An unvalidated multi-agent framework is proposed to automate clinical data pipelines from ingestion to inference for tabular and imaging data, with no reported benchmarks.
Self-Explanation in Social AI Agents
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abstract
Social AI agents interact with members of a community, thereby changing the behavior of the community. For example, in online learning, an AI social assistant may connect learners and thereby enhance social interaction. These social AI assistants too need to explain themselves in order to enhance transparency and trust with the learners. We present a method of self-explanation that uses introspection over a self-model of an AI social assistant. The self-model is captured as a functional model that specifies how the methods of the agent use knowledge to achieve its tasks. The process of generating self-explanations uses Chain of Thought to reflect on the self-model and ChatGPT to provide explanations about its functioning. We evaluate the self-explanation of the AI social assistant for completeness and correctness. We also report on its deployment in a live class.
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Agentic AI framework for End-to-End Medical Data Inference
An unvalidated multi-agent framework is proposed to automate clinical data pipelines from ingestion to inference for tabular and imaging data, with no reported benchmarks.