AI & R&D

Healthcare, Reimagined Through Disease-Specific AI

AyurLekh's AI story is disease-specific and status-labelled. It is designed as an intelligence layer around healthcare professionals, not autonomous diagnosis.

The doctor is the centre

AI should make doctors more capable, not make doctors irrelevant.

AI can structure symptoms and history, organise reports, support clinically appropriate imaging workflows, reduce repetitive documentation and highlight information that may deserve clinician review.

Responsible positioning: These capabilities are described as AI-assisted clinical support, not autonomous diagnosis. Clinical claims require appropriate validation and intended-use requirements.

Real Amogh Facilities & AI Solutions registry.

Current vs future

Problem → approach → workflow → capability → status.

Every AI/R&D card carries a visible status badge.

Vertical	What it covers	Website status
Dermatology AI	Disease-specific workflows, AI-assisted analysis, image/report workflows, patient segmentation.	IN DEVELOPMENT
Cardiology AI	Disease-specific workflows, clinical data analysis, AI-assisted insights.	IN DEVELOPMENT
Radiology AI	Imaging workflows, X-Ray/CT/MRI, DICOM support, AI-assisted imaging.	IN DEVELOPMENT
Research Module	Disease-wise research, structured clinical data, privacy-aware collaboration, federated-research direction.	R&D ROADMAP

The reference proposal explicitly says the clinical AI is not yet a validated diagnostic product, so the site does not make clinical-accuracy or diagnostic claims.

Human oversight

AI outputs support rather than silently replace clinical judgement.

Validation

Clinical claims should be supported by evidence and evaluation.

Transparency

Users should understand what the system can and cannot do.

Escalation

Higher-risk or uncertain cases should be surfaced for professional review.

Explore each AI vertical.

See the problem, approach and status separately.

Disease-Specific AI →