Patient records, lab reports, prescriptions, and clinical notes contain highly sensitive information. Using external AI platforms may require this data to leave the hospital environment for processing, raising important privacy and security concerns.
Most AI models are trained on data from across the world. While they have strong general medical knowledge, they may not fully understand Indian lab report formats, commonly used medicines, local medical terminology, and healthcare practices. A model may understand healthcare in general but still lack the context in which healthcare is delivered in India.
We learned that simply deploying a powerful AI model was not enough to generate high-quality healthcare responses. The model is only one part of the overall AI system. Additional layers for patient context, information retrieval, reasoning, validation, and response processing are required to generate more relevant and useful responses.
Analysing medical reports, patient history, and large amounts of clinical information requires significant processing. Larger AI models also require substantial computing resources, which can result in slower response times and require continuous optimisation.
Hosting AI models on our GPU servers introduced challenges related to server connectivity, model availability, and service interruptions. We needed additional monitoring and infrastructure improvements to make the AI services more stable and reliable.
Medical information may be available as scanned lab reports, PDFs, images, or clinical documents. This information cannot always be directly analysed by a generic AI model and needs to be extracted, cleaned, and structured before analysis.
An AI response may sound correct but could still miss an important medical finding or patient context. Technical testing alone was not sufficient; feedback from doctors was essential to understand whether the responses were clinically relevant and practically useful.
Our LLM, OCR, and voice-to-text models are deployed on-premises within the private and controlled environment. Instead of relying on a single model, specialised models handle medical analysis, scanned reports and images, and voice processing, while helping keep sensitive medical information within the hospital infrastructure.
Instead of relying on a single AI model for every task, we use specialised models for medical analysis, document processing, and voice processing. OCR helps extract and structure medical information from scanned reports, PDFs, and images, while voice-to-text enables doctors and medical teams to capture clinical observations using speech. Together, these capabilities reduce manual data entry, speed up information capture, and make medical data more efficiently available for AI-driven analysis.
We built a healthcare-specific information retrieval system, commonly known as RAG. This provides relevant patient history and medical information to the AI before generating a response, helping make the analysis more contextual and patient-specific.
We are continuously improving the AI with Indian healthcare context by incorporating relevant medical data, local lab report formats, medical terminology, medicine-related information, and reference guidelines from organisations such as ICMR, WHO, CSI, NVBDCP, IOA, etc. This helps the AI better understand local healthcare practices and provide more contextually relevant responses.
The solution has been deployed within our hospital, allowing doctors to review actual AI-generated responses and provide clinical feedback. This feedback helps us identify missing medical context, incorrect assumptions, and areas where the responses can be improved.
We are working on a framework to identify possible incorrect information, missed findings, and response quality issues. The framework will also help us compare different AI models using our actual healthcare use cases and provide structured feedback for further improvement.
The on-premise architecture allows healthcare information to be processed closer to where it is stored, reducing our dependency on external AI services for analysing sensitive patient information.
By providing relevant patient history and medical context, the AI can generate responses based on the patient's available information rather than providing only general medical responses.
Doctors have identified clinical gaps and missing context that technical testing may not detect. Their feedback directly helps us improve the AI and make responses more relevant to real healthcare scenarios.
Our focus on Indian healthcare context is helping improve the system's understanding of local medical terminology, reports, medicines, and healthcare practices.
OCR helps extract medical information directly from scanned reports, PDFs, and images, while voice-to-text models enable faster capture of clinical observations using speech. Together, these capabilities reduce manual data entry, save time, and make medical information available for AI analysis more efficiently.
We can evaluate different AI models based on healthcare response quality, response time, and infrastructure requirements. This reduces dependency on a single AI model provider.
The same AI foundation can extend beyond individual patient analysis to identify health trends across larger populations, support population-level health intelligence, and enable forecasting of potential healthcare risks and disease patterns.
Keeps patient medical data within the hospital environment
Operates within the hospital's existing security infrastructure
Provides greater control over data access, storage, and retention
Provides better visibility and control over the complete AI processing workflow
Enables continuous AI customisation for the Indian healthcare system