Microsoft, Sophia Genetics team up to drive data-driven medicine

The new Microsoft partnership aims to enhance data interoperability with artificial intelligence and machine learning and drive a global transition to precision medicine.


Multimodal health data sets can be the largest and most complex to structure, analyze and archive. 

The multiyear effort will focus on developing and deploying next-generation healthcare tools that equalize data across silos, improve clinical workflows and elevate standards of care, according to the partnership announcement.

The Sophia Genetics’ data display module leverages Microsoft Azure services to provide AI and machine learning capabilities and deliver data curation at scale.

Sophia DDM’s AI and machine learning platform for multimodal purposes analyzes clinical, biological, genomics and radiomics data, and in the future, could offer insights for digital pathology, proteomics and metabolomics.

It’s currently used by hospitals, laboratories and biopharma institutions for research in the United States and other countries. 

Providers using Sophia Genetics’ platform on Azure will be able to scale their ability to aggregate multimodal data types to extract insights within existing workflows, according to Sophia Genetics.

The goal is to improve clinical outcomes and make patient care more personalized by connecting institutions – paving the way to precision medicine, Dr. Jurgi Camblong, co-founder and CEO, explained in the company’s statement. 

“Sophia Genetics will help accelerate the transition to a decentralized care model for hospitals, healthcare providers and biopharma by breaking down data silos and delivering innovation at scale,” he said.

The healthcare SaaS company with employees in more than 28 countries provides data insights to support discoveries, treatment decisions and drug development efforts, according to its website.


AI has transformed language and speech analysis, resulting in technological process advantages for many industries. 

But medicine holds greater changes for AI developers, with its large numbers of unique features or signals contained in data from a multitude of sources.

In order to address the technical challenges of AI in generalizing diverse populations, there is a need for novel tools that can “meaningfully process” health data from multiple sources and “provide value across biomedical discovery, diagnosis, prognosis, treatment and prevention,” according to the recent Multimodal Biomedical AI research by Julian Acosta, Guido Falcone, Pranav Rajpurkar and Eric Topol published last month in Nature.

However, developing trust in healthcare AI is the key as it directs clinical decision-making. AI has the power to increase inequities, particularly when the source is biased data, such as when a dataset does not sufficiently represent patient populations. 

Empowering data tool developers to protect patients and populations is what experts like Dr. Tania Martin-Mercado, digital advisor in healthcare and life sciences at Microsoft, said will take healthcare AI beyond performance.

“In other words, homogenous data teams should be avoided,” she told Healthcare IT News earlier this year during a discussion about addressing equity in healthcare AI.


“Sophia Genetics’ mission is to democratize data-driven medicine,” Dr. David C. Rhew, global chief medical officer for Microsoft, said in the announcement.

“Microsoft is pleased to support this mission by providing secure and scalable cloud infrastructure, alongside Sophia Genetics’ advanced artificial intelligence and machine learning tools and technologies that can help generate actionable insights, which can lead to better health outcomes,” he said.

Andrea Fox is senior editor of Healthcare IT News.
Email: [email protected]

Healthcare IT News is a HIMSS publication.

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