3 Indian AI Startups That Are Quietly Revolutionizing Our Healthcare

I’ve spent over six years in AI research, but what’s happening in India’s medical tech scene right now is something else. Here’s a closer look at the pioneers in a market that is set to explode.

I’ve spent the better part of a decade neck-deep in AI research, from NLP to clinical data, across labs in India and Germany. You see a lot of fascinating theory, but nothing gets me more excited than seeing that theory leap off the page and into the real world, solving problems that actually matter.

And right now, there’s no better place to watch that happen than in India’s healthcare AI scene.

India’s healthcare sector is rapidly transforming with the integration of artificial intelligence, poised to reach $1.6 billion by 2025 with a CAGR of 40.6% (Source: IndiaAI blog dated December 31, 2024). 

This, I feel, is where the real magic is. It’s not just about algorithms in a vacuum; it’s about technology making a tangible difference in a country with immense challenges and even greater potential.

I’ve noticed a gap. We see a lot of high-level talk about market growth and billion-dollar valuations, but very few people are digging into the how — the specific tech, the unique hurdles of the Indian context, and the real-world problems these startups are actually solving.

That’s the gap I want to start bridging with this article. By connecting my academic background with what’s happening on the ground in the industry, I want to give you a real look under the hood.

This piece is for anyone who is:

• Curious about how AI is being used beyond the usual buzzwords.
• A student or researcher wants to see real-world applications of their field.
• Simply fascinated by the innovation brewing in India.

We’ll dive into three Bangalore-based pioneers I’ve been following: Niramai Health Analytix, SigTuple, and Haptik. We’ll explore not just what they do, but why it matters, especially for India.

This is just the first installment in a series I’m planning on the medical AI revolution in India. The field is massive, and I know there are more stories to tell. Your feedback and recommendations for other startups to feature are most welcome!

I truly believe that by understanding their journey, we can all get a better sense of where the future of healthcare is headed.

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P.S. — If you’re new to the world of AI in Medicine and are looking for a place to start, you might find this other article of mine useful. It’s a list of top researchers and open-source resources to help you get started on your own journey.

How to Get Started with Research in AI for Medicine: A Comprehensive Guide

A Self-help guide to starting your own Machine Learning research project

So, let’s dive in.

1. Niramai Health Analytix

Focus: AI-based early breast cancer detection using thermal imaging. 
 Website: https://niramai.com
 Niramai offers a non-invasive, radiation-free, and affordable breast cancer screening solution using AI-powered thermal analytics, widely adopted across India.

Impact: Address low screening uptake rates (especially in Low and Middle-Income countries) for breast cancer that is leading to high mortality rates. Earlier detection leads to better patient outcomes and reduced treatment costs.

Here, I will explain the technology powering one of their product offerings — Thermalytix: AI-Powered Breast Cancer Screening Test.

Technology powering Thermalytix

The following description is based on my understanding of the product details mentioned at https://niramai.com/about/thermalytix/

A high-resolution thermal sensing device is used to capture a thermal image or scan of the chest.

It is then sent to a cloud-hosting analytics solution, where patented AI and deep learning algorithms analyze approximately 400,000 temperature points on the chest and identify known patterns of abnormalities, such as blood vessel patterns and heat signatures.

Analyzing temperature maps instead of thermal images allows Thermalytix to gain 50 times more thermal sensitivity. This leads to more accurate risk score predictions and provides the scope for earlier detection.

The output is a breast cancer screening report with quantitative risk scores that will be reviewed by a medical expert to determine the next step.

Question: How is the AI model trained?

Answer: The AI model is continuously trained over time on large amounts of thermal scan data covering diverse demographics, in conjunction with the associated radiology (imaging), such as mammography and ultrasound, and histopathology (also known as biopsy or pathology) reports. This allows the AI model to identify complex patterns of breast cancer that are linked to malignancy and reduce the number of false positives.

Just for context, radiology refers to the “field of medical specialty that uses medical imaging to diagnose diseases and guide treatment within the bodies of humans and other animals”. https://en.wikipedia.org/wiki/Radiology

Histopathology refers to “the microscopic examination of tissue in order to study the manifestations of disease. Specifically, in clinical medicine, histopathology refers to the examination of a biopsy or surgical specimen by a pathologist, after the specimen has been processed and histological sections have been placed onto glass slides.” https://en.wikipedia.org/wiki/Histopathology

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2. Haptik

Focus: Conversational AI-based Assistants for multiple domains, including healthcare
Website: https://haptik.ai
Haptik develops AI chatbots that handle patient communication, appointment scheduling, and symptom checking. The interactions range across multiple platforms based on target use case — WhatsApp, Facebook Messenger, Instagram Direct, SMS, and even website chatbots.

