Advancements in Computational Pathology and Clinical Decision-Making

Microsoft and Paige researchers develop second-generation foundation models for computational pathology. Med42-v2 suite of clinical language models achieves high accuracy on medical benchmarks. A groundbreaking deep learning model predicts endometrial cancer recurrence. LessonPlanner tool enhances novice teachers' effectiveness in lesson planning.

Sources:
https://www.marktechpost.com/2024/08/13/microsoft-and-paige-researchers-developed-virchow2-and-virchow2g-second-generation-foundation-models-for-computational-pathology/
https://www.marktechpost.com/2024/08/13/med42-v2-released-a-groundbreaking-suite-of-clinical-large-language-models-built-on-llama3-architecture-achieving-up-to-94-5-accuracy-on-medical-benchmarks/
https://www.physiciansweekly.com/predicting-endometrial-cancer-recurrence-with-a-deep-learning-model/
https://www.marktechpost.com/2024/08/13/lessonplanner-a-tool-for-enhancing-novice-teachers-effectiveness-by-integrating-large-language-models-with-structured-pedagogical-strategies-to-improve-lesson-planning-quality/

Outline:
(00:00:00) Introduction
(00:00:45) Predicting Endometrial Cancer Recurrence With a Deep Learning Model
(00:02:53) LessonPlanner: A Tool for Enhancing Novice Teachers’ Effectiveness by Integrating Large Language Models with Structured Pedagogical Strategies to Improve Lesson Planning Quality