Microsoft, Jackson Lab make strides with AI-enabled precision medicine
Microsoft this week announced new progress in its genomics collaboration with the Bar Harbor, Maine-based Jackson Laboratory.
WHY IT MATTERS
Jackson Lab has been using artificial intelligence tools, developed as part of Microsoft's Project Hanover, to help manage the vast amount of genomic data needed to power its precision medicine initiatives.
Specifically, the technology has helped the laboratory scale up its Clinical Knowledgebase, or CKB too – a vast searchable database that helps oncologists and other healthcare experts make detailed interpretations of complex sequencing and maintain troves of leading-edge insights – drawn from thousands of cancer research papers each day – to help drive personalized treatments.
The machine learning technology, which is still evolving, is increasingly able to "read" complex medical and research documents – trained to highlight important and relevant information contained within them such as new insights into genetics, drugs and patient response.
That mining of disparate knowledge sources means clinicians can save hours finding and curating relevant data, targeted to specific genomic profiles.
Crucially, human supervision plays a big part in the augmented intelligence process here. Human curators at Jackson Lab working on CKB are enabled focus on flagged oncology and genomics studies, validating their accuracy and using them, or not, as needed.
"Our goal is to make the human curators superpowered," said Hoifung Poon, director of precision health natural language processing at Microsoft and the lead researcher on Project Hanover, in a company blog post.
"With the machine reader, we are able to suggest that this might be a case where a paper is talking about a drug-gene mutation relation that you care about,” he explained. “The curator can look at this in context and, in a couple of minutes, say, 'This is exactly what I want,' or 'This is incorrect.'"
THE LARGER TREND
Artificial intelligence will be a critical must-have for the hugely data-intensive realities of personalized care. And an array of AI and machine learning tools are fast-evolving to help advance the potential or precision medicine.
Properly deployed, AI has huge promise to enable big advances in treatment possibilities. But having a clear-eyed understanding of its capabilities and a strategy for its implementation is essential.
ON THE RECORD
"For something that really matters like cancer treatment where there are thousands of new research papers being published every day, we actually have a shot at having the machine read them all and help a board of cancer specialists answer questions about the latest research,"said Peter Lee, corporate vice president of Microsoft Healthcare, in a statement.
"Because there is so much data and so many complexities, without embracing and incorporating artificial intelligence and machine learning to help in the interpretation of the data, progress will be slow," added Susan Mockus, associate director of clinical genomic market development at Jackson Laboratory Farmington, Connecticut-based genomic medicine institute.