☕ A Coffee Break Read On: Artificial Intelligence x Healthcare In India 🥼💊
In India, the adoption of technology in the healthcare sector is not as fast as in sectors like fintech (digital payments), IT services, travel & hospitality. AI is one of the upcoming technologies that can have a significant impact on healthcare in terms of quality, cost, and remote availability — all of which are very necessary in India. As a Dentist and Healthcare professional, I intend to help bring more technology into this segment by identifying various technology-related investment opportunities leading to better health outcomes. The following deep dive is the first of a series of pieces I will be writing in this space — long enough to give you a comprehensive overview of a topic, short enough to read in your coffee break…
🔑 Key Drivers Behind The Adoption Of AI In Indian Healthcare
India is home to a population of 1.42 billion. With such a massive population comes several challenges in each sector, including providing healthcare. Whilst I could write a book on this subject, I have distilled some key trends and points to consider that should last your coffee break so that you have a holistic overview of healthcare x AI investment opportunities across India within 18 minutes.
In this article, I explore methods being developed to find newer techniques or reframe older ones — once thought of as optional exercises — these have now become critical to the future of healthcare.
As you start to sip your latte/ cappuccino, here’s a visual I’ve put together of some of the key players in the Indian healthcare ecosystem including their investors.
Please reach out to me on LinkedIn or Twitter if your company is missing from this map or if you raised a new round. :)
Okay… now that you’ve got some context, let’s go! 🚀
🦠 The Impact Of Covid On AI Adoption Across India
COVID-19 highlighted discrepancies in an already saturated public and private sector. While it has been dabbled with for several years, it is only after COVID-19 that it has gathered more attention and put a global spotlight on AI. Every nation is making efforts to include AI in Healthcare and India is no exception. AI expenditure in India is expected to reach US$ 11.78 billion by 2025, adding over $1 trillion to India’s economy by 2035, as per a World Economic Forum report. The Global AI in Healthcare Market is projected to grow from $14.6 Billion in 2023 to $102.7 Billion by 2028.
👵🏽👴🏽 A Growing Elderly Population Accelerating The Need For AI In Healthcare
India has a large elderly population of 7.1% (100+ million) which is the most in need of healthcare. Of this, 60 % is in rural areas, where the reach of healthcare is even less.
Mudit Dandwate, CEO and founder of Dozee said. “All the high-risk disease population is increasing. And when we look at the population of doctors or nurses, it is not increasing and we already have a deficit in India. Now in such a scenario, how do we ensure quality care reaches everyone? And I believe that is somewhere AI can play a big part.”
Moreover, over the next few decades, as the next segment of the population (50%) ages, the need for health care will grow even more dramatically.
Do we have enough resources to provide quality healthcare to such a large pool of population a few years down the line?
💰 Why Should You Invest In Indian Healthtech Now?
According to analysts from Transparency Market Research, the global digital healthcare market will be worth nearly $536 billion by 2025. In 2019, PWC estimated that India ranks among the top 10 global growth markets for healthcare and nutrition.
Investing in healthcare AI in India at this point could be quite beneficial for several reasons.
- Growing demand for healthcare services
- Digital Transformation in Healthcare
- Government Initiatives
- Growing interest by VCs in this sector
Significant investments are being poured into AI start-ups. As R&D continues, any breakthrough in technology will not only help the local market but will also likely attract global attention. Given the size of the worldwide healthcare market and the simplicity of promoting technological solutions on a global scale, these factors contribute to making Indian investment an attractive and rewarding prospect for VC.
🩺 Impact Of AI In Healthcare
AI has the potential to transform healthcare in various ways. It can turn large amounts of patient data into actionable information, improve public health surveillance, accelerate health response, and streamline and expedite research and development.
AI has potential benefits in healthcare, including medical record analysis, treatment design, health event forecasting, support for repetitive tasks, clinical decision-making assistance, medication management, drug development, and promoting healthier choices.
Likewise, it is highly beneficial in areas where human resources are scarce, especially in rural and remote areas. AI can help in:
- creation of electronic health data repositories which have adequate high-quality annotated health data for machine learning applications
- creation of a national-level clinical decision support system that could enable better management of routine clinical problems by less-skilled healthcare providers
- creation of self-learning digital systems in fields such as radiology, genomics, and pathology to enhance the future of healthcare
Broadly, the uses of AI in healthcare can be categorized into:
Moreover, AI can act as a valuable guide for newly qualified physicians, supplementing their limited practical experience. With AI’s assistance, they can reach the correct diagnosis and provide effective treatment more quickly. In this way, AI serves as an educational tool, accelerating the learning process for new doctors.
