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 »
Amid rising advanced artificial intelligence (AI) technologies, global health systems are embracing the instant capability of natural language processing (NLP) applications. These applications maximise large language models (LLM) algorithms for holistic health care services, to improve patient-centred outcomes.
2024: LLM Chatbot for Patient-Centred Outcomes with Yake and Medspacy? 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 »
NSCLC is a highly prevalent cancer and accounts for 85% of cases of lung cancer. Conventional cancer treatments, such as
Breast cancer is one of the leading causes of death for women worldwide. Early screening is essential for early identification,