Building AI Tools for Medicine: A Developer Guide
🧑💻 For Developers
This guide shows how developers can use Claude Workbench to prototype structured medical summarization tools that clinicians later access through Claude Chat or EMR integrations. You're learning to build systems for clinicians, not how to use them as a clinician.
🔄 Understanding the Two Environments
🧑💻 Claude Workbench
Audience: Developers and engineers
Purpose: Sandbox for prompt engineering and prototype testing
Environment: Anthropic Console → Workbench
Capabilities:
- Define and iterate on system and user prompts
- Choose models (e.g., Claude 3.5 Sonnet, Haiku)
- Evaluate consistency and accuracy using test inputs
- Export prompt logic for embedding in applications
👩⚕️ Claude Chat (claude.ai)
Audience: Clinicians, educators, professionals, end-users
Purpose: Natural-language interface where AI executes predefined logic
Environment: Public web app (claude.ai) or integrated EMR chat module
Characteristics:
- Simplified chat-based workflow
- No system prompt visibility
- Developers' prototype logic runs behind the scenes
🔄 Relationship Between Them
| Role | Tool | Function |
|---|---|---|
| Developer | Claude Workbench | Build and refine structured prompts and logic |
| Clinician | Claude Chat | Use the deployed version through chat interface or EMR plugin |
Analogy: Think of Claude Workbench as the lab and Claude Chat as the clinic. Developers experiment and tune in the lab; clinicians use the result in real patient workflows.
Development Workflow
Part 1: Clinical Reference Data
Understanding the patient scenario and medical context that your AI tool needs to process effectively.
Part 2: Prompt Engineering Process
Using the Workbench to iterate on prompts until you achieve consistent, accurate medical note structuring.
Part 3: Developer's Workbench
Exploring the interactive Workbench interface where you prototype and test your medical AI workflows.
Part 1: Clinical Reference Data
As a developer, you need to understand the clinical context your AI tool will handle. This realistic case serves as your reference dataset for testing and validation. The brain regions shown below represent the medical complexity your system needs to process accurately.
A 68-year-old male presents with a 3-month history of progressive word-finding difficulties and intermittent confusion. His family reports recent episodes of getting lost in familiar places and difficulty managing his finances. His past medical history is significant for hypertension and hyperlipidemia, both well-controlled on medication. He has no history of stroke or traumatic brain injury.
Hover over regions for clinical details relevant to your AI development.
Part 2: Prompt Engineering Process
This section demonstrates the transformation your AI system needs to achieve. As a developer, you'll use the Workbench to iterate prompts until you achieve consistent terminology and sectioning (e.g., Chief Complaint, HPI). The before/after comparison below shows your target output quality.
Part 3: The Developer's Workbench
This is where you prototype and refine your medical AI workflows. The interactive Workbench below shows the actual development environment where you'll build the logic that powers the clinician-facing chat interface. Hover over elements to understand the developer workflow.
Developer Workflow Components
Understanding each component of the Workbench is crucial for effective medical AI development. The interactive diagram above shows your development environment.
- System Prompt: This defines role, constraints, and safety filters for your medical AI. Example: "You are a medical documentation assistant. Structure clinical notes into standard formats using proper medical terminology. Never add clinical information not present in the original note. Always maintain HIPAA compliance."
- User Prompt Area: Where you input test clinical notes to validate your system's performance. Use real examples like the case above to ensure consistent output quality.
- Run Button: Executes your prompt with defined parameters. This is where you test iterations and validate that your logic produces the desired structured output.
- Output Panel: Shows model output for validation. This is where you verify that your prompts consistently produce properly formatted medical notes that meet clinical standards.
- Get Code Button: Exports your finalized prompt logic for integration into production applications, EMR systems, or API endpoints.
Part 4: What the Doctor Sees
Once you've perfected your prompts in the Workbench, this is how clinicians will interact with your deployed system. The chat interface below demonstrates the end-user experience powered by your Workbench logic.
Please structure this patient note:
pt is a 68yo man here w/ family. they say he's been having trouble finding his words for maybe 3 mo. also getting confused. wife says he got lost driving home from the grocery store last week, a trip he's made for 20 yrs. can't handle the bills anymore. hx of htn and high chol, but takes meds for it. denies any weakness or numbness. no head trauma. i checked him out, neuro exam is pretty much normal, cn ii-xii intact, 5/5 strength throughout. but his mmse score was low and he couldn't draw the clock right. seems like something's going on with his thinking. maybe alzheimers? need to investigate further.
Patient: 68-Year-Old Male
Chief Complaint: Cognitive Decline
History of Present Illness: Patient presents with a 3-month history of progressive cognitive decline, characterized by:
- Anomic aphasia (word-finding difficulty)
- Visuospatial disorientation (getting lost in familiar locations)
- Executive dysfunction (difficulty with financial management)
Past Medical History: Hypertension, Hyperlipidemia (well-controlled)
Review of Systems: No history of CVA or TBI. Neurological exam within normal limits except for cognitive deficits noted above.
Assessment: Cognitive decline concerning for possible dementia. Recommend further evaluation including neuropsychological testing and imaging.
🔗 From Development to Deployment
This is the interface your end-users will see once your Workbench logic is integrated into a clinical system. The structured output they receive is generated by the prompts you refined in the development environment. Your role as a developer is to ensure this experience is consistent, accurate, and clinically useful.
⚠️ Important Developer Notes
🚨 Clinical Responsibility Disclaimer
This prototype is for educational purposes only and should not replace clinical judgment. Any medical AI system you develop must be validated by clinical professionals and comply with all relevant medical device regulations before deployment in healthcare settings.
🔒 Technical Implementation Requirements
All clinical tools using LLMs must comply with privacy, audit, and institutional data governance standards. This includes HIPAA compliance, data encryption, audit logging, and integration with existing clinical workflows. Consider partnering with healthcare IT professionals and legal teams for production deployments.
🛠️ Development Best Practices
- Test extensively: Use diverse clinical cases to validate prompt consistency
- Version control: Track prompt iterations and performance metrics
- Clinical validation: Have medical professionals review outputs
- Error handling: Plan for edge cases and unexpected inputs
- Performance monitoring: Implement logging and analytics for production systems
❓ Frequently Asked Questions
Common questions about developing medical AI tools with Claude Workbench, answered for developers.