Dr.Naresh Trehan, Chairman and Managing Director of Medanta — states that AI has become an instrumental tool in healthcare, offering significant support in the field of diagnosis and treatment. It empowers doctors by providing a comprehensive overview of potential diagnoses, calculated from available data. This computational ability not only increases the accuracy of diagnosis but also assists in formulating optimal treatment plans.
However, the notion that AI can help lower qualification criteria for doctors requires additional scrutiny. AI can supplement a physician’s knowledge and decision-making process, but cannot replace the fundamental skills, knowledge, and empathetic understanding that a human physician brings to patient care. Thus, while AI may expedite the learning process, the qualifications and rigorous training required for medical professionals should remain uncompromised.
With the development of more technology and AI, healthcare can eventually be delivered at a lower cost because when efficiency is increased, diagnostics will be more focused.
With the help of data, we can predict the people who are likely to face health issues. We can get a skeletal picture as the robot measures every muscle in your body and instructs the preventive steps that patients need to take to avoid further health complications.
👩🏾⚕️ Potential Use Cases & Application Of AI In Healthcare In India
1. Cancer Screening
India is witnessing a surge in cancer cases, with around 1 million new diagnoses annually. The country’s limited number of experienced pathologists, radiologists & oncologists highlights the potential role AI could play in improving diagnosis. For example,
In the image above, two identical black-and-white pictures of murky shapes sit side-by-side on a computer screen. On the left side, Ismail Baris Turkbey, MD a radiologist with 15 years of experience, has outlined an area where the fuzzy shapes represent what he believes is a creeping, growing prostate cancer. On the other side of the screen, an artificial intelligence (AI) computer program has done the same -– and the results are nearly identical. Courtesy: National Cancer Institute
“The AI model finds the prostate and outlines cancer-suspicious areas without any human supervision,” Dr. Turkbey explains. He hopes that AI will help less experienced radiologists find prostate cancer when it’s present and dismiss anything that may be mistaken for cancer.
Doctors utilize imaging tests in various ways, from whole organ X-rays to microscopic images of cancer cells. These tests help detect cancer in its initial stages, determine the stage of a tumor, evaluate the effectiveness of treatments, and monitor for any recurrence after treatment.
💭 Things To Ponder On
The success of AI in this field hinges on access to high-quality pathology datasets.
NITI Aayog is working towards establishing a National repository of well-annotated, curated pathology images. The establishment of an Imaging Biobank for Cancer is currently under discussion.
A partnership between Tata Medical Center and the Indian Institute of Technology led to the creation of India’s first de-identified cancer image bank, known as the Comprehensive Archive of Imaging. These high-quality images can be used by AI tools to identify biomarkers, enhancing cancer research outcomes.
Diabetic retinopathy screening
NITI Aayog, Microsoft, and Forus Health are working together on a pilot project to create an AI algorithm for early diabetic retinopathy detection, compare its diagnosis to family physicians, and integrate it with Microsoft’s retinal imaging APIs into the ‘3Nethra’ device for usage in remote areas with low cloud connectivity.
Promising startups like Niramai, Qure.ai, and Oncostem are making notable strides within this particular sector.
2. 🫁Chronic Obstructive Pulmonary Disease Diagnosis and Management
AI-specialised spirometers and analytics can provide early warnings for COPD by identifying triggers, symptoms, and trends. Inhaler-based AI solutions monitor the effectiveness of drug delivery and track patient compliance with medication regimens. AI-enhanced lung imaging, employing CT Scans, X-rays, and Computational Fluid Dynamics tools, assists pulmonologists in visualizing lung structures and functions.
Microsoft and Apollo Hospitals have partnered to form an AI-focused network in cardiology.
Dr. Sangita Reddy, Joint MD, Apollo Hospital
“We built a partnership where they bring in the technology and we bring in the data, algorithms, and clinical insights into what is impacting and how this can translate into number one risk scoring and then in differential patterns and methodologies” (Dr. Sangita Reddy, Joint MD, Apollo Hospitals)
3. Tuberculosis (TB) Diagnosis 🩻
The Central TB Division of India’s Ministry of Health partnered with the Wadhwani Institute for Artificial Intelligence to incorporate AI in the fight against Tuberculosis, focusing on vulnerability mapping, improved screening methods, diagnostics, caregiver decision support, and overall AI readiness.
X-ray and CT scans are primary tools for diagnosing pulmonary diseases.
