**Why Medical Professionals Should Be Interested in Generative AI**
As generative AI evolves from simple hype to an essential resource in the healthcare sector, physicians can greatly gain by leveraging its capabilities at the point of care. The introduction of ambient “copilot” applications that produce encounter notes, large language models (LLMs) summarizing recent studies in mere seconds, and the resulting decline in burnout levels among early users showcase how generative AI has the potential to revolutionize medical practice.
**Five Fundamental Concepts and Clinical Analogies**
1. **Neural Networks**: Similar to medical trainees who engage in numerous simulations to identify patterns, neural networks excel at repeated pattern recognition. Tools like Abridge and Nuance DAX adapt from thousands of consultations to refine note-taking formats.
2. **Prompt Engineering**: Clear guidance is crucial in AI just as it is in an emergency department. Incorporating specific context, patient details, and units guarantees precision from the outset.
3. **Large Language Models (LLMs)**: These predictive text engines have progressed to analyze images. GPT-4o provides plain-language summaries of studies, whereas Med-PaLM 2 offers reliable citation management.
4. **Image Generators**: Applications that turn text into slides support educational training. They facilitate the creation of unique-disease X-ray images for resident practice, protecting patient health information (PHI).
5. **Video Generators**: These expand static visuals into dynamic instructional content. For example, eight-second videos illustrate inhaler techniques in clinical waiting environments, improving patient understanding.
**Effective Prompt Formulation**
Implementing a structured prompt framework can boost AI efficacy: Role → Task → Context → Constraints → Expected output. For instance: “You are a board-certified cardiology scribe. Compose a SOAP note for a 62-year-old male with newly diagnosed atrial fibrillation. Include ICD-10 codes, omit billing modifiers, plain text, under 250 words.” Keeping related terms together improves the model’s ability to associate them correctly.
**Rapid Pilot Initiatives (Approximately One Hour Each)**
– **Ambient Notes**: Assess a complimentary scribe during three low-complexity visits and monitor editing time.
– **Literature Summaries**: Employ an LLM to glean three key insights from the latest NEJM article.
– **Synthetic Imaging**: Generate 20 variations of pneumothorax for morbidity and mortality reviews.
– **Micro-Videos**: Create an eight-second Veo clip on the DASH diet for patient portals.
– **Voice Clones**: Produce a multilingual discharge letter using 11 Labs to assess patient recall.
**Necessary Safeguards**
– Confirm all citations and calculations for accuracy.
– Remove personal identifiers from data before submission, using BAA-compliant providers.
– Perform audits to identify biases concerning age, gender, and language.
– Follow the FDA’s current recommendations on AI-integrated devices.
**What Lies Ahead?**
AI’s context windows are expanding enough to include complete textbooks. Future copilots may analyze comprehensive electronic medical records (EMRs) and the latest protocols, improving managerial recommendations. While speed will enhance, the irreplaceable quality of clinical judgment guarantees that the human element remains essential.
The key takeaway: train AI as a junior colleague. Provide explicit direction, check its outputs, and assign routine responsibilities, enabling you to concentrate on the art of healthcare.
*Harvey Castro is a physician, healthcare consultant, and serial entrepreneur with a wealth of experience in the medical field. He can be reached via his [website](http://harveycastromd.info), [Twitter](https://twitter.com/harveycastromd), [Facebook](https://www.facebook.com/HarveyCastroMD), [Instagram](https://www.instagram.com/harveycastromd/), and [YouTube](https://www.youtube.com/harveycastromd). He has authored numerous books on AI and healthcare.*