Medical AI,
Textbook Accurate
The first medical AI that cites its sources. Every answer backed by Bailey & Love, Sabiston, ATLS, and more.
Ask Lymph
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7+
Medical Textbooks
10K+
Pages Indexed
94%
Citation Accuracy
<2s
Response Time
Knowledge Base
Powered by Authoritative Sources
Bailey & Love
Bailey & Love's Short Practice of Surgery
27th EditionSabiston
Sabiston Textbook of Surgery
22nd EditionATLS
Advanced Trauma Life Support
11th EditionSchwartz
Schwartz's Principles of Surgery
11th EditionDeVita
DeVita, Hellman & Rosenberg's Cancer
12th EditionTata Memorial
TMC Textbook of Oncology
Latest EditionPOS
Principles of Surgery
CurrentBailey & Love
Bailey & Love's Short Practice of Surgery
27th EditionSabiston
Sabiston Textbook of Surgery
22nd EditionATLS
Advanced Trauma Life Support
11th EditionSchwartz
Schwartz's Principles of Surgery
11th EditionDeVita
DeVita, Hellman & Rosenberg's Cancer
12th EditionTata Memorial
TMC Textbook of Oncology
Latest EditionPOS
Principles of Surgery
CurrentBailey & Love
Bailey & Love's Short Practice of Surgery
27th EditionSabiston
Sabiston Textbook of Surgery
22nd EditionATLS
Advanced Trauma Life Support
11th EditionSchwartz
Schwartz's Principles of Surgery
11th EditionDeVita
DeVita, Hellman & Rosenberg's Cancer
12th EditionTata Memorial
TMC Textbook of Oncology
Latest EditionPOS
Principles of Surgery
CurrentBailey & Love
Bailey & Love's Short Practice of Surgery
27th EditionSabiston
Sabiston Textbook of Surgery
22nd EditionATLS
Advanced Trauma Life Support
11th EditionSchwartz
Schwartz's Principles of Surgery
11th EditionDeVita
DeVita, Hellman & Rosenberg's Cancer
12th EditionTata Memorial
TMC Textbook of Oncology
Latest EditionPOS
Principles of Surgery
CurrentBuilt for Medical Excellence
Every feature designed with one goal: providing accurate, verifiable medical information that healthcare professionals can trust.
Textbook-Backed Answers
Every response cites specific chapters, sections, and topics from authoritative medical textbooks including Bailey & Love, Sabiston, ATLS, Schwartz, and DeVita.
Transparent Citations
See exactly where each piece of information comes from. No black-box responses—every claim is traceable to its source with full reference metadata.
RAG-Powered Architecture
Advanced Retrieval-Augmented Generation using Qdrant vector database ensures relevant context is retrieved for every query with semantic precision.
Semantic Search
Our MEDICA pipeline processes thousands of pages through semantic chunking, hierarchical analysis, and token-optimized splitting for perfect retrieval.
Multi-Platform Access
Access Lymph through Telegram, WhatsApp, or our web interface. Your conversation history syncs across platforms with smart context management.
MCQ & Open-Ended Support
Handles both multiple-choice questions with option-by-option analysis and comprehensive open-ended queries with structured explanations.
Hallucination Prevention
Strict grounding rules ensure responses only use provided textbook content. If evidence is insufficient, Lymph explicitly states when review is needed.
Fast & Reliable
Sub-2-second response times powered by Mistral's latest LLMs with intelligent load balancing across multiple API endpoints for high availability.
Continuous Updates
Our pipeline continuously processes new editions and textbooks. Stay current with the latest medical knowledge as it's published.
Redefining Trust in
Medical AI
In an era of AI hallucinations and misinformation, medical professionals deserve better. Lymph was born from a simple belief: medical AI should be as rigorous as the textbooks it learns from.
We don't just answer questions—we show our work. Every response traces back to specific chapters, sections, and topics from the world's most trusted medical references. This isn't just AI; it's verifiable intelligence.
“The best medical AI doesn't replace textbooks—it makes them conversational while preserving their authority.”
— The Lymph Philosophy
Precision Medicine Needs Precision AI
Medical AI shouldn't guess. It should cite, verify, and provide clear provenance for every piece of information. We're building the standard for trustworthy medical AI.
Empowering Healthcare Professionals
From surgical residents preparing for exams to practicing physicians seeking quick reference—Lymph serves as your intelligent medical companion that knows its limits.
Global Medical Knowledge Access
Quality medical education shouldn't be geographically limited. We're democratizing access to authoritative medical knowledge across the world.
The Future of Medical Learning
Interactive, conversational, and always grounded in evidence. Lymph represents the next evolution in how medical professionals learn and reference information.
Rigorously Evaluated
We don't just claim accuracy—we measure it. Our evaluation suite tests Lymph against real medical questions from authoritative sources.
Citation Accuracy
Correctly identifying and citing the source textbook
MCQ Accuracy
Correct answers on standardized medical MCQs
Reference Relevance
Retrieved content directly addresses the query
Avg Response Time
End-to-end query to answer latency
How We Measure Excellence
Our evaluation framework tests Lymph across multiple dimensions: factual accuracy, citation precision, response relevance, and clinical utility. We use a combination of automated testing and expert review.
Every answer is cross-validated against the source material. When Lymph says “according to Sabiston, Chapter 18”—we verify that claim automatically.
* Metrics based on internal evaluation suite. Updated January 2026.
What We've Learned
Building medical AI has taught us invaluable lessons. Here are key insights from our journey that shape how Lymph works.
Context Length Matters
Longer conversation history dramatically improves follow-up question accuracy. We maintain up to 100k tokens of context per session.
Semantic Chunking Beats Fixed-Size
Our LLM-powered semantic chunking outperforms traditional fixed-token chunking by 23% in retrieval precision.
Citation Order Affects Quality
Generating references BEFORE the explanation forces the model to ground its response, reducing hallucination rates by 40%.
Users Want Brief AND Detailed
76% of users prefer having both brief summaries and detailed explanations available. Context switching is common in clinical settings.
Edge Cases Are Critical
The most valuable feedback comes from queries where our RAG retrieves content that doesn't actually answer the question. We flag these explicitly.
Multi-Source Synthesis Is Hard
When textbooks disagree (e.g., different Sabiston editions), presenting both viewpoints without choosing maintains user trust.
We're Just Getting Started
Every day brings new insights as we refine Lymph. Join us on this journey to build the most trustworthy medical AI ever created.
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