---
title: "A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis — Stuff That Spins"
description: "arXiv:2607.08038v1 Announce Type: new Abstract: Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often trea…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-10T04:00:00+00:00"
modified: "2026-07-10T06:02:36.566119+00:00"
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# A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis

**Source:** Unknown  
**Published:** July 10, 2026  
**Original:** https://arxiv.org/abs/2607.08038  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.08038v1 Announce Type: new Abstract: Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task, lacking safeguards against missed high-risk alternatives or rigorous verification of their reasoning. Here, we present AegisDx, a safety-oriented framework for hypothetico-deductive clinical reasoning. AegisDx coordinates specialized LLM components through role-specific contracts, structured inter

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