Why a Generic LLM Won’t Cure Healthtech’s Biggest Problems

The global healthcare AI market is projected to reach $46.6 billion by 2035. It is set to transform how hospitals, payers and wellness providers manage everything from electronic medical records (EMR) to diagnostics and claims. However, as the industry evolves, generic AI platforms are proving insufficient for healthcare’s unique requirements.

Unlike consumer or enterprise IT, healthcare demands accuracy, compliance and explainability. Errors don’t just create inefficiencies; they can lead to serious, high-risk consequences. Hence, the next wave of healthcare AI is moving away from generic, speed-focused tools and towards verticalised, compliance-first platforms specifically designed for regulated industries.

India, long known for exporting global healthcare IT talent, is now building deep tech products. With the rise of global capability centres (GCCs) and a push for intellectual property creation, Indian firms are developing export-first platforms that meet stringent global compliance and interoperability standards.

Knewron is one such AI-native platform, built exclusively for healthcare by CitiusTech. The platform is designed for end-to-end product development in healthcare, embedding regulatory guardrails, persona-specific workflows, explainability and human-in-the-loop validation into every stage of the process.

“Every process flow includes validation points, audit trails, and compliance gates that cannot be bypassed. Clients can also add extra review steps for sensitive tasks. It’s how we deliver speed without sacrificing safety or accountability. ” said Sudhir Kesavan, COO, CitiusTech in conversation with AIM

One of the biggest bottlenecks in healthcare IT is the slow, fragmented development cycle. Multi-agent AI architectures promise to accelerate this process, but their outputs often conflict, introducing new risks. Knewron addresses this by orchestrating workflows with compliance guardrails and human oversight, preventing cascading errors.

Instead of retraining models every time regulations shift, the platform enforces the Health Insurance Portability and Accountability Act, General Data Protection Regulation, Medical Device Regulation and other compliance rules through a policy layer. This design enables healthcare organisations to adapt quickly to evolving regional requirements without stalling engineering teams.

The timing is significant. Global majors such as AWS and Accenture have already identified cloud, interoperability and AI as the foundation of equitable healthcare. But industry insiders point out that only domain-aware, healthcare-native platforms can deliver on this promise.

India’s Export-First Advantage

India has an edge in this transition. Its workforce combines technical expertise with clinical domain knowledge, while its lower-cost R&D base makes experimentation more viable. Growing digital health initiatives like the Ayushman Bharat Digital Mission are also creating a fertile ground for innovation.

With more than 8,500 specialists and a client base spanning over 140 global healthcare organisations, Indian deep tech firms are proving that they can compete on IP creation and compliance depth, rather than just labour cost.

Meeting the Challenges of Sensitive Data

The urgency is amplified by the healthcare industry’s pivot to value-based care in the US and the European Union, where providers and payers demand cost efficiency and seamless interoperability. AI platforms built natively for healthcare are expected to play a central role in claims automation, population health analytics and wellness applications.

However, the challenges are real. “Healthcare systems deal with sensitive PII, which requires strict protocols,” said Dhruvanandan V, a medical software developer. “We struggled to get LLMs to respond deterministically in a chatbot meant to match patients with doctors.”

“Failure to comply with these protocols carries huge legal fines,” he added. “Domain-specific models, fine-tuned or augmented with tools like RAGs and function calls, have improved reliability, but we’re still not confident deploying them in consumer-facing products.”

While foundation models and generic tools may accelerate experimentation, healthcare demands deterministic, auditable and compliant systems before they can be trusted in production.

Embedding Compliance Into Engineering

Platforms like Knewron are focusing on embedding compliance and auditability directly into engineering workflows. Instead of treating regulations as a bolt-on, these platforms build them into the core architecture, delivering speed without sacrificing accountability.

The approach also reflects advances in AI research itself. Beyond conventional large language models, innovations like Spatio-Temporal Graph Attention Networks (STGATs) by Shunya Labs are enabling more nuanced healthcare applications. 

By introducing a time dimension into model reasoning, STGATs capture causal relationships, such as the sequence of symptoms that can mean the difference between a correct diagnosis and a misdiagnosis.

From Service Hubs to Deep-Tech Exporters

As the healthcare AI market matures, the winners are unlikely to be horizontal, one-size-fits-all tools. The edge will belong to verticalised, compliance-first solutions that deeply understand regulated industries.

For India, this marks a turning point. Companies are no longer confined to the role of outsourced service providers. They are creating deep-tech, export-first platforms that serve some of the most highly regulated and high-value sectors in the world.

The momentum is that speed alone is not enough in healthcare. Trust, compliance and accountability are becoming the defining differentiators. And the companies that can embed these principles from the ground up are the ones most likely to lead the next phase of healthcare AI.