BiomedicalAISystem

v2026.5.0 suspicious
4.0
Medium Risk

BiomedicalAISystem: Universal, agentic life-science & clinical AI skills platform (BioKernel runtime, USDL, multi-LLM orchestration, MCP).

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential obfuscation and unusual metadata characteristics, suggesting it may be new or less active.

  • Obfuscation risk of 5/10
  • Unusual metadata including a single release and a potentially unverified author
Per-check LLM notes
  • Network: The use of HTTP requests is common for packages that require API interactions or fetching data from external sources.
  • Shell: No shell execution patterns were detected, indicating no direct system command execution risk.
  • Obfuscation: The observed obfuscation pattern is suspicious and could indicate an attempt to hide code functionality, but it's not conclusive without further analysis.
  • Credentials: No clear patterns of credential harvesting are present based on the provided information.
  • Metadata: The package shows some red flags such as a single release and an author with a missing name and a single package, indicating it might be new or inactive.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • httpx resp = httpx.get(f"{self._api_base}/api/tags", timeout=5) if
  • ery}) async with httpx.AsyncClient() as client: resp = await client.post(
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • enerate HTML report") def eval(eval_file, platform, html): """Run evaluation suite
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: mssm.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with BiomedicalAISystem 
Create a comprehensive patient symptom analysis tool using the 'BiomedicalAISystem' Python package. This tool will enable users to input their symptoms and receive a preliminary diagnosis along with recommended actions based on the input data. The application will utilize the BioKernel runtime, USDL, and multi-LLM orchestration capabilities provided by the 'BiomedicalAISystem' package to deliver accurate and contextually relevant results.

### Step-by-Step Guide:
1. **Setup**: Install the necessary dependencies including the 'BiomedicalAISystem' package.
2. **User Interface**: Develop a simple web interface where users can enter their symptoms via checkboxes or free-text fields.
3. **Symptom Analysis**: Use the BioKernel runtime within 'BiomedicalAISystem' to process the user inputs and perform initial filtering based on common symptom patterns.
4. **Multi-Agent Coordination**: Leverage the multi-LLM orchestration feature to consult multiple AI models simultaneously for a more nuanced understanding of the potential conditions associated with the symptoms entered.
5. **Contextual Understanding**: Implement the Unified Semantic Data Language (USDL) to ensure that the system understands the context of each symptom and how they relate to one another.
6. **Diagnosis Generation**: Based on the processed data and consultations from various AI models, generate a preliminary diagnosis.
7. **Action Recommendations**: Provide actionable steps based on the diagnosis, such as when to see a doctor, over-the-counter medication suggestions, etc.
8. **Feedback Loop**: Incorporate a feedback mechanism allowing users to rate the accuracy of the diagnosis and provide additional details if needed, which can then be used to improve the system over time.

### Suggested Features:
- Symptom-to-Symptom Correlation: Analyze how different symptoms might be related.
- Disease Probability Estimation: Offer probabilities of having certain diseases based on the symptoms entered.
- Personalized Health Tips: Tailor health advice based on the user’s specific symptoms.
- Integration with Wearable Devices: Allow input of biometric data from wearable devices like smartwatches for more accurate analysis.
- Continuous Learning Mechanism: Utilize the feedback loop to continuously refine the AI models and improve the accuracy of diagnoses.

### How 'BiomedicalAISystem' is Utilized:
- **BioKernel Runtime**: For executing complex biomedical algorithms efficiently.
- **Unified Semantic Data Language (USDL)**: To ensure consistent interpretation of symptom data across different AI models.
- **Multi-LLM Orchestration**: For leveraging multiple AI models to enhance the depth and breadth of the analysis.
- **MCP (Multi-Component Platform)**: To manage and integrate various components of the system seamlessly.