BioData World West wrapped up its third year! A mix of experts from industry, academia, and government mingled and mused on the data management supporting the healthcare industry.
Below are the insights from our own Chief Technology Officer, Gunnar Schaefer and Director of Sales, Marco Comianos, who attended.
Gunnar Schaefer, Co-Founder and CTO of @Flywheel_io presents on scaling medical imaging and machine learning in clinical research
Share quality data within your organization
The main focus among conversations at BioData this year was making data accessible across departments and organizations. Letting data flow freely between labs in life sciences organizations creates a feedback loop from health network partners and previously unprofitable drug trials. In health networks, data scientists can highlight opportunities where patients are underserved to create better experiences and processes that can be streamlined to cut costs.
When these different sources of data are merged, unconventional combinations of biomedical data can point to obscure patterns of disease. Scientists from organizations like GenomeAsia, Sidra Medicine, and AstraZeneca presented their findings from blending microbiome and genetic research, genotypic and phenotypic data, and imaging and text data.
In order for machine learning to power artificial intelligence applications, data must be routed, organized, cleaned, and standardized from the moment of creation. More important than proper data storage is the ability to query a system over and over for renewed insight. Genentech introduced the need to store data so it is FAIR: findable, accessible, interoperable, and reusable. That way, data are ripe for query and can integrate together for analysis.
However, it’s important to remember that no matter how well sources are linked together, data must be high-quality and machine learning investigations must be ethically supervised. As Faisal Khan of AstraZeneca put it: “Tortured data will confess to anything.”
Looking forward, expect life sciences companies to adopt better data principles in their data strategies, refine what’s working already, and search for software that bridges the gaps.
Being precise about requirements for precision medicine
Much of the groundwork for precision medicine is now being laid, though mostly in oncology. At BioData, speakers gave direction for its high-value applications.
Today’s genomics research can treat previously-untreated rare diseases. A panel addressed how data sharing must accompany public genomic projects to optimize therapeutic development for rare diseases. Presenters also reported on diversifying the pools for large genome projects. On the treatment side, analysts explained methods to match an individual’s genomic profile with one out of many pre-existing drugs, saving time for patients facing debilitating diseases.
These advancements require access to large amounts of data with well-defined interoperability. Looking forward, expect the general hype around precision medicine to fade, making way for discussions about infrastructure which enable answers to disease-specific precision questions.
Machine learning shortens both ends of drug trials
Beyond the potential for drug discovery using genetic markers, algorithms were showcased which had correctly predicted the pharmacokinetics and effectiveness of drug compounds. Not only does this technology assist researchers and cut costs for developing compounds or finding targets, once therapies are in clinical trials, AI can predict the likelihood of certain subpopulations having an adverse reaction to a drug. Clinical trial pools normally miss these portions of the population, which can result in a public perception crisis.
Looking forward, expect to see AI use with historical clinical data and patient data becoming a competitive factor in shortening the time horizon for successful drug launches. We’ll also see which AI vendors become the most productive partners for life sciences organizations.
AI specialists come ready to partner
If data scientists hold some healthy skepticism of practically applying machine learning, AI specialists showed up with the energy to compensate. AI specialists are drawing talent from universities to specialize in anatomical regions. Companies in this vertical are also starting to partner with each other to complement their deep expertise in one region.
Many AI companies at BioData specialize in genomics and digital slide pathology, so look forward to development and consolidation in this field. Fewer imaging analysis companies were present at BioData – stay tuned for the imaging market insights yet to come out of RSNA!
At RSNA’s Annual Meeting, Flywheel will be exhibiting from December 1st to December 5th in the AI Showcase. Schedule a demo and find us at booth #11618.