2025
MIDAS: AI Benchmarking for Skin Cancer
This research paper describes the creation and evaluation of the Multimodal Image Dataset for AI-Based Skin Cancer (MIDAS), a significant new resource for the development and testing of artificial intelligence models in dermatology. MIDAS is notable for being the largest publicly available dataset of biopsy-proven skin lesions that includes paired dermoscopic and clinical images, reflecting a…
Introductory Hands-on Workshop on IoT-AI for Healthcare
Link: https://ihub-data.ai/archives/events/introductory-hands-on-workshop-on-iot-ai-for-healthcare/ This Introductory Hands-on Workshop on IoT-AI for Healthcare offers a unique opportunity for students to explore the intersection of Internet of Things (IoT) and Artificial Intelligence (AI) in real-world medical applications.
Edition II – AI Revolution in Healthcare
Link: https://events.assimilate.one/AIRevolutioninHealthcare Mar 27 – 28, 2026 Dubai, Dubai – United Arab Emirates
Weekly Digest: Ambient AI Scribes in Clinical Documentation
Published: February 21, 2024Source: NEJM Catalyst Innovations in Care DeliveryAuthors: The Permanente Medical Group (TPMG) Overview The study by The Permanente Medical Group (TPMG) explores the implementation of ambient AI scribe technology to assist clinicians in documenting patient encounters. The pilot, conducted between August and December 2023, aimed to assess the tool’s impact on clinician…
Case Study: Understanding AI Startups in Radiology
Radiology has been at the forefront of AI adoption in clinical practice. Startups in this space often promise rapid image interpretation, early disease detection, workflow optimization, and even diagnostic support. While these innovations offer exciting possibilities, it is critical for clinicians to understand both what these tools offer — and what they don’t. This case…
Case Study: AI Startups in Early Warning Systems
Artificial Intelligence is increasingly being marketed as a solution for identifying early warning signs of clinical deterioration — such as sepsis, cardiac arrest, or respiratory failure. While the promise of anticipating critical illness is compelling, clinicians must approach these tools with caution, especially when the marketing outpaces the science. This page highlights what these AI…