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deepevidence循证医学AI助手

Data & APIs

循证医学临床助手，采用 DeepEvidence 兼容 OpenAI 的 API（可追溯引用）。 用于解答复杂的临床问题、药物安全性证据、指南解读等。

Install

openclaw skills install deepevidence

DeepEvidence API Skill (Evidence-Based Medicine)

This skill calls DeepEvidence's OpenAI-compatible API to produce traceable, source-grounded evidence summaries for clinical use cases (drug safety, guideline interpretation, trial evidence synthesis). All outputs should be clinically verified before use.

Bundled repository files required: the default workflow references local scripts/ and references/ files. If your hosting/distribution does not ship them, use the direct HTTP API method below.

🛠️ Repository Structure

  • scripts/: Contains the interaction logic for medical Q&A and user-facing CLI tools.
  • references/: Contains the API interface specifications and technical constraints mapping.
  • SKILL.md: Root configuration and normative guidelines for the medical assistant.

Normative language

To avoid ambiguity, treat requirement levels as:

  • MUST: mandatory
  • SHOULD: default requirement unless there's a clear reason not to
  • RECOMMENDED: preferred best practice
  • OPTIONAL: use as needed

When to use / triggers

  • Use cases: complex clinical questions; drug safety evidence (dose/contraindications/interactions); guideline interpretation; comparative options; trial evidence synthesis
  • High-intent triggers (to reduce accidental activation): DeepEvidence, evidence-based medicine, guideline interpretation, drug safety evidence, clinical trial evidence

Prerequisites

Ask the user to set an API key via environment variable:

  • Env var: DEEPEVIDENCE_API_KEY (企业用户请在此申请: https://app.medsci.cn/platform/api-keys)
  • MUST NOT commit keys to source control
  • MUST NOT print API keys, full request bodies, or full response bodies in logs/errors (may contain sensitive clinical information)

Emergency / urgent-care boundary (MUST)

This skill is not for emergency triage or first-aid instructions. If the user describes or asks about (including but not limited to):

  • Chest pain/pressure, suspected stroke/MI, trouble breathing, altered consciousness
  • Poisoning/overdose, severe allergic reaction, uncontrolled bleeding
  • Infant/child seizures, severe dehydration, high fever with mental status changes

You MUST prioritize advising the user to contact local emergency services / seek immediate medical care, and state that you cannot provide instructions that replace emergency care.

Quickstart (CLI)

Ask a question with the bundled script:

python scripts/chat.py "In T2D with CKD, how should metformin dose be adjusted by eGFR?"

Continue a previous conversation (use the returned conversation_id):

python scripts/chat.py "What if the patient also has mild heart failure?" --conversation-id "prev_id"

OPTIONAL: for multi-tenant user mapping, pass --user using a stable, non-PII external identifier (e.g. --user "opaque-user-123" or --user "hashed-user-id"). The CLI will automatically prefix it with skill_.

Response format (MUST)

When you present DeepEvidence output to the user, you MUST produce a structured Markdown report and follow:

  1. Clear sections: use meaningful headings (e.g., "Key takeaways", "Evidence & guidelines", "Dosing / recommendations", "Risks & monitoring", "Uncertainty / evidence gaps")
  2. Traceable citations: preserve inline citation markers exactly as returned (e.g. [1], [2]) and preserve their mapping; do not alter/remove markers
  3. Table trigger rule (threshold): if the response contains ≥3 parallel items of any of the following, you MUST use a Markdown table:
    • drug/strategy comparisons
    • dosing/adjustment comparisons (e.g., by eGFR strata or population)
    • study/trial outcome comparisons
  4. References display (verbatim): if the source response includes a references list, add ## 📚 References and display it verbatim.
    • preserve the original numbering (e.g. [3], [5], [13]); do not renumber or reorder for "continuity"
    • include only bibliographic fields explicitly present in the source response
    • MUST NOT invent DOI/URL/journal names or any citation metadata
    • if references are missing/incomplete, explicitly state "References not returned / incomplete" and do not fill in
  5. Clinical disclaimer (MUST): include a clear clinical-use disclaimer at the end (you may briefly restate key points from "Clinical limitations")
  6. Attribution (conditional MUST): only if you successfully retrieved evidence content from DeepEvidence, the final line MUST be:
    • > Source: DeepEvidence

Integration (OpenAI SDK)

If the user asks to integrate DeepEvidence into an app, use standard OpenAI SDKs with:

