AI in Clinical Decision Support:
Roadblocks and Opportunities
Niki Athanasiadou, MRes, PhD
Principal Data Scientist at Common Sense Analytics (1)
and Data Scientist at H2O.ai (2)
When: Date TBD, 6pm – 8:30pm
As data processing and storage is becoming cheaper, the main barrier to entry for AI adoption is often data availability. This couldn’t be better exemplified than in medicine, where advancements in data capturing and storage are creating the necessary conditions for efficient AI support systems. AI-enabled clinical decision support includes diagnosis and prognosis, and involves classification or regression algorithms that can predict the probability of a medical outcome or the risk for a certain disease. Several image classification algorithms using medical images have been approved by the FDA as diagnostic tools in the last two years, and more are certain to follow. Similarly, FDA approval has already been given to wearable devices that monitor vital signs to capture irregularities. These early examples demonstrate the huge potential of AI applications in medicine, as the volume and variety of medical data that get captured increases.
More than 80-90% of US hospitals and physician offices are implementing some form of an EHR, and similar or even higher adoption rates are seen globally. Despite persistent outstanding issues, the lack of interoperability between EHR systems or patient history continuity, past barriers to adoption relating to data usability and availability are being overcome. Examples of clinical decision support AI models will be discussed with emphasis on the importance of data augmentation and built-in model interpretability. As EHR information becomes standardized and disease-specific models are being refined, medicine is poised to leverage AI breakthroughs to improve health outcomes.
Niki Athanasiadou is the principal data scientist in Common Sense Analytics LLC, and a data scientist in the Mountain View-based H2O.ai, working closely with companies in the healthcare industry and beyond helping them to expand their capabilities using AI. Having earned a PhD in molecular and cellular biology by the University of Edinburgh (UK), her past experience includes computational modeling in genomic research, medicine, and population health. As a fellow in the NIH (US), Niki prototyped a pipeline that uses genomic and EHR-level information to create personalized models of disease risk. Alongside being published in international peer-reviewed journals, Niki's work has earned several awards in the UK and US, including the NYC Open Data competition for a Data Science project in 2018.