Accurate image interpretation requires significant experience. In a country like India, where doctors have less than a minute to give to each patient. And when the patient volumes are high & the timing is critical doctors need reliable tools. That’s where artificial intelligence applications come in handy. Advanced image recognition algorithms identify subtle patterns in X-ray images. Works faster and helps in patient classification and triage.
4. 🦷 AI in Dentistry
Over the past 8 years in healthcare, I have witnessed the changes happening in the dental field. Sometimes, there are differences in opinion, also trust issues could arise with two doctors giving two different treatment plans for the same conditions, hence leaving patients confused. However, with advancements in the field, dentists now have a strong basis to rely on the final diagnosis which is supported by the application of AI.
For instance, radiography, intraoral scans, and facial scans often present dental practitioners with a quantity of overwhelming and unstructured data.
The good news?
AI-driven dental imaging software can help make sense of this data quickly and efficiently.
ML is already being used for:
1. Automated interpretation of dental images (radiographs, CBCT, and MRI scans)
2. Treatment recommendations
3. Future dental disease predictions.
And the best part?
Machine learning algorithms also proved to outperform dentists in diagnosing tooth decay or predicting whether a tooth should be extracted, retained, or have restorative treatment. Take a look at the breadth of ML research across a variety of dental fields.
The experience of Indian healthcare professionals, particularly in dentistry, highlights the impact of AI tools. They’ve improved diagnosis, treatment accuracy, and disease prediction, often exceeding the skills of seasoned dentists. This success and the rising use of AI in India’s healthcare sector make a compelling case for VC investments.
5. AI for Digital Pathology 🧑🔬
Digital Pathology is an emerging sub-division of conventional microscopy, enabling practitioners to virtualize glass pathology slides for more in-depth analysis. It converts regular microscope slides into a digital format, and AI assists in analyzing and understanding these digital images. It helps in a thorough examination of tissue samples. AI can match current cases with previous ones. It improves the accuracy of diagnosis and facilitates early disease detection.
Diving you all into some of the most promising startups in AI in Healthcare with their Name, Focus Areas, Funding, and Investors.
A) Qure.ai
AI in diagnosis — provides an automated interpretation of radiology exams like X-rays, CTs, and MRIs. The startup aims to create personalized cancer treatment plans & provide affordable, accessible healthcare solutions that can assist professionals in detecting diseases.
Total of $40M in funding over 3 rounds.
Their latest funding was raised on monarchy 30, 2023, from a Series C round of $16M
Investors: HealthQuad, MSD, Novoholdings, Fractal, Sequoia, MassMutual Ventures, TeamFund
B) SigTuple
Automated Digital Microscopy -SigTuple democratizes microscopy by automating it through advanced AI and robotics. AI-assisted digital microscopy, enabled through the cloud, takes the drudgery out of the current process.
It currently offers five products: 1. Shonit- automated blood smear analysis,
2. Shrava- urine microscopy analysis
3.Ai100- specimen analysis for in-vitro diagnosis,
4. Semen analyzer
5. SaaS platform for diabetic retinopathy screenings.
Total of $45M in funding over 6 rounds.
Their latest funding was raised on March 1, 2023, from a Series C round of $4.19M
Investors: Accel, Endiya Partners, Chiratae Ventures, Trifecta Capital Advisors, Pi Ventures, iLabs Capital, Venture Highway
C) Oncostem
Cancer treatment: An AI startup in the cancer treatment space for breast & ovary cancer, uses machine learning and AI.
First, algorithms analyze a sample of a patient’s tumor to predict the likelihood of recurrence, labeling patients as low or high risk. These algorithms assist healthcare providers in assessing and leveraging personalized cancer treatments.
Product: CanAssist Breast has been launched for hormone-positive breast cancer.
Total of $9M in funding over 6 rounds.
Their last funding was raised on Sept 6, 2017, from a Series A round of $6M
Investors: Peak XV partners, Artiman Ventures
D) Healthify Me
Health & Fitness app: With the help of AI, the app monitors calorie intake and gives dietary recommendations, tips, and nutritious recipes. Moreover, HealthifyMe has an AI assistant called Ria which solves user queries about fitness and health in 10 languages
Total of $130M in funding over 10 rounds.
Their recent funding was raised on June 7, 2023, from a Series D round of $30M
Investors: Khosla Ventures, Health Quad, Blume Ventures, Chiratae Venture, Sistema Asia Funds, Unilever Ventures
E) Niramai
Early detection of Breast Cancer:
Niramai has developed an innovative thermal analytics-based Breast Cancer solution for early-stage breast cancer detection. It’s able to identify malignant cells sooner and at a lower cost than traditional diagnostics.
The system is automated, portable, and detects cancer cells faster than even a self-examination. Plus, it’s a radiation-free, non-invasive system.
