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Evidence record 5210 · automatically gathered

Detecting Clinical Discrepancies in Health Coaching Agents: A Dual-Stream Memory and Reconciliation Architecture

As Large Language Model (LLM) agents transition from single-session tools to persistent systems managing longitudinal healthcare journeys, their memory architectures face a critical challenge: reconciling two imperfect sources of truth. The patient's evolving self-report is current but prone to recall bias, while the Electronic Health Record (EHR) is medically validated but frequently stale. General-purpose agent memory systems optimize for coherence by overwriting older facts with the user's la

Record details

Published: 29 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Healthcare

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arXiv · 23 April 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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arXiv · 26 March 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

Front-End Ethics for Sensor-Fused Health Conversational Agents: An Ethical Design Space for Biometrics

arXiv · 14 March 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

AgentFairBench: Do LLM Agents Discriminate When They Act?

arXiv · 15 June 2026

How to cite this record

ethics.ai (29 April 2026), “Detecting Clinical Discrepancies in Health Coaching Agents: A Dual-Stream Memory and Reconciliation Architecture,” evidence record 5210, https://ethics.ai/record/5210 (originally published by arXiv).

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