At HealthGPT.Plus, our mission is to revolutionize the way people manage their health. By leveraging the power of large language models, prompt engineering, and diagnostic calculators, we offer possibly the smartest suite of medical products in the world. With three main features designed to match, diagnose, and treat health concerns. This page explores the scientific foundation behind our cutting-edge platform.
1. Large Language Models in Healthcare
HealthGPT.Plus is built upon state-of-the-art AI technology, utilizing large language models like GPT-4 to solve complex problems in healthcare. These models analyze vast amounts of data, enabling our app to provide users with accurate, personalized health information and support.
Studies show that GPT-4, without any specialized prompt crafting, exceeds the passing score on United States Medical Licensing Examination by over 20 points and outperforms models specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned version of Flan-PaLM 540B). It also scored a 75% on the Medical Knowledge Self-Assessment Program.
2. Prompt engineering and self critique/reflection
Prompt engineering has been shown to increase the accuracy and performance of language models like GPT4. Most notably by breaking down complex health problems into smaller, context-free tasks, and having the AI reflect/critique and debate it’s own answers. Our AI platform’s proprietary prompting is based on the following papers.
| Lesson | Paper |
|---|---|
| Break complex tasks into simpler subtasks (and consider exposing the intermediate outputs to users) | AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts |
| Improve output by generating many candidates, and then picking the one that looks best | Training Verifiers to Solve Math Word Problems |
| On reasoning tasks, models do better when they reason step-by-step before answering | Chain of Thought Prompting Elicits Reasoning in Large Language Models |
| Improve step-by-step reasoning by generating many explanation-answer outputs, and picking the most popular answer | Self-Consistency Improves Chain of Thought Reasoning in Language Models |
| The step-by-step reasoning method works great even with zero examples | Large Language Models are Zero-Shot Reasoners |
| Do better than step-by-step reasoning by alternating a ‘selection’ prompt and an ‘inference’ prompt | Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning |
| On long reasoning problems, improve step-by-step reasoning by splitting the problem into pieces to solve incrementally | Least-to-most Prompting Enables Complex Reasoning in Large Language Models |
| Have the model analyze both good and bogus explanations to figure out which set of explanations are most consistent | Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations |
| Think about these techniques in terms of probabilistic programming, where systems comprise unreliable components | Language Model Cascades |
| Eliminate hallucination with sentence label manipulation, and you can reduce wrong answers with a ‘halter’ prompt | Faithful Reasoning Using Large Language Models |
3. Medical Calculators and Diagnostic Flowcharts
HealthGPT.Plus incorporates various medical calculators and diagnostic flowcharts to help users identify potential health risks and diagnose symptoms. These tools, based on evidence-based medicine and clinical guidelines, ensure the accuracy and reliability of the information provided. By combining AI technology with established medical knowledge, our app offers a user-friendly way to navigate complex health information.
Sources
OpenAI. “GPT-4 Technical Report.” ArXiv abs/2303.08774 (2023): n. pag.