wisediag checkup

The problem

Medical checkup reports are often filled with dense clinical data and jargon that are difficult for individuals to interpret. Manually cross-referencing lab values against lifestyle factors and family history is time-consuming and prone to oversight.

What it does

• Identifies and flags abnormal items within medical reports.
• Provides clinical explanations for lab results and findings.
• Correlates health questionnaire data with report findings for personalized assessments.
• Generates structured lifestyle advice and actionable health recommendations.
• Supports both remote PDF URLs and local binary file uploads for analysis.

Frameworks & tools

Python 3, WiseDiag API, and CLI-based async task orchestration.

Why this beats prompting it yourself

Generic LLM prompts often hallucinate medical ranges or fail to maintain clinical structure over long documents. This skill uses a specialized medical analysis engine via WiseDiag, providing a multi-step verification process that ensures data is processed through clinical models rather than raw token prediction.

Use cases

• Extracting and explaining abnormal results from a scanned annual physical report.
• Analyzing a blood work PDF in the context of specific symptoms or family history.
• Generating a structured digital summary of health trends for personal records.
• Providing lifestyle recommendations based on specific clinical markers.

Known limitations

Requires a WiseDiag API key. Documents are processed on remote servers, so it is not suitable for users requiring strict offline air-gapped data privacy.