Remote photoplethysmography (rPPG) offers a non-invasive means of estimating heart rate in telemedicine settings. Yet its reliability remains uncertain due to the limited diversity and ecological validity of existing benchmark datasets. In this work,…
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It’s easy to talk about AI in broad terms. It’s harder to show how the technology supports a person trying to navigate benefits, costs, or a complex health journey. That’s exactly what Mike Serbinis…
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Sjögren’s disease (SjD) is a common systemic autoimmune disease that remains difficult to diagnose early due to non-specific symptoms and lack of definitive biomarkers. This multicentre retrospective cohort study analysed data from 34,958 patients…
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Alzheimer’s Disease (AD), the most common neurodegenerative disease, is underdiagnosed and more prominent in underrepresented groups. We performed semi-supervised positive unlabeled learning (SSPUL) coupled with racial bias mitigation for equitable prediction of undiagnosed AD…
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Accurate detection of lung cancer from Computed Tomography (CT) scans is vital for improving patient survival but remains challenging for deep learning models, which struggle with scale variations of pulmonary nodules and the complex…
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Effective risk stratification is essential in clinical practice, enabling better resource allocation and improved patient outcomes. Although machine learning models have been widely used for risk prediction and stratification in electronic health record (EHR)…
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Lung cancer remains the top cause of cancer death, demanding consistent decisions. This clinically oriented review synthesizes computer-aided diagnosis across classical imaging, machine learning, and deep learning, emphasizing bedside-proven advances: multimodal CT/PET–clinical fusion; small-data…
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Background: The mHealth App was believed as an effective method to support family caregivers to better care for stroke patients. This study aimed to explore the status and the influencing factors of mHealth App…
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Clinical decision-making often simplifies continuous risk data into discrete levels using round-number thresholds. These simplifications can distort risk assessments. To systematically uncover these distortions, we develop an interpretable machine learning model that identifies anomalies…
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This study introduces a machine learning framework to predict effective antiviral combinations for influenza A. It identifies Pimodivir with Epinephrine or L-Adrenaline as synergistic agents, confirmed by experiments demonstrating increased binding affinity and viral…
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