Systemic integration and equitable adoption of Digital Health Technologies (DHTs) require timely, comprehensive, harmonised policies. This paper presents five complementary key enablers: defining DHTs in the scope of fit-for-purpose policy interventions, implementing AI-ready regulatory…
Continue Reading
This multicenter retrospective study developed and validated an AI driven model for automated segmentation and quantitative evaluation of meibomian glands using infrared meibography images acquired by the Keratograph 5M device. A total of 1350…
Continue Reading
Large scale data repositories like the All of Us Research Program are spurring new understanding of health and disease. All of Us aims to create a database of all Americans, addressing patterns of understudy…
Continue Reading
Alzheimer’s Disease and Related Dementias (ADRD) pose a major public health challenge, with a critical need for accurate and scalable tools for detecting cognitive impairment (CI). Readily available electronic health records (EHRs) contain valuable…
Continue Reading
Artificial intelligence (AI) offers a solution to glaucoma care inequities driven by uneven resource distribution, but its real-world implementation remains limited. Here, we introduce Multi-Glau, an three-tier AI system tailored to China’s hierarchical healthcare…
Continue Reading
Symptom monitoring using electronic patient-reported outcomes (ePROs) has demonstrated benefits for patients with cancer, yet the systematic effects for lung cancer remains unknown. This study performed a literature search in Medline, Embase, Cochrane library,…
Continue Reading
Subspecialty knowledge barriers have limited the adoption of large language models (LLMs) in oncology. We introduce Woollie, an open-source, oncology-specific LLM trained on real-world data from Memorial Sloan Kettering Cancer Center (MSK) across lung,…
Continue Reading
Low-income populations have disproportionately low completion of recommended healthcare services, from missed vaccinations to cancer screenings. While machine learning models help identify high-risk patients for targeted treatment, they have rarely been evaluated for quality…
Continue Reading
Electronic health records (EHR) contain data from disparate sources, spanning various biological and temporal scales. In this work, we introduce the Multiple Embedding Model for EHR (MEME), a deep learning framework for clinical decision…
Continue Reading
Fundus fluorescein angiography (FFA) is the gold standard for diagnosing chorioretinal diseases, but its interpretation requires significant expertise and time. Despite generative AI’s enormous potential in medical report generation, automatic FFA interpretation lacks robust…
Continue Reading
