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The "Self-Discover: LLMs Self-compose Reasoning Structures" app is an educational and experimental tool designed to showcase the capabilities of large language models (LLMs) in automating the selection, adaptation, and implementation of reasoning structures based on the principles outlined in the research paper "Self-discover: LLMs self-compose reasoning structures" by Google DeepMind. The app leverages a combination of predefined reasoning modules and a user-provided task description to generate a structured reasoning approach, facilitating enhanced performance on complex tasks.

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Use Cases Limitations Evidence Owner's Insight
  • Medical Education: Medical students and educators can use the app to simulate and understand diagnostic reasoning, exploring how AI might assist in structuring complex clinical decision-making processes.
  • Clinical Research: Researchers in clinical fields can employ the app to test hypotheses about the automation of diagnostic reasoning or to explore new methodologies for patient data analysis.
  • Professional Development: Healthcare researchers and professionals can use the app to understand how AI might assist or enhance human reasoning skills.
  • Scope of Tasks: The app's effectiveness is limited to the quality and scope of the input task descriptions and examples. It may not perform well on tasks that are poorly defined or extremely complex.
  • Dependence on User Input: The accuracy of the reasoning structures depends significantly on the user’s ability to provide clear and relevant task descriptions and examples.
  • No PII Handling: The app should not be used with personally identifiable information (PII). It also shouldn't be used for making decisions in sensitive areas like healthcare or legal advice without human oversight.

The framework is based on principles from the "Self-discover: LLMs self-compose reasoning structures" paper by Google DeepMind, showcasing potential uses of LLMs in structured problem-solving. While promising, the application in medical settings remains theoretical and experimental, necessitating further validation and testing to ensure reliability and accuracy in clinical applications.

I'm Venkata Chengalvala, the main developer of this app. As an AI consultant for Health Universe, I port high-quality peer-reviewed AI models helpful to clinicians, patients, and/or researchers to Health Universe's platform. I have a Bachelor of Science in Molecular, Cellular, and Developmental Biology (MCDB) and Computer Science from the University of Michigan-Ann Arbor. During my education, I engaged in medical research, co-authoring a literature review on tumor-derived exosomes, cited by 30+ people so far: https://www.sciencedirect.com/science/article/pii/S2211383521001398.

I created this app to demonstrate the potential of large language models (LLMs) such as GPT-4 in handling tasks that require complex, multi-step reasoning—a common necessity in healthcare tasks. By simulating reasoning processes, the app offers a unique tool for clinicians, researchers, and patients to explore the integration of AI in medical decision-making.

Your insights and feedback on this app are greatly appreciated. If you have any thoughts or feedback on this app, feel free to start a discussion in the "Discussions" tab on this page.

Prototype

Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.


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Venkata Chengalvala

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