Get Ready for RSNA 2019: What everyone in MedTech is talking about

by Gemseek
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With RSNA 2019 around the corner, it is time to peak into the recent developments in
healthcare and the medical technology industry in particular. Pressed by increasing
investor expectations and struggling to commercialize AI solutions, these are some of
the main strategic directions that MedTech executives have taken to drive business
growth.

Expansion to an Entire Clinical Area

A number of MedTech M&A deals from 2019 indicate that companies are seeking an additional competitive advantage by expanding their capabilities in strategic areas. Pressed by the ever-increasing investor expectations, many are entering adjacent fields in order to cover an entire clinical area. Some examples in this direction are adding interventional oncology to traditional oncology propositions, or enhancing interventional oncology portfolios with new applications.

Bringing AI/ML Innovations to Market

While a lot of industries are betting on AI to solve long-standing issues in their fields, in healthcare data science technologies literally have the potential to save and change lives. That’s why MedTech vendors have doubled their efforts to take their AI developments from the phases of research and prototyping to viable commercial products. Multiple partnerships and incubator programs have been set up between large original equipment manufacturers (OEMs) like Philips, Siemens Healthineers, GE Healthcare and smaller innovative startup companies or health IT vendors.

Stepping into Integrated Diagnostics by Merging Radiomics, Genomics & Pathology

This is a budding field with the potential to bridge the gap between diagnostic disciplines and improve patient outcomes. Already data science technologies exist which can enhance significantly a diagnostic system that integrates data from imaging, genomics, laboratory medicine and pathology. The integration of smart data will advance Precision Medicine – a promising area which is gaining traction with the advancements in data science.

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