AI ethics evidence index Search
Source-linked daily record
Evidence record 11068 · automatically gathered

MMCRAG-Resp: a multi-modal corrective retrieval-augmented generation framework for explainable respiratory disease reasoning

BackgroundStandard Retrieval-Augmented Generation (RAG) systems only use semantic similarity to retrieve information, and since this method is quite limiting, it may find clinically irrelevant evidence and produce outputs that are unsafe or hallucinated. This drawback is particularly important in respiratory care, where the diagnosis relies heavily on very accurate physiological indicators such as spirometry patterns and symptom profiles.MethodsWe propose MMCRAG-Resp., a clinically grounded, phy

Record details

Published: 16 July 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Healthcare · Transparency
Retrieved: 17 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: Healthcare · Shared topic: Transparency

Comparative Analysis of Expert, Clinician, and Health Care User Interactions With Summary of Findings Tables: Usability Study

JMIR (Journal of Medical Internet Research) · 15 July 2026

Shared topic: Healthcare · Shared topic: Transparency

Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers

arXiv · 16 July 2026

Shared topic: Healthcare · Shared topic: Transparency

Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening

arXiv cs.AI · 16 July 2026

Shared topic: Healthcare · Shared topic: Transparency

Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation

arXiv cs.HC · 16 July 2026

Shared topic: Healthcare · Shared topic: Transparency

Clinical Audit Logs as Multi-Axial Traces of Care Delivery

arXiv · 16 July 2026

Shared topic: Healthcare · Shared topic: Transparency

Prediction of female reproductive tract infections risk among college-going young adult women in Delhi using explainable artificial intelligence

Frontiers in Artificial Intelligence · 17 July 2026

How to cite this record

ethics.ai (16 July 2026), “MMCRAG-Resp: a multi-modal corrective retrieval-augmented generation framework for explainable respiratory disease reasoning,” evidence record 11068, https://ethics.ai/record/11068 (originally published by Frontiers in Artificial Intelligence).

JSON

Use and limitations

This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.