According to the World Cancer Day the key cancer facts are that 10 million people die from cancer every year, at least one third of common cancers are preventable, cancer is the second-leading cause of death worldwide, 70% of cancer deaths occur in low-to-middle income countries, millions of lives could be saved each year by implementing strategies for prevention, early detection and treatment, and the total annual economic cost of cancer is estimated at $ 1.16 trillion. For all the above reasons, AI and ML techniques are breaking into cancer research and oncology, where the potential applications are vast, and include early detection and diagnosis of cancer, subtype classification of cancer, optimisation of cancer treatment and identification of new therapeutic targets. In particular, AI is predicted to change cancer health care by advancing clinical research and drug development. And besides cutting costs, improving trial quality and reducing trial times by almost half, AI is predicted to find novel cancer biomarkers and gene signatures, recruit eligible clinical trial patients in minutes and read volumes of text in seconds. Moreover, breakthrough discoveries involving new diagnostic tools for cancer have seen AI as a major player. The phrase "prevention is better than cure" is often attributed to the Dutch philosopher Desiderius Erasmus in around 1500, but prevention is now synonym to "it's cheaper too", since preventing future illnesses and complications is vital to the future sustainability of health systems and households as well.

As the pharmaceutical and medical care industries move toward adoption of artificial intelligence technologies, new possibilities are arising for improvements in drug trials through streamlined clinical operations processes. Worldwide Clinical Trials' recent partnership with Deep Lens could provide evidence of how future drug studies can benefit from AI-driven technologies. A recent study estimates the average dropout rate for all clinical trials at 30%.1 Such patient discontinuation can necessitate exponential increases in patient numbers to achieve required levels of statistical significance. The goal is to minimize additional recruitment costs and delays by improving efficiencies during study execution. As clinical trial stakeholders seek to streamline clinical processes, artificial intelligence emerges as an innovative approach to improving patient monitoring and clinical care, as well as enhancing and accelerating end point detection.

Dave Billiter, an Ohio native and graduate of Ohio Northern University and Columbus Southern University, joined Nationwide Children's Hospital in 2004, where he led the health system's informatics efforts. There, he oversaw the creation of a digital pathology suite that has since been used by clinicians at 65 institutions in eight countries. And if all goes according to plan, the platform he greenlit will serve as the foundation for an ambitious cloud-hosted, AI-powered toolset that'll be free of charge for doctors around the world. Billiter teamed up with Simon Arkell, the founding CEO of analytics company Predixion Software, to launch Deep Lens, a startup that aims to commercialize some of the technologies Billiter helped develop. The Columbus, Ohio company today exited from stealth with a $3.2 million seed funding round led by Sierra Ventures, with participation from Rev1 Ventures and Tamarind-Hill Fund.