Anandaswarup
Vadapalli
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EVALUATION / Accepted · ACDSA 2027

An explanation for every audience?

Examining readability, safety, and human–AI agreement in audience-targeted explanations of brain MRI images.

01 / MOTIVATION

The question

Can audience-specific prompting make an explanation more accessible while preserving reliable image interpretation?

02 / METHOD

The approach

Four audience-specific prompts are evaluated on 400 brain MRI images spanning four classes, yielding 1,594 valid explanations.

Evaluation covers readability, hallucination, safety, and audience fit. Human and AI ratings are compared on 160 shared responses, and explicit tumor-class mentions are checked against dataset labels.

03 / EVIDENCE

Findings & scope

Patient and student prompts produced simpler explanations. Exact human–AI agreement on hallucination ratings was only 4%.

Audience adaptation did not ensure reliable class identification. This is an evaluation of generated explanations, not evidence of clinical diagnostic readiness.

READ THE WORK

Related publications

Accepted · ACDSA 2027

Brain MRI Explanations Using LLM Prompting: Effects on Readability, Safety, and Tumor-Class Naming

Saketh Raj Kolar, Anandaswarup Vadapalli

International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications

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