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  • Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devices. NPJ digital medicine Mehta, V., Komanduri, A., Bhadouriya, R. S., Mehta, V., Johnson, M. D., Shrestha, P., Nikolov, M., Jain, B., Shah, N., Schulman, K. 2025; 8 (1): 673

    Abstract

    The rapid integration of artificial intelligence (AI) and machine learning (ML) into medical devices has underscored the need for transparency in regulatory reporting. In 2021, the U.S. Food and Drug Administration (FDA) issued Good Machine Learning Practice (GMLP) principles, but adherence in FDA-reviewed devices remains uncertain. We reviewed 1,012 summaries of safety and effectiveness (SSEDs) for AI/ML-enabled devices approved or cleared by the FDA between 1970 and December 2024. Transparency in model development and performance was assessed using a novel AI Characteristics Transparency Reporting (ACTR) score across 17 categories. The average ACTR score was 3.3 out of 17, with modest improvement by 0.88 points (95% CI, 0.54-1.23) after the 2021 guidelines. Nearly half of devices did not report a clinical study and over half did not report any performance metric. These findings highlight transparency gaps and emphasize the need for enforceable standards to ensure trust in AI/ML medical technologies.

    View details for DOI 10.1038/s41746-025-02052-9

    View details for PubMedID 41249460

    View details for PubMedCentralID 11041443

  • Cholinergic modulation of dopamine release drives effortful behavior. bioRxiv : the preprint server for biology Touponse, G. C., Pomrenze, M. B., Yassine, T., Mehta, V., Denomme, N., Zhang, Z., Malenka, R. C., Eshel, N. 2025

    Abstract

    Effort is costly: given a choice, we tend to avoid it1. But in many cases, effort adds value to the ensuing rewards2. From ants3 to humans4, individuals prefer rewards that had been harder to achieve. This counterintuitive process may promote reward-seeking even in resource-poor environments, thus enhancing evolutionary fitness5. Despite its ubiquity, the neural mechanisms supporting this behavioral effect are poorly understood. Here we show that effort amplifies the dopamine response to an otherwise identical reward, and this amplification depends on local modulation of dopamine axons by acetylcholine. High-effort rewards evoke rapid acetylcholine release from local interneurons in the nucleus accumbens. Acetylcholine then binds to nicotinic receptors on dopamine axon terminals to augment dopamine release when reward is delivered. Blocking the cholinergic modulation blunts dopamine release selectively in high-effort contexts, impairing effortful behavior while leaving low-effort reward consumption intact. These results reconcile in vitro studies, which have long demonstrated that acetylcholine can trigger dopamine release directly through dopamine axons6-11; with in vivo studies that failed to observe such modulation12-14, but did not examine high-effort contexts. Our findings uncover a mechanism that drives effortful behavior through context-dependent local interactions between acetylcholine and dopamine axons.

    View details for DOI 10.1101/2025.06.18.660394

    View details for PubMedID 40667072

    View details for PubMedCentralID PMC12262595