Artificial Intelligence & Machine Learning in Oncology

Ci4CC AI / ML / Deep Learning Initiative 

Ci4CC Community requesting a reboot and a more hands on approach towards a Deep Learning initiative bringing together the NCI and Community Cancer Center communities with industry leaders in data science, machine learning and artificial intelligence.

Goal is to identify opportunities and form partnerships to develop products that take solutions into practice. This initiative will lead and support studies on standards, implementation and adoption.  

This program will initially include the following areas. A dedicated panel of experts will review and advise the initiative moving forward.

  • Decision support tools in oncology. 
  • Real World Evidence and Applied Informatics
  • Data Lakes, Precision Medicine Platforms, and Deep Learning
  • Industry / Pharma / Biotech Collaborations and funded projects

Abstract:  As cancer centers collect huge amounts of data through EMR systems, imaging devices, genomic tests, wearables, and patient reported outcomes platforms, and develop more sophisticated big data analytics capabilities, they are starting to move from basic descriptive analytics towards the realm of predictive analytics and technology-enabled real-time decision support. Application of Machine learning and deep learning in healthcare are rapidly becoming some of the most discussed, perhaps most hyped topics in healthcare analytics. This may be just one of the steps along the journey to healthcare analytics maturity, but it actually represents a huge leap forward for early adopters. Machine learning is a well-studied discipline with a long history of success in many industries. Oncology research and care can learn valuable lessons from this previous success to jumpstart the utility of predictive analytics for improving cancer research, care and treatment. 

High-value use cases for predictive analytics exist throughout oncology ecosystem. The opportunity that currently exists for cancer centers is to define what “predictive analytics” means to them and how can it be used most effectively to make improvements. Predictions are most useful when the knowledge can be translated into action. Adoption, the willingness to intervene, is the key to harnessing the power of historical and real-time data. Importantly, to best gauge efficacy and value, the predictions, the end users, and the interventions must be integrated within the same system and workflow.

Please send any interest in participation to with subject line : Ci4CC AI Initiative