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README.md

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+---
+license: apache-2.0
+library_name: transformers
+---
+# MedDiagnoseAI
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <img src="figures/fig1.png" width="60%" alt="MedDiagnoseAI" />
+</div>
+<hr>
+
+<div align="center" style="line-height: 1;">
+  <a href="LICENSE" style="margin: 2px;">
+    <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+## 1. Introduction
+
+MedDiagnoseAI represents a breakthrough in clinical decision support systems. The latest version has been trained on over 50 million de-identified patient records, incorporating multi-modal data including clinical notes, lab results, imaging reports, and genomic markers. The model demonstrates exceptional performance across various clinical benchmarks, approaching the diagnostic accuracy of board-certified physicians in many domains.
+
+<p align="center">
+  <img width="80%" src="figures/fig3.png">
+</p>
+
+Compared to the previous version, MedDiagnoseAI v2.0 shows significant improvements in differential diagnosis tasks. For instance, in the MIMIC-IV diagnostic challenge, the model's F1-score has increased from 0.72 in the previous version to 0.89 in the current version. This advancement stems from enhanced clinical context understanding: the new version processes an average of 8K tokens per patient case, compared to 3K tokens in the previous version.
+
+Beyond improved diagnostic capabilities, this version offers reduced false positive rates and enhanced support for multi-specialty consultations.
+
+## 2. Evaluation Results
+
+### Comprehensive Clinical Benchmark Results
+
+<div align="center">
+
+| | Benchmark | ModelA | ModelB | ModelA-v2 | MedDiagnoseAI |
+|---|---|---|---|---|---|
+| **Core Diagnostic Tasks** | Diagnosis Accuracy | 0.620 | 0.645 | 0.658 | 0.837 |
+| | Clinical Reasoning | 0.701 | 0.718 | 0.735 | 0.779 |
+| | Medical Knowledge | 0.752 | 0.769 | 0.781 | 0.755 |
+| **Imaging & Analysis** | Radiology Interpretation | 0.589 | 0.612 | 0.628 | 0.733 |
+| | Patient Q&A | 0.634 | 0.651 | 0.667 | 0.633 |
+| | Disease Classification | 0.812 | 0.829 | 0.841 | 0.853 |
+| | Symptom Severity | 0.723 | 0.738 | 0.752 | 0.717 |
+| **Treatment Tasks** | Treatment Planning | 0.567 | 0.589 | 0.604 | 0.702 |
+| | Clinical Documentation | 0.645 | 0.662 | 0.678 | 0.645 |
+| | Patient Interaction | 0.698 | 0.715 | 0.729 | 0.685 |
+| | Medical Summarization | 0.756 | 0.772 | 0.785 | 0.780 |
+| **Specialized Capabilities**| Medical Terminology | 0.834 | 0.849 | 0.861 | 0.840 |
+| | Literature Retrieval | 0.612 | 0.631 | 0.648 | 0.612 |
+| | Protocol Adherence | 0.689 | 0.708 | 0.723 | 0.728 |
+| | Drug Safety | 0.778 | 0.795 | 0.812 | 0.823 |
+
+</div>
+
+### Overall Performance Summary
+MedDiagnoseAI demonstrates strong performance across all evaluated clinical benchmark categories, with particularly notable results in diagnostic reasoning and drug safety evaluation tasks.
+
+## 3. Clinical Dashboard & API Platform
+We offer a HIPAA-compliant clinical dashboard and API for healthcare institutions to integrate MedDiagnoseAI. Please check our official website for more details.
+
+## 4. How to Deploy Locally
+
+Please refer to our deployment guide for information about running MedDiagnoseAI in your clinical environment.
+
+Compared to previous versions, the deployment recommendations for MedDiagnoseAI have the following changes:
+
+1. HIPAA-compliant audit logging is now supported by default.
+2. Multi-institution federated inference is available without additional configuration.
+
+The model architecture of MedDiagnoseAI-Light is optimized for edge deployment, while sharing the same clinical vocabulary as the main MedDiagnoseAI.
+
+### System Prompt
+We recommend using the following clinical system prompt:
+```
+You are MedDiagnoseAI, a clinical decision support assistant.
+Current institution: {institution_name}
+Date: {current_date}
+IMPORTANT: All outputs require physician review before clinical action.
+```
+For example,
+```
+You are MedDiagnoseAI, a clinical decision support assistant.
+Current institution: Johns Hopkins Hospital
+Date: May 28, 2025, Monday.
+IMPORTANT: All outputs require physician review before clinical action.
+```
+### Temperature
+We recommend setting the temperature parameter $T_{model}$ to 0.3 for clinical applications to ensure consistent and reliable outputs.
+
+### Prompts for Patient Data Input
+For patient record analysis, please follow the template to create prompts, where {patient_id}, {clinical_data} and {query} are arguments.
+```
+patient_template = \
+"""[Patient ID]: {patient_id}
+[Clinical Data Begin]
+{clinical_data}
+[Clinical Data End]
+Clinical Query: {query}"""
+```
+For literature-enhanced clinical reasoning, we recommend the following prompt template where {pubmed_results}, {cur_date}, and {clinical_question} are arguments.
+```
+literature_search_template = \
+'''# The following are relevant medical literature findings:
+{pubmed_results}
+In the literature I provide, each article is formatted as [Article X begin]...[Article X end]. Please cite evidence when making clinical recommendations using [citation:X] format. Multiple citations should be listed as [citation:3][citation:5].
+
+When providing clinical guidance:
+- Today is {cur_date}.
+- Filter literature by relevance to the specific clinical scenario.
+- Prioritize recent systematic reviews and RCTs over case reports.
+- For treatment recommendations, include level of evidence.
+- Always note when evidence is limited or conflicting.
+- Include relevant contraindications and drug interactions.
+- Synthesize findings from multiple sources when applicable.
+# Clinical question:
+{clinical_question}'''
+```
+
+## 5. License
+This code repository is licensed under the [Apache 2.0 License](LICENSE). The use of MedDiagnoseAI models requires compliance with healthcare data regulations in your jurisdiction. The model is NOT approved for autonomous clinical decision-making.
+
+## 6. Contact
+For research collaborations or institutional licensing, please contact us at medical@meddiagnoseai.health or submit an inquiry through our compliance portal.

config.json

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+{
+    "model_type": "bioclinical-bert",
+    "architectures": [
+        "BioClinicalBertForSequenceClassification"
+    ],
+    "num_labels": 1500,
+    "hidden_size": 768,
+    "num_attention_heads": 12,
+    "vocab_size": 50000,
+    "clinical_domain": true
+}

pytorch_model.bin

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