After Capturing a Record US$2.4 Billion in 2019 AI Investments, Healthcare AI Startups Set for Stardom in the Post-COVID-19 World

In 2019, the United States led in Artificial Intelligence (AI) venture capital investment, with 220 deals and a total of US$16 billion. China remained a distant second with 24 deals and US$3.6 billion. Overall, global tech market advisory firm, ABI Research, finds that 2019 was another record year for AI investment, with total investment reaching US$22.7 billion globally. This is a 22% year-on-year increase as compared to 2018, despite multiple headwinds and realignment of interest and priorities.

Automotive, data science, and healthcare and pharmaceuticals are among the key verticals that drive the growth of AI investment. Led by Argo AI, Cruise Automation, and Aurora, the self-driving industry is not only looking at passenger vehicles, but also expects to bring its self-driving capabilities into both long haul and last mile logistics, where human drivers are exposed to long operating hours and safety challenges. In data science, OpenAI, Databricks, DataRobot, and Kabbage continue to push the boundary of machine learning, offering new deep learning platforms, frameworks and models for AI developers.

Among all three, the healthcare and pharmaceutical sector is the most interesting one. There was a significant jump in AI venture capital investments in the sector, from US$1 billion in 2018 to US$2.4 billion in 2019. “This signifies maturing capabilities and growing investor confidence in that domain.,” explains Lian Jye Su, Principal Analyst at ABI Research. “By leveraging advanced analytics and machine-learning algorithms, the healthcare system is now able to start pivoting from an illness-centric to patient-centric model. Leveraging various source of information, such as photo, microscopic image, X-ray, CT scan, and MRI scan, healthcare institutions can bring augmented and accurate disease screenings and treatment to patients. Healthcare startups receiving funding in 2019, such as Babylon Health, Tempus, and Freenome, may start to play pivotal role in post COVID-19 crisis via remote health check and diagnosis, cancer treatment analysis, and delivery of targeted therapeutics.”

Prior to COVID-19, healthcare startups were often confronted with the harsh reality of a conservative IT system in healthcare industries. Healthcare institutions are still relying on hand-written records. This is not useful when it comes to the training of machine learning-based AI that require digital data. Most healthcare systems are siloed in nature, which lacks the right architecture for data streaming, storage, processing, and analysis. Meanwhile, pharmaceutical industry is still relying on labor intensive drug development methods based on existing expertise and knowledge. There has been early effort to combine automation, machine learning, and genomics to synthesize new molecules and polymers for more effective cures.

“Innovation in healthcare generally comes in a slow pace, but once we get past COVID-19, healthcare regulators and agencies may start to pay more attention to the benefits of AI in the automation and augmentation of existing medical and R&D processes. We expect COVID-19 will spur the digital transformation of the healthcare and pharmaceutical sector. The sector will invest solutions for computing, storage and networking servers, big data integration and processing, auto-scaling features, data science toolkits and libraries, and self-service dashboards. This will provide more opportunities for healthcare and pharmaceutical startups to reach out to a wider audience, benefiting more healthcare workers and patients along the way,” concludes Su.

These findings are from ABI Research’s Artificial Intelligence Investment in 2019 application analysis report. This report is part of the company’s AI and Machine Learning research service, which includes research, data, and analyst insights. Based on extensive primary interviews, Application Analysis reports present in-depth analysis on key market trends and factors for a specific technology.