Artificial intelligence in health an opportunity for the Baltic Sea Region
One of the main transnational challenges is to improve co-operation and quality between research and industry when applying artificial intelligence tools for life science solutions. Tero Piispanen, Director of International Services at Turku Science Park and vice chairman of ScanBalt says:
“In the Baltic Sea Region there are a number of SMEs and larger companies, who have a need to explore the opportunities that artificial intelligence tools could provide for their business. However they do not always know where and how to test their ideas. This we can and should remediate via Baltic Sea Region collaboration”
In the Baltic Sea Region, a survey conducted January – March 2018 by the Interreg project BaltCityPrevention among local Health IT SME’s indicates that artificial intelligence belongs to the top priorities of future commercial opportunities of the companies.
Several life science research institutes and innovative SMEs have already been quick in embracing the AI technologies but the main challenge they are facing is lack of awareness of what they do, since they are generally still in the initial phases and work mostly locally.
Artificial intelligence, a market in strong growth – But companies at risk
The Artificial intelligence in healthcare market size is estimated to grow from USD 667 million in 2016 to USD 7,988 million by 2022, at an annual growth rate of more than 50%.
The market is primarily being driven by factors like the rise of personalized medicine in tests for clinical decision-making, big data in healthcare industry and the growing adoption of artificial intelligence in genetics.
The healthcare artificial intelligence market has been gaining popularity over the years but is still highly fragmented. Moreover, the market faces a major challenge in form of deployment issues.
According to the report Life sciences 4.0 companies are at risk of falling behind technology competitors in the race to address evolving consumer demands if they are not able to connect, combine and share data in a smart way.
30 November 2020
Public Health Data Bootcamp, 10-14 December 2020