Total of $14M in funding over 8 rounds.
Their recent funding was raised on June 7, 2023, from a Series D round of $30M
Investors: Karnataka Startup Cell, Pi Ventures, Ankur Capital, BEENEXT, Binny Bansal — Angel Investor
F) Tricog
Accelerating Cardiac Health: Provides virtual cardiac diagnostic tools and services that help them with accurate timely heart conditions.
Its product portfolio includes InstaECG, a cloud-connected device that analyses and interprets ECG reports within 10 minutes, and InstaEcho, an AI-powered device that helps doctors get an accurate and fast echocardiogram for diagnosis of issues like heart failure.
Total of $30M in funding over 9 rounds.
Their recent funding was raised on April 20, 2023, from a Series B round of $8.5M
Investors: Sony Innovation Fund, UTEC, SGInnovate, Inventus partners, Omron Healthcare
G) Consure Medical
Critical Care Technology: The product is a novel device for the management of fecal incontinence in bedridden patients, a condition affecting nearly 100 million people worldwide.
Total of $36M in funding over 6 rounds.
Investors: Accel, Indian Angel Network
H) Aknamed
Supply Chain (simplify Procurement): A cloud and AI-based material management startup, that combines machine learning tools designed to analyze purchase and consumption data with automation. Products are tracked through their entire life cycle, simplifying procurement and making it one of India’s largest supply-chain-focused platforms in the healthcare space. It provides an infrastructure to keep hospitals in good operational standing and makes it easier to find waste areas.
Total of $7M in funding over 1 round. Series A
Investor: Light rock
I) Brainsight AI
Brain Mapping: It is an AI-based SaaS platform to enable greater precision in neurological and psychiatric investigation. Provide two AI-based solutions, Voxelbox and Snowdrop. Voxelbox — fMRI processing engine helps in generating reports & aids in clinical decision-making.
Snowdrop — Patient care app
Their recent funding was raised on July 10, 2023, and raised $9.9M as convertible notes.
Investor: RedStart Labs
J) Healthplix
EMR Software: Digitization of healthcare through its AI-powered EMR platform. The startup empowers 10K plus doctors to drive better health outcomes for their patients by providing clinical decision support at the point of care.
Total of $43.1M in funding over 9 rounds.
Their recent funding was raised on April 24, 2022, from a Series C round of $20M
Investors: Avataar Venture, SIG venture capital, Kalaari Capital, Chiratae Ventures, Light-speed venture capital, JSW venture
🛑 Challenges of AI In Healthcare
💊 Accountability
On the one hand, AI systems are merely decision support systems, not intended to replace doctors/dentists. On the other hand, to reap the full benefits of AI and if they are to help with scale and efficiency, then a lot of functions will get delegated solely to AI. In this scenario, who is to be held accountable in case of misdiagnosis or errors?
💊 Creative Ability
A big disadvantage of AI is that it cannot learn to think outside the box (yet). While it does learn over time, this learning is limited to pre-fed data and experiences. It cannot deal easily with new situations and cannot exhibit creativity. Human intervention is needed for this. For example: suppose a patient presents with a set of symptoms that do not fit any established diagnostic categories. A human doctor, drawing from their knowledge, intuition, and experience, might make a creative leap to propose a novel diagnosis or treatment approach that was not previously documented or considered. This may involve connecting seemingly unrelated pieces of information in a way that a machine trained on existing data could not do.
Other Disadvantages
There are several other challenges that we must deal with such as — AI bias, loss of traditional jobs, need for several new regulations, misuse of technology, increased exposure to hacking, lack of human insight into decisions, etc.
👉🏼 Challenges Uniquely Faced By India
India faces several challenges in its adoption of AI for Healthcare, such as:
- Data-related issues — availability, interoperability
- Privacy and data protection concerns
- Infrastructure availability
- Lack of AI skills and training
- Funding concerns
- Lack of strong governance and regulations
- Lack of a sustainable business case for AI in rural healthcare
Of these, the major concerns are access to data and availability/cost of Infrastructure.
👩🏽💻 Access To Data
In India, the healthcare data ecosystem poses challenges for AI integration. Data is fragmented, often gathered from various providers, and lacks consistency due to the prevalence of unaccredited practitioners. Many large hospitals have poor digitization practices, leading to non-standardized and redundant patient records. Handwritten prescriptions and manual recording by frontline health workers further complicate data extraction. Available data for AI may not truly represent the diverse Indian populace, and relying on foreign datasets risks inaccuracy in local applications.
This scenario presents a potential investment opportunity for VCs to foster standardization and adaptability in India’s healthcare sector.
