Microsoft logo: go to microsoft.com

Realizing the AI promise:
bringing solutions to market, today

These stories, research projects and technology capabilities from Microsoft and its partners around the world, are a great start to unravel the magic behind AI in health and understand how systems of intelligence are changing the lives of patients and the work of medical practitioners.

Optimizing Clinical Effectiveness

Partner projects
Research projects
Research

HealthVault Insights

Using machine learning and analytics, HealthVault Insights is designed to generate new insights about patient health, drive adherence to care plans, and encourage patient engagement.

Read more

BACK
Research

Health Bot

Access health data in an entirely new way with health bot, a project to leverage conversational intelligence, rich user understanding, and world knowledge for smarter triage and information lookup.

Read more

BACK
Partner

ScanDiags

ScanDiags shortens time and cost of MRI-based diagnosis through image analysis that leverages Artificial Intelligence. The solution detects visible structures and pathologies and generates a written report that can then be completed with a radiologist's or doctor's conclusion. A catalog of 400 different structures is currently being developed. An initial set will be available during Spring 2017.

Video | Website

BACK
Partner

Connected ICU

DevScope – HVITAL processes massive amounts of hospital data, 24/7, giving clinicians advance notice of oncoming patient risk, and thus the opportunity to avoid them. It can predict up to 30% of ICU admissions, 7 days in advance, based on assessments of clinical deterioration, infection management, and antibiotic misuse.

Read more | News article

BACK
Research

Search algorithms to predict lung cancer

“People tend to whisper their health concerns into search engines on a regular basis” says Eric Horvitz. Using AI to analyze search data provide early warning to people who are candidates for disease screening.

Read more

BACK
Research

InnerEye

The project’s main focus is in the treatment of tumors and monitoring the progression of cancer in temporal studies.

InnerEye builds upon many years of research in computer vision and machine learning. It employs decision forests (as used already in Kinect and Hololens) to help radiation oncologists and radiologists deliver better care, more efficiently and consistently to their cancer patients.

Video

BACK
Partner

Optolexia

Optolexia puts dyslexia risk assessment within reach during a child’s early primary schooling, making early diagnosis and treatment possible and learning easier. Optolexia plots and compares a child’s eye movements while reading to the relevant population at large, to determine if significant anomalies suggest further investigation and treatment.

Video

BACK
Partner

Graphnet

Diagnosing and treating epilepsy is complicated. myCareCentric Epilepsy is the first solution to combine all available data sources to create a network of expert care that improves treatment for patients and reduces stresses on the healthcare system.

Reference case | Website | Video

BACK
Research

Epimed

One of Epimed’s customers, Rede D’Or, reduced the incidence of hospital-induced infections by 20 percent and achieved and reached comparable international benchmark figures in its mortality rate by using the Epimed Monitor System—while minimizing IT costs.

With Azure Machine Learning, predictive analytics are significantly reducing their Hospital Acquired Infection and mortality rates. Epimed is also utilizing health information systems to predict and manage ICU Lengths of Stay (LOS). ICU’s become more efficient, producing results comparable to a 30% increase in hospital beds. Data-driven healthcare is the future.

Reference case | Blog | Another case

BACK
Research

Innovation Race

Personnel from Akademiska Sjukhuset (Uppsala University Hospital), AbbVie, Bristol-Myers Squibb and Microsoft will work together for 52 hours, to develop and try new ideas and models for early diagnosis and better treatment of cancer, with a clear focus on patient benefit.

Press release | Blog | Short video| Long video

BACK
Research

Project Hanover

Project Hanover is making progress in three directions:

  • Machine reading: We are developing NLP technology for converting text into structured databases, which enables us to build genome-scale knowledge bases by automatically reading millions of biomedical articles.
  • Cancer decision support: We are collaborating with the Knight Cancer Institute to develop AI technology or cancer precision treatment, with a current focus on developing a machine learning approach to personalizing drug combinations for Acute Myeloid Leukemia (AML), where treatment hasn't improved in the past three decades.
  • Chronic disease management: Our long-term goal is to develop AI technology for predictive and preventive personalized medicine to combat the soaring cost of caring for cancer and other chronic diseases, which accounts for nearly 90% of the U.S. healthcare spending.

Blog

BACK
Partner

Kanteron

Information is only as good as the ability to use it, and Jorge Cortell ‘s vision is to make it available and usable to every medical professional involved in a case, to facilitate the most customized, effective treatment. Using machine learning and Microsoft Azure, images are standardized for all platforms, genomic data is shared, and relevant biological information is amassed to enable the best treatment.

Blog

BACK