Here, we will limit the analysis to use cases developed by Haptik, related to conversational AI for healthcare (Haptik webpage).

Case study of Netmeds (online pharmacy) — 

• Business Use-case: Surge (10x) in online traffic during COVID-19 for tracking and managing medicine orders, led to increased load on human agents.
• Solution: WhatsApp chatbot for tracking and managing medicine orders.
• Impact: 99% improvement in First Response Time, 2600+ man hours saved, 83.6% Automation Rate.

Case study of Dr. LalPathLabs (medical diagnostics) — 

• Business Use-case: AI-driven customer care automation to reduce human intervention for consumer queries like finding the nearest test centre, diagnostic test prices, and status check of test reports.
• Solution: Website chatbot provides 24×7 assistance to patient queries.
• Impact: 4 lakh+ conversations till date, reduced load on call centres by 20%, and a high positive resolution rate of chats.

Case study of MyGov Corona Helpdesk with the Government of India

• Use-case: To raise COVID-19 Awareness and counter health misinformation.
• Solution: WhatsApp Chatbot providing 24×7 support and updates.
• Impact: 5 million+ conversations processed in its first 48 hours.

Technology powering Haptik

To the best of my effort, I could not find much information or any publication about the technology stack from their website, except once where they mentioned GPT-powered chatbots. Their technology is HIPAA-compliant, ensuring the chatbots adhere to strict privacy and security protocols.

To give context, HIPAA refers to the 1996 Health Insurance Portability and Accountability Act of the United States. “ It generally prohibits healthcare providers and businesses called covered entities from disclosing protected information to anyone other than a patient and the patient’s authorized representatives without their consent.” — Wikipedia

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3. SigTuple

Focus: AI-driven automation of medical diagnostics (blood and urine microscopy, pathology)
Website: https://sigtuple.com
SigTuple developed an AI-enabled medical device called AI100 to assist pathologists in performing blood and urine microscopy.

Business use-case:

• Roughly one pathologist exists for every 65000 patients.
• Blood and urine microscopy currently requires a pathologist to be physically present, and thus cannot be performed remotely like telemedicine.
• Present commercial equipment is made for high-volume scenarios, hence very costly. Therefore, the hub-and-spoke model is followed, where samples from Tier-2 or Tier-3 settings are sent to Tier-1 cities for analysis. This leads to significant sample reporting turnaround time and sample degeneration.

Solution:

• A medical device that combines artificial intelligence, robotics, microfluidics, and cloud computing to automatically capture high-resolution microscopic images of the samples (blood or urine) and upload them to the cloud.
• The AI models running on the cloud analyze the images and send the pathology report for review to a pathologist in a Web browser, which can be accessed from anywhere, even from home.

Impact:

• Increased efficiency of pathologists due to AI-enabled diagnosis draft, and remote accessibility. Instead of 30 slides per day, it improved to 300 slides per day.
• AI100 can be set up in Tier-2 and Tier-3 cities, which alleviates the need to transport samples all the way to the Tier-1 cities.
• Optimal time allocation. Pathologists can quickly review the near-normal cases and can allocate more time to serious and more complex cases.

Technology powering AI100

Local technicians take the urine or blood sample and smear it on a slide, which is placed into the “mechanical stage” part of the AI100 medical device. The mechanical stage is controlled by a printed circuit board to correctly position the slide under the microscopic lens (400X magnification). An LED unit is also attached to illuminate the area properly. The device also has significant compute hardware comprising an Intel processor and a GPU. The digital images are then sent to the cloud, where AI models analyze these images and provide an automated pathology report for the pathologist to review.

References: (i) The better India blog, (ii) The Economic Times

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Final Words

These startups represent the forefront of AI innovation in Indian healthcare, offering scalable solutions that improve diagnostics, monitoring, personalized treatment, and patient engagement.

In summary, we covered two computer vision solutions for radiology (Niramai Health Analytix) and pathology (SigTuple) use cases. We also covered an NLP-based AI solution based on Conversational AI assistants (Haptik).

I hope you enjoyed such technology deep-dive articles. I hope to cover more medical AI-based Indian startups in the future.

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Disclaimer

This article focuses solely on medical AI-related use cases and is intended for informational and educational purposes only. All information presented is based on publicly available sources cited appropriately within the text. It is not intended to represent a comprehensive overview of all product offerings or to comment on the reputation or brand of any featured startup. This content does not constitute medical advice, diagnosis, or treatment. Always consult with a qualified healthcare professional for any medical concerns or before making any decisions related to your health.

Please let me know if some facts are misleading or incorrect. I would be happy to correct them.

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Do you have any suggestions regarding some interesting topics I should cover? Do you think I missed anything? Please feel free to let me know in the comments.

To learn more about my research portfolio, please visit https://roysoumya.github.io/

Thank you once again for taking the time to read the article.