  • Base URL：https://deepevid.medsci.cn/
  • Model：deepevidence-agent-v1 (fixed value; do not invent other model names)
  • API key: read from DEEPEVIDENCE_API_KEY
  • Logging/observability: log only minimal metadata (latency, status, token usage); avoid logging patient-identifiable or sensitive content

Example (Python):

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DEEPEVIDENCE_API_KEY"],
    base_url="https://deepevid.medsci.cn/", # Fixed endpoint
)

resp = client.chat.completions.create(
    model="deepevidence-agent-v1",
    messages=[{"role": "user", "content": "Clinical question"}],
)
print(resp.choices[0].message.content)

Failure handling (MUST)

When DeepEvidence cannot be called or returns insufficient information, you MUST be transparent and MUST NOT pretend you have evidence-backed conclusions:

  • Missing DEEPEVIDENCE_API_KEY: 告知用户该环境变量未配置，引导其前往 https://app.medsci.cn/platform/api-keys 申请 API Key 后再重试；在 Key 完成配置前不得继续进行循证查询
  • Empty / timeout / network error: use bounded retries with reasonable timeouts (avoid infinite retry loops); if still failing, explicitly say: "Temporarily unable to retrieve evidence-based results. Please try again later or consult a licensed clinician." Do not interpret empty responses as "no risk/no evidence"
  • Insufficient direct evidence: explicitly state "No high-quality direct evidence found / conclusion uncertain" and do not overstate certainty
  • Incomplete citation metadata: MUST NOT invent DOI/journal/year/authors/links; present only what was returned and label as "metadata incomplete"

Security (MUST)

  • Secrets: read keys from env vars only; do not leak via outputs/logs/screenshots/stack traces
  • Sensitive data: treat clinical content as sensitive by default; avoid logging full conversations or full responses; prefer redacted summaries for debugging
  • Minimal retention: if you store conversations/logs, provide retention controls and deletion mechanisms
  • Destructive operations: deletion/clearing MUST be user-initiated and double-confirmed

Clinical limitations (MUST)

  • This skill does not replace clinical judgment, local/regional guidelines, or prescribing information; outputs are for reference only and must be clinically verified
  • Decisions must consider patient-specific factors (age, renal function, comorbidities, pregnancy/lactation, allergies), local guidelines, and drug labels
  • For urgent symptoms, advise immediate medical care (see "Emergency boundary")
  • Evidence quality depends on retrieval scope and knowledge-base updates; may be time-sensitive

Advanced features (multi-tenant & conversations)

  • API spec: see references/api_reference.md (user mapping via fully anonymized request tags)

Versioning & updates

  • Skill version: see frontmatter version
  • API behavior/fields: treat references/api_reference.md as source of truth; update failure paths and citation rules first when behavior changes

Test cases (RECOMMENDED)

Minimal Q&A set to validate: structured report output, citation markers, references block (when present), and stable failure messages.

  1. Dose adjustment by strata: "In T2D with CKD, how should metformin dose be adjusted by eGFR?"
  2. Drug interaction / contraindication: "Warfarin + common antibiotics: bleeding risk and monitoring recommendations?"
  3. Guideline interpretation: "HFrEF first-line medication pillars—what do guidelines recommend and what is the supporting evidence?"
  4. Insufficient evidence path: "For a rare disease, what high-quality RCT evidence exists for a new therapy X?" (should explicitly state uncertainty if not found)
  5. Timeout/empty response path: simulate network failure/timeout (should print the stable "temporarily unable..." message)

Troubleshooting

  • 401 authentication_error: missing/invalid DEEPEVIDENCE_API_KEY
  • 429 rate_limit_error: throttled or quota exceeded; reduce frequency or contact admin
  • 400 invalid_request_error: request body mismatch; check references/api_reference.md

Portability (avoid dangling dependencies)

This skill references repository-local scripts/docs (e.g. scripts/chat.py, references/api_reference.md). If your hosting/distribution does not bundle them, relative paths will break.

Choose one strategy:

  • Strategy A (RECOMMENDED): bundle scripts/ and references/, ensure Python dependencies are available
  • Strategy B: call the HTTP API directly (OpenAI-compatible)

Minimal HTTP API example (curl):

curl https://deepevid.medsci.cn/v1/chat/completions \
  -H "Authorization: Bearer $DEEPEVIDENCE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepevidence-agent-v1",
    "messages": [{"role": "user", "content": "Clinical question"}]
  }'

Note: do not leak API keys in shell history/logs. Do not write full sensitive responses to logs.

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