The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation
arXiv:2603.28387v2 Announce Type: replace-cross Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts. We evaluate 12 open-weight vision-language models (VLMs) on binary classification across two clinical neuroimaging cohorts, \textsc{FOR2107} (affective disorders) and \textsc{OASIS-3} (cognitive decline). Both datasets come with structural MRI data that carries no reliable individual-level diagnostic signal. Under these conditions, smaller VLMs exhibit gains of up to 58\% F1 upon introduction of neuroimaging
The proliferation of multimodal AI models and their application in sensitive domains like clinical diagnostics necessitates rigorous evaluation to ensure legitimate efficacy versus superficial performance.
This research highlights a critical vulnerability in VLM evaluation, showing that prompt framing can misleadingly inflate performance, which can lead to misdiagnosis and erode trust in clinical AI.
The focus for clinical VLM development shifts from purely 'higher F1 scores' to 'genuinely evidenced' performance, requiring more sophisticated and robust evaluation methodologies.
- · AI ethics and safety researchers
- · Robust multimodal AI development platforms
- · Regulatory bodies for AI in healthcare
- · Clinical AI developers with superficial evaluation methods
- · Healthcare systems adopting unverified AI solutions
- · Patients relying on flawed AI diagnostics
Increased scrutiny and demand for transparent, robust evaluation benchmarks for clinical multimodal AI.
A delay in widespread clinical adoption of VLMs as verification processes become stricter and more complex.
The emergence of new sub-disciplines in AI focused on 'deep fakery' detection and 'genuine evidence integration' in multimodal systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG