Healthcare Application Development: Ideal Methods, Plans, and Movements in Medical Industry
Artificial intelligence, particularly large language models (LLMs), can significantly benefit healthcare organizations, enhancing administrative efficiency and improving care quality. However, AI is still not on par with artificial superintelligence, and requires proper guidance to be useful. This is where prompt engineering comes into play.
"Health systems are increasingly relying on AI solutions to lighten burdens, expand care access, and accelerate clinical insights," says Kenneth Harper, general manager of the Dragon product portfolio at Microsoft. "Emphasizing prompt engineering is crucial to help the healthcare industry harness the full potential of AI for improving patient outcomes and streamlining operations. It's a key element driving success and lasting, positive impact through AI."
Prompt engineering is essentially telling an AI solution what to do and how to do it, using precise and effective natural language prompts. This helps the LLM understand how to complete a task and generate accurate and useful answers. For example, a physician could ask the LLM to "summarize three possible treatment plans for a 55-year-old male diagnosed with Type 2 diabetes, and limit each summary to 300 words."
Here are some key best practices for AI prompt engineering in healthcare:
- Specific Prompts: AI prompts should be very specific to avoid irrelevant responses. Clearly and concisely tell the LLM the desired response format, such as a summary, chart, or list.
- Provide Relevant Context With Follow-Up Prompts: Follow-up prompts provide more context and help generate more specific responses. For instance, a follow-up to a prompt about treatments for a patient with diabetes could be, "The patient is immunocompromised due to a recent organ transplant. Adjust the treatment plan to account for potential drug interactions and infection risk."
- Give Examples of Desired Outputs: Users can generate desired outputs by demonstrating proper responses. The AI learns from these examples and can use the knowledge to improve outputs.
- Feedback from Users: As an AI incorporates into a healthcare system, prompt engineering best practices will evolve based on performance. To analyze how the AI is working, get evaluations from doctors and researchers, and use the feedback to tweak and update the prompts.
Different LLMs perform differently, and trial and error is essential in prompt engineering. Users should experiment with the format of their AI prompts to determine the model's response best. As AI gets smarter, there will be more focus on traceability and auditability in the future.
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Here's a handy guide to creating clear and effective AI prompts in healthcare:
- Focus on Key Concepts: Ensure your prompts focus on the key words and concepts relevant to the healthcare task at hand.
- Avoid Unnecessary Words: Omit unnecessary words to make the prompt concise and clear.
- Logical Order: Structure your prompts in a logical order that reflects the natural flow of tasks or questions.
By adhering to these tips, healthcare professionals can get the most out of AI technology, improving diagnostics, personalized medicine, and overall care quality.
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- Incorporating AI into 'healthcare operations' can lead to efficiency improvements and better care, as demonstrated by the use of large language models (LLMs) for summarizing treatment plans or clinical insights.
- To ensure the AI provides accurate and relevant responses, healthcare professionals should employ 'artificial-intelligence prompt engineering' best practices, such as providing specific prompts, contextual follow-up prompts, examples of desired outputs, and soliciting feedback for continuous improvement.
