herenciageneticayenfermedad: Artificial Intelligence in Healthcare | Medical News | Medical Articles

martes, 28 de julio de 2020

Artificial Intelligence in Healthcare | Medical News | Medical Articles | Artificial Intelligence in Healthcare |

	Artificial Intelligence in Healthcare
	The latest artificial intelligence in healthcare news from News Medical

Don't Miss the Digital Pathology and AI Congress!

The 6th Digital Pathology and AI Congress returns to New York on the 19-20th of November 2020. A range of speakers within the industry discuss how to utilize AI and digital pathology to enhance patient care and pathology practices. Don't miss out!

Read the Agenda Here

			AI's use in Genomics The scale and complexity of data being thrust out of genomics has become so great that the struggle now lies with converting this data into useful information.

			Microscopy and Artificial Intelligence AI is set to revolutionize the field of microscopy. Recent years have seen numerous research teams successfully integrate AI into vision applications.

	New AI tool could predict risk of lung cancer recurrence Computer scientists working with pathologists have trained an artificial intelligence tool to determine which patients with lung cancer have a higher risk of their disease coming back after treatment, as part of Cancer Research UK’s landmark TRACERx study.

	Artificial intelligence may help study the structures and properties of unknown samples Researchers at the Institute of Industrial Science, The University of Tokyo (UTokyo-IIS) used artificial intelligence to rapidly infer the excited state of electrons in materials.

	Artificial intelligence, probabilistic modeling can improve prediction of survival in cancer patients Improving the prediction of survival indicators in patients with breast cancer using tools from artificial intelligence and probabilistic modeling is the aim of ModGraProDep.

	Structures, Atomic Dynamics of Nanoparticles Predicted by Machine Learning Researchers have demonstrated that new distance-based machine learning methods, developed in the University of Jyväskylä, are capable of predicting structures and atomic dynamics of nanoparticles reliably.