AI Tools for Healthcare Professionals
What You'll Learn
In this lesson, you will explore the specific AI tools available to healthcare professionals today. You will learn about general-purpose AI assistants, healthcare-specific platforms, and how to evaluate which tools are appropriate for clinical versus non-clinical use. By the end, you will know which tools to consider and how to start using them responsibly.
General-Purpose AI Assistants
Several widely available AI tools can be useful for healthcare professionals in non-clinical tasks such as drafting patient education materials, summarizing research, and administrative writing.
ChatGPT (OpenAI)
ChatGPT is the most widely known large language model (LLM). It can help with drafting correspondence, explaining complex medical concepts in patient-friendly language, summarizing articles, and brainstorming differential diagnoses for educational purposes. The paid version (ChatGPT Plus) offers GPT-4, which demonstrates stronger reasoning on medical questions.
Important: ChatGPT is not FDA-cleared for clinical decision-making. Do not enter protected health information (PHI) into it unless your organization has a HIPAA-compliant enterprise agreement (such as ChatGPT Enterprise or Azure OpenAI).
Claude (Anthropic)
Claude is known for careful, nuanced responses and strong performance on complex reasoning tasks. It is particularly useful for analyzing lengthy documents, such as clinical guidelines or research papers, and producing well-structured summaries. Like ChatGPT, it should not receive PHI without a compliant enterprise setup.
Doximity GPT
Doximity, the professional network for physicians, offers DoximityGPT — an AI writing tool built specifically for doctors. It can generate patient-friendly explanations, draft letters to insurance companies, write referral letters, and create patient after-visit summaries. Because it is designed for physicians, its templates and outputs are tailored to clinical workflows.
Healthcare-Specific AI Platforms
Beyond general-purpose tools, a growing ecosystem of AI platforms is designed specifically for healthcare workflows.
Ambient AI Scribes
These tools listen to clinical encounters and generate documentation automatically:
- Nuance DAX Copilot — Integrated with Microsoft and Epic, DAX listens to patient visits and drafts notes directly into the EHR. It is one of the most widely deployed ambient documentation tools.
- Abridge — Used by health systems including UC Health and UPMC, Abridge generates real-time summaries of patient conversations with linked citations to the transcript.
- Suki — An AI assistant that handles voice-based documentation and integrates with multiple EHR systems.
Clinical Decision Support
- Viz.ai — Analyzes medical imaging in real time and alerts stroke teams when it detects a large vessel occlusion, reducing time to treatment.
- Aidoc — Flags critical findings on CT scans for radiologists, prioritizing worklists based on urgency.
- Sepsis Watch (Duke Health) — A machine learning model that predicts sepsis risk and alerts rapid response teams.
Administrative AI
- Notable Health — Automates prior authorizations, patient intake, and scheduling using AI.
- Olive AI — Streamlines revenue cycle management and claims processing.
- Regard — Analyzes patient charts and suggests diagnoses that might be missing from documentation, improving clinical capture.
How to Evaluate an AI Tool
Before adopting any AI tool in your practice, ask these questions:
1. Is It FDA-Cleared or Validated?
For any tool used in clinical decision-making, check whether it has FDA clearance or De Novo authorization. The FDA maintains a public list of AI/ML-enabled medical devices. Tools used only for administrative or educational purposes may not need FDA clearance, but should still have validation data.
2. Does It Handle PHI Appropriately?
If you are entering patient information, the tool must comply with HIPAA. Look for a Business Associate Agreement (BAA) with the vendor. General consumer versions of ChatGPT, Claude, and Google Gemini are not HIPAA-compliant by default.
3. What Data Was It Trained On?
Understanding the training data helps you assess potential biases. A model trained predominantly on data from academic medical centers may not perform well in rural or community health settings. Ask vendors about their training data demographics and validation studies.
4. How Does It Integrate With Your Workflow?
The best AI tool is useless if it adds friction. Look for tools that integrate with your existing EHR (Epic, Oracle Health, MEDITECH), rather than requiring a separate login and manual data transfer.
5. What Is the Evidence?
Look for peer-reviewed studies or at least published case studies demonstrating the tool's effectiveness in real clinical settings, not just marketing claims.
Getting Started Safely
If you are new to AI tools, here is a practical starting path:
- Start with non-clinical tasks — Use ChatGPT or Claude for drafting patient education handouts, summarizing research papers, or writing appeal letters. Do not include any PHI.
- Explore Doximity GPT — If you are a physician on Doximity, try its AI writing tools for clinical correspondence.
- Ask your IT department — Find out what AI tools your organization has already vetted and approved. Many health systems are deploying Nuance DAX or similar tools.
- Attend training — If your organization is rolling out an AI tool, attend every training session offered. Understanding the tool's capabilities and limitations is essential.
- Keep a critical eye — Always review AI-generated content before using it. Look for inaccuracies, hallucinations (fabricated information), and inappropriate suggestions.
Key Takeaways
- General-purpose AI tools like ChatGPT, Claude, and Doximity GPT can assist with non-clinical tasks such as drafting correspondence and summarizing research
- Healthcare-specific AI platforms include ambient scribes (Nuance DAX, Abridge), clinical decision support tools (Viz.ai, Aidoc), and administrative AI (Notable Health, Regard)
- Always evaluate AI tools for FDA clearance, HIPAA compliance, training data quality, workflow integration, and published evidence
- Start with non-clinical tasks and never enter PHI into tools that lack a HIPAA-compliant enterprise agreement
- Treat every AI output as a draft that requires your professional review before use