AI and a high-resolution thermal sensing device for the detection of breast cancer at an earlier stage.
Qure.ai a Mumbai based start up uses AI and deep learning algorithms to interpret and radiology images and scans like chest X-rays, head CT scans, POQUS, chest CT scans, within a couple of seconds. Founded by Prashant Warier and Pooja Rao in 2016 Qure.ai is one of the promising start up that aims to be more proficient in coming years.
HealthifyMe , started in 2012 in Bengaluru-is a health and wellness platform started by Founders Tushar Vashisht, Sachin Shenoy and Mathew Cherian . The aim of introducing digital healthcare to Indians and uses an AI-based virtual assistant, Ria.
Ria AI keeps in touch with its users and solves their queries around fitness, nutrition, and health in 10 different languages. Additionally, the app provides dietary recommendations, uses AI to track calorie intake and provides suitable healthy recipes and tips.
PharmEasy is Mumbai based AI healthcare, was founded in 2015 by Dharmil Sheth, Mikhil Innani and Dhaval Shah to develop an application connecting users with pharmacies. The smartphone-based app connects users with these pharmacies to make seamless medical deliveries.
The organization uses lot of data crunching and analytics solutions for the app. It has partnered with more than 80,000 pharmacies across 1200-plus cities in the country, serving more than five million customers.
InstaECG the flagship product is a cloud-connected device that helps interpret and analyse ECG reports within just 10 minutes. Another cardiac product that assists doctors in the quick and accurate echocardiogram diagnosis within a few hours of test.
The company has served in more than 12 countries, empowering 2,600-plus health workers, impacting more than three million patients and saving 90,000-plus lives. It was also awarded the NASSCOM Artificial Intelligence Game Changer Award in 2018
Recently Duke Health, a world-renowned academic medical centre, and SAS, a global analytics leader, recently have formalized a letter of intent to explore innovative and collaborative solutions together that will shape the future of health care via informed data and analytics.
The two organizations share a vision to develop new cloud-based and AI–powered products, focusing on health care solutions and services for improved care and delivery outcomes, business operations, and health services research.
Achieving AI Maturity
Achieving AI maturity in healthcare requires investment and research for research, workforce, data mining and training workforce. Partnership between private and government stakeholders is utmost important to create innovative business model.
AI research requires expansion of investments for future healthcare usage of AI models to better serve the customer and patients’ requirement.
The world economic forum in its annual meeting has categorically stated that
- AI in healthcare will increase by 2030 and AI will access multiple sources of data to reveal patterns in disease and aid treatment and care.
- Healthcare systems will be able to predict an individual’s risk of certain diseases and suggest preventative measures.
- AI will help reduce waiting times for patients and improve efficiency in hospitals and health systems.
There are many myths and prejudices surrounding AI implementation in healthcare but achieving accuracy in reports and cost deduction in the long run will bring in immense benefit during treatments.
This include treatments of various life threating diseases as AI has range of applications that can do multi-tasking and thereby reduce time required from patients waiting for test results to starting of treatment.
Nowadays machine learning and various algorithms have capability to analyse enormous quantity of data and diagnostic images, unlike earlier when note were made when analysing reports and clinical records.
If patients get access to timely treatment and doctors have information at hand on time, long waiting period gets over and brings in relief to patients and timely detection of diseases has a huge bearing on the procedure of treatments.
In case of various drug discoveries many research process happen which is time consuming affair and has cost. Machine learning (ML) has been used by many organization when it came to drug discovery in recent times that involves various permutation and combinations in chemical compounds and testing. This involves many rounds of test which are avoided in many cases, but usage of ML has no such restriction. Machine learning (ML) uses algorithms and previous loaded data and experiments, there by assisting in drug discovery which is less time consuming.
AI is penetrating deep into our life raising question about ethical usage of AI which has probability to replace humans. But at the same time we have to understand in healthcare major decisions are taken which have impact on life of patients. In this cases ethical usage and role of AI is paramount and have to be used judiciously.
With proper training of employees and new age digital assistants we will witness AI playing a greater and effective role in healthcare system.
We are yet to witness a lot when it comes to AI dependencies in healthcare system. There is lot more to overcome in terms of data privacy concerns for patient’s data, healthcare records and payment details, sufficient government intervention, lack of human insight and human and machine made errors.
(Image courtesy: www.forbes.com)