Reinforcement Learning (RL) is a machine learning paradigm that enhances clinical decision-making for healthcare professionals by addressing uncertainties and optimizing sequential treatment strategies. RL leverages patient-data to create personalized treatment plans, improving outcomes and…
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John J. Hopfield and Geoffrey E. Hinton were awarded the 2024 Nobel Prize in Physics for developing machine learning technology using artificial neural networks. In Chemistry it was awarded to Demis Hassabis and John…
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CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our…
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Artificial intelligence (AI)-enabled decision aids can contribute to the shared decision-making process between patients and clinicians through personalised recommendations. This systematic review aims to understand users’ perceptions on using AI-enabled decision aids to inform…
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Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early…
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A fundamental goal of evaluating the performance of a clinical model is to ensure it performs well across a diverse intended patient population. A primary challenge is that the data used in model development…
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Patient-centred instructions on discharge can improve adherence and outcomes. Using GPT-3.5 to generate patient-centred discharge instructions, we evaluated responses for safety, accuracy and language simplification. When tested on 100 discharge summaries from MIMIC-IV, potentially…
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What You Should Know:
– Today, the Digital Medicine Society (DiMe) unveiled a suite of resources to guide industry in evaluating international regulatory pathways. These resources equip digital health technology developers with essential tools to compare…
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Online cognitive tasks are gaining traction as scalable and cost-effective alternatives to traditional supervised assessments. However, variability in peoples’ home devices, visual and motor abilities, and speed-accuracy biases confound the specificity with which online…
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We developed an interpretable model, BOUND (Bayesian netwOrk for large-scale lUng caNcer Digital prescreening), using a comprehensive EHR dataset from the China to improve lung cancer detection rates. BOUND employs Bayesian network uncertainty inference,…
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