MEDICAL Prompt Engineering PRO + Clinical RAG
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- Опубліковано 8 вер 2024
- Professional medical /clinical Prompt Engineering 2024: Medprompt.
RAG was so yesterday, welcome to an advanced clinical RAG!
In the realm of AI and medicine, there's a surprising trend where non-fine-tuned models, like GPT-4 with MedPrompt, are outperforming specialized models like Med PALM 2 in medical applications. This raises questions about the efficacy of intensive domain-specific tuning versus advanced prompt engineering in foundation models. Multiple choice datasets, used for benchmarking these models, play a crucial role in this evaluation. The emerging scenario suggests that foundational models like GPT-4 might achieve superior performance in medical tasks without the need for extensive domain-specific training, solely through really sophisticated prompt engineering.
The Shift in AI Strategy for Medical Applications
Microsoft's investigation into whether foundational models can surpass specialized models in medicine has led to an interesting discovery: GPT-4's automated chain of thought reasoning significantly reduces the dependency on specialized human expertise and medical datasets. This approach is contrasted by Google's focus on assisting physicians with hands-free devices for real-time integration of patient conversations into health records, and AWS's emphasis on summarizing patient visits. Meanwhile, top consultancies like Accenture and Deloitte are leveraging Google's language model for their global clients, indicating a diverse range of strategies in applying AI to healthcare.
MedPrompt Methodology and Ensemble Techniques
MedPrompt introduces an innovative approach where GPT-4 autonomously generates high-quality chain of thought prompts for complex medical challenges. This process is augmented by ensemble techniques, combining outputs from multiple model runs to achieve robust results. However, these techniques have a significant computational demand. Microsoft's MedPrompt methodology involves multiple inference runs per question to ensure accuracy, illustrating the trade-off between computational efficiency and performance. This approach suggests a new direction in prompt engineering, prioritizing cost-effectiveness and practicality in medical AI applications.
Limitations and Practical Applications of MedPrompt
Despite its advancements, MedPrompt as an ICL (In-context Learning) based Prompt Engineering endeavor faces limitations, particularly linmited in effect by token length constraints, which affect the model's ability to process comprehensive prompt ensembles. This limitation emphasizes the need for a strategic balance in prompt design to optimize responses within token limits. Furthermore, Microsoft acknowledges the experimental nature of MedPrompt, expressing caution in directly applying these methods in practical medical settings. This caution highlights the gap between theoretical AI advancements and their real-world medical applications, underscoring the need for further testing and validation outside of controlled benchmark environments.
#ai
#medical
#reasoning
This is, hands down, the most under appreciated channel on AI today. I watch a LOT of channels and none of them are as good as this one. 👍
Appreciate that!
Would love to hear more healthcare videos from you! One of the best educational channels on UA-cam hands down
Thanks. More to come!
examples needed how you used this technique and got good results
Wonder about a DIY ensemble strategy: prompt engineer a few different prompts, get resulting responses, summarize
I was hoping there would be a link here for a proper medical vector space, but still interesting video - so for a simple guy this means that if you want a better medical answer for your health issue, you need to google a few solutions for something similar (basically acting as a vector space for llm) and post a few of those problems and diagnoses before asking for your own.
Contact Microsoft and ask for their - as you call it "proper medical vector space".... for free. Smile.
Should we just wait for global corporations to just stitch this in as a basic model operation functionality in some way or another? Or is this their firm profit point and we can keep our dreams to ourselves?
Your content fills me with joy 🥰