Early detection of eye diseases is vital for preventing vision loss. Existing ophthalmic artificial intelligence models focus on single modalities, overlooking multi-view information and struggling with rare diseases due to long-tail distributions. We propose…
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There are over 7000 rare diseases, some affecting 3500 or fewer patients in the United States. Due to clinicians’ limited experience with such diseases and the heterogeneity of clinical presentations, ~70% of individuals seeking…
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Prostate cancer (PCa) is one of the most common types of cancer, seriously affecting adult male health. Accurate and automated PCa segmentation is essential for radiologists to confirm the location of cancer, evaluate its…
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The ethical integration of artificial intelligence (AI) in healthcare necessitates addressing fairness. AI fairness involves mitigating biases in AI and leveraging AI to promote equity. Despite advancements, significant disconnects persist between technical solutions and…
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Accurate detection of Parkinson’s disease (PD) through speech analysis holds great promise for early diagnosis and improved patient management. However, developing robust machine learning models is challenging due to the decentralized nature of medical…
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We explore biases present in publicly available fetal ultrasound (US) imaging datasets, currently at the disposal of researchers to train deep learning (DL) algorithms for prenatal diagnostics. As DL increasingly permeates the field of…
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What You Should Know:
–DCAI today announced that Novo Nordisk has become a customer of the Gefion AI supercomputer.
– Through this agreement, Novo Nordisk will gain access to Gefion’s unparalleled computational capabilities to process vast datasets, with a…
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Atropos Health, a company that translates real-world clinical data into real-world evidence, announced a partnership with Databricks, a data intelligence and AI provider.By expanding on the Databricks Data Intelligence Platform and leveraging delta sharing,…
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Phenotypic information for cancer research is embedded in unstructured electronic health records (EHR), requiring effort to extract. Deep learning models can automate this but face scalability issues due to privacy concerns. We evaluated techniques…
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Challenges exist with the adoption of Fast Healthcare Interoperability Resources (FHIR) within analytics, including the difficulty in transforming complex data structures, and performance issues when querying large datasets in their native JSON or XML…
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