Rajneesh De, Consulting Editor, APAC News Network
The Indian healthcare sector has been growing rapidly and as per Nasscom, the market size of was valued at $372 billion in 2022. This is expected to grow at a CAGR of nearly 20% from 2023 to 2030. While the pandemic pushed rapid advancements in technology in the healthcare sector, it also propelled the AI related investment in the domain leading to much better and efficient care management.
AI, without doubt, today is rapidly transforming the Indian healthcare industry, bringing in hitherto unprecedented tools for diagnosis, treatment and patient care. The overall AI expenditure in India is expected to reach $11.78 billion by 2025 and consequently add $1 trillion to India’s economy by 2035, as per World Economic Forum. More relevantly, the AI in Healthcare Market is projected to grow from $14.6 Billion in 2023 to $102.7 Billion by 2028.
There is a gap in workforce though to match this potential. The NITI Aayog report on the National Strategy for Artificial Intelligence shows that shortage of qualified healthcare professionals and non-uniform accessibility to healthcare across the country. India has only 64 doctors available per 1,00,000 people compared to the global average of 150. Can AI sufficiently support the healthcare infrastructure to bridge this gap?
Components of AI used by Indian healthcare
- Machine Learning (ML): ML enhances performance with increased exposure to relevant information. The system discovers patterns and attempts to make estimates. Healthcare companies like SigTuple and Niramai are currently leveraging ML.
- Robotics: Robotics facilitate the execution of routine tasks without further interventions. Bengaluru-based Alpha Care, a healthcare startup, employs robotics to simplify patient services and streamline operational processes. Quite a few hospitals across the country are currently using robotics-based surgery.
- Image Recognition: This technology recognises patterns in medical imaging for effective and timely diagnosis and treatment. Healthcare startup Artelus uses AI-powered image recognition for effective and early diagnosis.
- Speech Recognition: Many have adopted voice recognition technology to provide easy-to-access transcripts of medical records. Augnito seeks to convert human voice to written text and further empowers healthcare practitioners to minimize errors and save time.
- Natural Language Processing (NLP): NLP can help scan clinical data and identify the right disease. Ahmedabad-based healthcare startup Maruti Techlabs employs AI-driven NLP for text processing to gauge the patient’s healthcare needs.
- Rule-based Expert Systems: These expert systems are widely employed in healthcare as effective clinical decision support systems.
AI helps in managing a hospital very effectively and also accelerate the pace of diagnoses and treatments with low-cost solutions. This effectively frees up the valuable time of the doctors, surgeons and nurses and enables them to offer better quality patient care. The AI-based tools are also leveraged effectively in ancillary activities too like hospital billing, documentation, revenue and medical record management.
“AI adaptation in healthcare is still in its early stages. But it is leading us toward a promising future,” opines Niraj Garg, Head of Digital and Automation, Siemens Healthineers. “AI aids in saving time by automating mundane and routine tasks within the daily clinical routine. These time-saving measures allow healthcare professionals to focus more on critical decision-making and delivering quality patient care,” Garg adds.
For instance, chatbots help patients to raise their queries regarding appointments, bill payments, and more. Virtual health assistants help in answering patients’ queries via calls and emails, scheduling appointments with doctors, sending follow-ups and clinical appointment reminders to patients, etc.
“We have almost 100 million old people in the country,” informs Mudit Dandwate, CEO & Co-founder of Dozee. “All the high-risk disease population is increasing. And when we look at the population of doctors or nurses, it is not really increasing. In such a scenario AI can play a big part to ensure quality care reaches everyone.”
Assessing the successful AI in healthcare use cases
There is already a wide array of application and schemes in healthcare where AI has proved its mettle beneficially in India. These health areas and applications include
- Mining medical records.
- Designing health treatment plans.
- Predicting early detection of life-threatening diseases.
- Screening of cancer and diabetes patients.
- Using AI-based tools for eyes and opthalmic treatments.
- Managing Chronic Obstructive Pulmonary Disease (COPD) diagnosis
- Screening of Covid-19 patients
- Arriving at accurate and fast health diagnostic results.
- Helping in the drug designing process.
- Predicting timely outcomes for pregnant women and newborns
- Monitoring consistently newborns identified as low birth weight.
Google, Microsoft and IBM have multiple partnerships with private hospital groups such as Narayana, Apollo and Fortis, as well as partnerships with state governments in India. These are working on a range of solutions, including AI systems for hospital management, disease detection and prediction, as well AI service delivery in remote areas
Chaitanya Raju, executive director, HealthPlix Technologies agrees. “AI models often help with early detection of patients at high risk of contracting complex chronic diseases. This assists doctors in early treatment of these diseases and thus lowers the patient burden of surgery care, specialists.”
An example illustrates this AI-healthcare jugalbandi further. In the area of cardiovascular healthcare, Microsoft’s AI Network for Healthcare and Apollo Hospitals are developing a ML model to predict the risk of heart attacks. Using the clinical and lab data from more than 4 lakh patients, the AI solution can identify new risk factors and provide a heart risk score to patients without even a detailed health check-up. This can enable early disease detection often leading to prevention.
