Artificial Intelligence (AI) is prevalent in business and is now applied in healthcare. The rise of healthcare data implies the increasing use of Artificial Intelligence (AI). The key areas where Artificial Intelligence is being applied are early disease diagnosis, drug design process, drug trials, diabetic retinopathy, cancer treatments, cardiovascular disease, and eye care.
AI expenditure in India is expected to reach $11.78 billion by 2025. It is expected to add $1 trillion to India’s economy by 2035. The huge growth in investment, particularly in AI is being done due to a lot of reasons. India has just 64 doctors available per 100,000 people compared to the global standard of 150 doctors per 100,000 people. Primary Healthcare centers in rural areas lack infrastructure. This translates to a lack of high-quality diagnostic services in rural India which is home to more than 70% of the population. Even in Urban areas, the impact of technology has been modest. Online doctor consultations received a big hike during the COVID - 19 but the impact has not been as transformational as expected.
Integration of AI into healthcare is not a one-day dream. It requires the government to introduce AI Courses and AI-based curriculum in schools and colleges, especially medical and health science colleges. Leveraging predictive analysis using Artificial Intelligence (AI) for early detection or diagnosis can become an asset in the healthcare industry, especially in rural India where there is a lack of basic infrastructure and even healthcare professionals. AI-based technologies can help bridge the gap between supply and demand in the healthcare industry in India which is expected to become a $372 billion industry this year.
The Role of Artificial Intelligence (AI) in Healthcare
AI expenditure in India increased by over 109% in 2018, making it a $665 million industry. It is expected to reach $11.78 billion by 2025, adding $1 trillion to India’s economy by 2035.
AI is already being integrated into diagnosis and early detection methods. NITI Aayog, a public think tank, policy, and program framework for the Indian government has been testing the application and implementation of AI in the early detection of diabetes and is currently working on the use of AI as a screening tool in eye care. The aim here is to integrate AI-based technologies with portable screening devices that can accelerate eye screening and early diagnosis, especially in rural and remote areas.
AI-based technologies are being tested for cancer research. AI-based tools can use high-quality de-identified images to detect biomarkers. Recently, Tata Medical Center and the Indian Institute of Technology launched India’s first de-identified cancer image bank: the Comprehensive Archive of Imaging.
AI can use public health data to identify risk factors associated with cardiovascular disease. Microsoft’s AI Network for Healthcare and Apollo Hospitals are developing a machine learning model to predict the potential risk of heart attacks 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 the need for a detailed health check-up.
Predictive analysis using AI is also used in the drug design process. AI has the potential to accelerate the hit-to-lead stage of early drug discovery and provide accurate results of the drug target.
Challenges while Integrating AI into Healthcare
The primary requirement to integrate AI in healthcare is public health data, which is also one of the potential risk factors. AI-based tools and technologies require massive amounts of patient data. Fragmented or inaccurate data can increase the risk of inaccurate decisions like inappropriate drug prescriptions or disease detection. Thus, patients need to realize how their data is being used to train the AI models and they must provide accurate data.
The healthcare industry should also realize that the integration of AI in healthcare is just not the end. A high level of automation based on AI could compromise the ability of physicians to accurately detect mistakes in any stage of AI integration and lead to an overreliance on AI-based tools.
AI should support healthcare decision-making, not be used to automate decision-making. AI-based tools should never be replaced with primary healthcare, but should instead help in providing advanced tools and infrastructure to rural populations.
Conclusion
AI integration into healthcare requires critical investments in the workforce, infrastructure, regulatory mechanisms, stakeholders, and business models.
This also requires the integration of AI into the school curriculum for medical and paramedical students. The Indian government will also require to make appropriate investments in the basic healthcare infrastructure. Government must also invest in public and private organizations like NITI Aayog, Tata Institute of Healthcare, Apollo Hospitals, etc. to facilitate the research being done to integrate AI-based solutions into healthcare.
India’s adoption of AI into healthcare can enable it to bridge the imbalance between the increasing rural population and the lack of basic healthcare infrastructure. India can become a front-runner among emerging markets and can meet its sustainable development goals in the near future.
