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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Artificial intelligence models analyzing speech show remarkable promise for identifying cognitive decline, achieving performance comparable to clinical assessments. However, their “black box” nature poses significant barriers to clinical adoption, as healthcare professionals require transparent…
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Wearable and health app data hold significant potential for healthcare and research, yet fragmented consent mechanisms create challenges for ethical, transparent, and user-controlled data sharing. We describe a centralised consent management system with a…
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The effectiveness of digital health management (DM) in post-discharge care for patients with coronary artery disease (CAD) remains insufficiently explored, particularly across public health emergencies. A total of 24,129 patients with CAD enrolled in…
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Rare diseases (RD) affect 3.5–5.9% of the global population, yet patients often endure a diagnostic odyssey averaging around 5–6 years, navigating multiple specialties and referrals. This study investigates the use of information content (IC)…
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Many workforces are exposed to traumatic events which can precipitate the development of depression, anxiety and posttraumatic stress symptoms. This randomized controlled trial aimed to evaluate if an app-based version of the Skills for…
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Multimodal models have enabled the integration of digital pathology, radiology, clinical information, and omics data to enhance Colorectal cancer (CRC) care. This systematic review critically appraises Multimodal digital pathology techniques applied in CRC, their…
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