Similar applications have been developed in oncology too. The Tata Medical Center and IIT Bombay launched India’s first de-identified cancer image bank called the Comprehensive Archive of Imaging. AI-based tools can use high-quality de-identified images to enable machine learning models to detect biomarkers and improve outcomes for cancer research.
Columbia Asia Hospitals in Bengaluru employs AI to automate processes, allowing doctors to record each detail the physician and patient communicate, providing visibility into trends. These predictive analysis helps in the early detection of any ailments and aids in treating life-threatening conditions.
A non-profit AI based healthcare start-up, Wadhwani AI is an official AI partner of Central Tuberculosis (TB) Division. It is developing various interventions related to the TB patient care and helping India’s National TB Elimination Program (NTEP) become AI-ready. In fact, different state governments are getting involved in various AI-related healthcare initiatives.
The Maharashtra government has signed an MoU with NITI Aayog to unveil the International Centre for Transformational Artificial Intelligence (ICTAI), focusing on rural healthcare. The Karnataka government launched “Healthcare Pods” developed by the Bangalore-based Vevra. These pods are innovative movable hospitals integrated with AI and help in containment of contagious diseases such as Covid-19 and TB. The Telangana state government has adopted the Microsoft Intelligent Network for Eyecare, and has been a pioneer in launching Covid-19 live monitoring App for Telangana.
Even the healthcare startups are looking at more effective leverage of AI. Staqu is a Gurugram-based startup that uses an AI-based thermal camera to identify a person with a body temperature above 37 degrees Centigrade. The camera identifies multiple suspects parallelly whoever are within a range of 100 meters. Another AI-powered medical technology healthcare startup Aindra employs an AI platform, Astra. It helps detect critical illnesses such as cancer. Aindra has developed a point-of-care detection system for cervical cancer that facilitates affordable and faster detection.
Pharmeasy offers an AI-based application that connects users with pharmacies. It uses a smartphone-based application that facilitates the seamless delivery of medicines. This application uses ML and tools for big data analysis. HealthifyMe, a health-related digitally based wellness platform in Bengaluru has adopted Ria, an AI-powered virtual app that helps users and answers their questions about fitness, nutrition, and health in ten languages.
Dissecting the AI interventions in detail
In the overall analysis, there are certain areas in which AI interventions are being developed. Disease detection and diagnostics is one of them. ML is being used to build decision-support systems for diagnostics, as well as in predictive systems for prognostication. Computer vision and DL models are being used to read medical scans such as X-rays, CT scans, PET scans and ultrasound scans. SigTuple is using an AI platform called Manthana for automated analysis of blood smears as well as for the digitization of blood, urine and semen samples. Platforms such as OnliDoc and Lybrate are also using AI methods to provide virtual assistance and diagnostics remotely. OnliDoc uses AI for symptom checking and treatment selection.
ML processes are being developed to create new efficiencies in areas such as hospital bed management and processing of insurance claims. ML is also being developed for bed management and planning, to predict rates of ‘patient churn’ (turnover of beds), in order to optimize the use of beds in hospitals.
In addition, a few start-ups and initiatives have begun to provide personalized health solutions. Bengaluru-based startup Healthi uses predictive analytics, personalization algorithms and ML to deliver personalized health suggestions. Similarly, Manipal Hospitals is using IBM Watson for Oncology, a cognitive-computing platform, to help physicians discover personalized cancer care options. The online platform mfine handles more than 15,000 patient interactions per month using chatbots.
Reminding the challenges of AI
While AI offers remarkable capabilities, it would be futile to suggest that it could replace the expertise, intuition, and compassion of healthcare professionals. “Amidst the awe-inspiring AI advancements, one fundamental truth remains. The skill of healthcare professionals will continue to be indispensable in delivering excellent care for patients,” asserts Nivedita Verma, Hospital Director, Surya Mother & Child Super Speciality Hospital, Pune.
“While AI algorithms can process vast amounts of data and identify patterns, the expertise, intuition, and compassion of healthcare professionals make a critical difference,” she added. For example, social, economic and historical factors can play into appropriate recommendations for particular patients. An AI system may be able to allocate a patient to a particular care center based on a specific diagnosis. However, it may not account for patient economic restrictions or other personalized preferences.
Nevertheless whatever AI advancements we talk about, the apprehension about the cost challenges remain. After all, it is the challenge of prohibitive costs that still prevent the uniform spread of healthcare across India to all the socio economic strata. Experts though opine that AI can make healthcare services affordable and accessible in the remotest areas efficiently.
Explains Dandwate,“ Continuous monitoring is only available in ICU beds right now, which is only 1,00,000 beds out of close to about 20 lakh beds in the country. What AI can do is it can monitor any bed at 1/10th the cost of a normal ICU cost. Therefore, in the cost of putting one ICU bed, now you can put 10.”
