arXiv subjects

Laleh Jalilian

Publications and source records attributed to Laleh Jalilian.

4 recordsLinked to original sources

arXiv2025

Equitable non-contact infrared thermography after solar loading using deep learning

Widely deployed for fever detection, infrared thermometers (IRTs) enable rapid non-contact measurement of core body temperature but are inaccurate in unconstrained environments when skin temperature is transient. In this work, we present the first study on the effect of solar loading--solar radiation-induced elevation of skin but not core temperature--on IRT performance. Solar loading causes poor specificity in IRT fever detection, and the standard procedure is to reacclimate subjects for up to 30 minutes before IRT measurement. In contrast, we propose a single-shot deep learning model that removes solar loading transients from thermal facial images, allowing accurate IRT operation in solar loaded conditions. Forehead skin temperature increases by 2.00°C after solar loading, and our deep learning model, SL-Net, reduces this error by 68\% to 0.64°C. We show that the solar loading effect depends on skin tone, introducing inequity in IRT performance, while SL-Net is unbiased. We open source a diverse dataset of 100 subjects with co-registered RGB-thermal images, and IRT and skin tone measurements. Our work shows that it is possible to use machine learning to correct complex thermal perturbations to enable robust and equitable human thermography.

eess.IV↗

arXiv2024

2-Factor Retrieval for Improved Human-AI Decision Making in Radiology

Human-machine teaming in medical AI requires us to understand to what degree a trained clinician should weigh AI predictions. While previous work has shown the potential of AI assistance at improving clinical predictions, existing clinical decision support systems either provide no explainability of their predictions or use techniques like saliency and Shapley values, which do not allow for physician-based verification. To address this gap, this study compares previously used explainable AI techniques with a newly proposed technique termed '2-factor retrieval (2FR)', which is a combination of interface design and search retrieval that returns similarly labeled data without processing this data. This results in a 2-factor security blanket where: (a) correct images need to be retrieved by the AI; and (b) humans should associate the retrieved images with the current pathology under test. We find that when tested on chest X-ray diagnoses, 2FR leads to increases in clinician accuracy, with particular improvements when clinicians are radiologists and have low confidence in their decision. Our results highlight the importance of understanding how different modes of human-AI decision making may impact clinician accuracy in clinical decision support systems.

cs.HC↗

arXiv2024

The Potential and Perils of Generative Artificial Intelligence for Quality Improvement and Patient Safety

Generative artificial intelligence (GenAI) has the potential to improve healthcare through automation that enhances the quality and safety of patient care. Powered by foundation models that have been pretrained and can generate complex content, GenAI represents a paradigm shift away from the more traditional focus on task-specific classifiers that have dominated the AI landscape thus far. We posit that the imminent application of GenAI in healthcare will be through well-defined, low risk, high value, and narrow applications that automate healthcare workflows at the point of care using smaller foundation models. These models will be finetuned for different capabilities and application specific scenarios and will have the ability to provide medical explanations, reference evidence within a retrieval augmented framework and utilizing external tools. We contrast this with a general, all-purpose AI model for end-to-end clinical decision making that improves clinician performance, including safety-critical diagnostic tasks, which will require greater research prior to implementation. We consider areas where 'human in the loop' Generative AI can improve healthcare quality and safety by automating mundane tasks. Using the principles of implementation science will be critical for integrating 'end to end' GenAI systems that will be accepted by healthcare teams.

cs.CY↗

arXiv2020

Diverse R-PPG: Camera-Based Heart Rate Estimation for Diverse Subject Skin-Tones and Scenes

Heart rate (HR) is an essential clinical measure for the assessment of cardiorespiratory instability. Since communities of color are disproportionately affected by both COVID-19 and cardiovascular disease, there is a pressing need to deploy contactless HR sensing solutions for high-quality telemedicine evaluations. Existing computer vision methods that estimate HR from facial videos exhibit biased performance against dark skin tones. We present a novel physics-driven algorithm that boosts performance on darker skin tones in our reported data. We assess the performance of our method through the creation of the first telemedicine-focused remote vital signs dataset, the VITAL dataset. 432 videos (~864 minutes) of 54 subjects with diverse skin tones are recorded under realistic scene conditions with corresponding vital sign data. Our method reduces errors due to lighting changes, shadows, and specular highlights and imparts unbiased performance gains across skin tones, setting the stage for making medically inclusive non-contact HR sensing technologies a viable reality for patients of all skin tones.

eess.IV↗