Infrastructure And Costs
AI systems can be expensive to train, test, and deploy. Datasets are expensive to collect, and there’s a high cost for computing power & storage. The lack of necessary digital infrastructure is another limitation. Much of the cloud computing infrastructure is based outside India, leading many start-ups to set up their businesses abroad. Local companies rely on big players like Google and Microsoft for cloud services.
In contrast to the promise of improving access to quality healthcare for underserved populations, there is a risk that affordability and the lack of a sustainable business case for AI may limit its adoption in rural areas.
Further concerns with linking Aadhar cards with health data, which has already been documented as suffering multiple privacy and security breaches.
For VCs, according to me, India’s healthcare AI has massive potential but faces hurdles: infrastructure gaps, high costs, and data privacy concerns. Investing can bridge these gaps and unlock vast untapped opportunities, especially in rural areas.
The Indian Government And AI
📑 The AI Policy Landscape In India
The progression of AI in India has been piecemeal. However, under the Digital India Initiative, the Indian government intends to direct funding towards cutting-edge technologies like AI, as per the 2020 plan by the Ministry of Electronics & Information Technology. AI policy in India: a framework for engaging the limits of data-driven decision making. Various policies and initiatives in India are aiding in the progression of AI.
Some of these are discussed below:
NITI Aayog’s National Strategy for Artificial Intelligence:
•#AIFORALL In the budget speech 2018–2019, India’s Finance Minister assigned NITI Aayog the creation a National Artificial Intelligence Mission program to steer R&D in emerging technologies.
•A three-pronged approach was adopted by NITI Aayog
i) carrying out exploratory proof-of-concept AI projects in various areas such as agriculture, health, etc
ii) Designing a national strategy to build a vibrant AI ecosystem in India and
iii) Collaboration with experts and stakeholders.
INDIAai- The National AI Portal of India
•INDIAai, initiated by the Indian government and other stakeholders, is a comprehensive AI portal aimed at preparing India for a future with artificial intelligence. It serves as a central hub for AI information for students, entrepreneurs, professionals, and scholars.
•Its main goal is to create a unified AI ecosystem in India that supports economic growth and improves lives. It facilitates the sharing of valuable AI resources such as technical papers, articles, and funding details for AI-oriented start-ups, companies, and educational organizations.
NITI Aayog’s National Strategy for AI prioritizes principles of privacy, ethics, security, fairness, transparency, and accountability, as well as alignment with the rights afforded by the Indian Constitution.
•The Indian government introduced Ayushman Bharat in 2018, a health insurance scheme for low-income families, building on the 2017 National Health Policy. The policy aims to create a linked health information system using the Aadhaar system, improve public health through big data analysis, and establish a state-supported digital infrastructure for data exchange. This data would be accessible to the private sector for innovation, using open APIs and promoting national data portability.
📖 AI Policies And Initiatives By Indian State Governments
Centre of Excellence for Data Science and Artificial Intelligence.
The Government of Karnataka, partnering with NASSCOM, established the Centre of Excellence for Data Science and Artificial Intelligence to bolster data science and AI development across India, aiming to position it among the top five global AI innovation centers within five years.
India is a founding member of the Global Partnership on AI alliance and has thus far adopted a measured approach to the integration of AI, in keeping with ethical and responsible standards. These principles must be applied in practice as the technology scales.
The Indian government will need to make appropriate investments in data infrastructure, such as interoperability, unified EMR, and data stewardship. This is essential to build trust and long-term integration of AI into India’s healthcare system.
⭐ Conclusion
India is already a mature market for health-tech investments, and there are plenty of recent successes to boast of despite the several challenges faced, the convergence of advances in AI technology, Government support, a growing economy, the healthcare market, its young population, and a thriving start-up ecosystem make the Indian health-tech sector an attractive proposition for VC investment.
And tada… you’ve just consumed one mammoth of an industry along with your coffee! Stay tuned for my next Coffee Break edition to give you context, guidance, and specifics on complicated investment opportunities in 17 minutes.
🙋I’d love to know your thoughts, please reach out to me in the comments with anything Healthtech, Startups, Pharma or VC related, or connect with me on LinkedIn.
References:
World Economic Forum, CB insights, The Express Healthcare, NASSCOM, HealthITAnalytics, Cognizant, YourStory, Csd Columbia Edu, Business Today, ET Healthworld, IAS Parliament, Money control, HT Health, Tracxn, Business Insider Today, Statista, Analytics Insights, Forbes, Forbes, INDIAai, TheHindu, Research and Markets, Wikipedia
