Large Language Models (LLMs) are showing incredible promise, but before they can be safely deployed in real-world biomedical and healthcare applications, they must undergo rigorous evaluation.
LLM for Biomedical QA: Bridging the Semantic-Precision Gap in Multi-Hop Reasoning Read Post »
Preeclampsia, a complex and potentially life-threatening hypertensive disorder of pregnancy, affects 5-10% of pregnancies worldwide. It remains a leading cause of maternal and perinatal mortality, largely because early and effective prediction remains a significant challenge.
Can AI Help Us Predict Preeclampsia? Announcing Our New Umbrella Review Read Post »
Natural Language Models (NLMs) are rapidly being explored for their potential to analyze complex medical data. However, Clinical Trial Reports (CTRs) present a unique and high-stakes challenge.
Safe AI for Sensitive Data: Our NLI4CT SemEval 2024 Submission Read Post »
Amid the rapid acceleration of artificial intelligence (AI) across healthcare, a critical question remains: is this new technology truly aligned with the principles of patient-centred care (PCC)?
Aligning AI with Patients: Our New Protocol for Evaluating Patient-Centred AI Read Post »
In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at
Artificial intelligence based prediction on lung cancer risk factors using deep learning Read Post »