AI in wearable sensing, home monitoring, medical imaging, genomics, biomarkers discovery, pharma, digital health, medical devices, personalized medicine, among others and leading to improved patient outcomes, health and care decision-making and efficiencies in healthcare systems. 



The advent of genomics and personalized medicine has generated tremendous volumes of data at different biological scales with a need for improved decision-making to assist medical practitioners. Novel approaches using AI in drug design, enable a much faster rate of exploration and discovery of novel molecules and candidate drugs. A growing availability of multimodal digital data enable the understanding of complex relationships between genotype, phenotype, and environment through large-scale AI approaches to tease out critical relationships relative to disease. The digitalization of healthcare data in hospitals and across healthcare systems provides access to vast amounts of real-world data on health, disease, and interventions. Novel AI approaches capitalize on the pre-acquired data to significantly speedup the acquisition, reconstruction, and quantification of medical imaging data. Similarly, real world data open the opportunity to develop novel and decentralized clinical trial as well as to generate virtual populations for in silico clinical trials. However, these opportunities come with concerns about reliability and robustness, explainability, privacy, and data cybersecurity. Furthermore, many other healthcare challenges can benefit from the use of AI, including medical personnel or equipment allocation and scheduling, home monitoring and objective patient outcome monitoring in clinical trials, improved medical devices and manufacturing processes, and supply chain optimization.



  • AI in Bioinformatics and Genomics, to improve our understanding of systems biology and disease relationships, leading to novel diagnostics or companion diagnostics for new medical entities with the hope of personalized risk assessments and therapies.
  • AI in Drug Discovery, to accelerate lead identification through cognitive molecule research and information sourcing, decision making during the production chain, pharmacovigilance, repurpose failed assets and to improve the rate at which new medical entities move to clinic.
  • AI in Clinical Trials, to streamline and improve the quality of clinical trial design and/or patient recruitment, and to automate the capture of clinical data from protocol to submission and reduce costs and timelines.
  • AI in Medical Imaging and Sensing, to assist doctors with rapid diagnoses or optimization of therapeutic decisions using complex and heterogenous healthcare data.
  • AI in Healthcare Operations, to improve hospital care provision or manufacturing quality and yield using electronic medical records or advanced business analytics.
  • AI in Supply Chain Management, to ensure that products are distributed efficiently and effectively despite global dynamics such as pandemics.
  • The challenges of regulatory approval for conventional and novel AI approaches.
  • Bridging research and financing towards the application and acceptance of new medical opportunities.



Tulay Adali (University of Maryland)

Selin Aviyente (Michigan State University)

Hsun-Hsien Shane Chang (Novartis)

Gary Fogel (Natural Selection, Inc.)

Alejandro F. Frangi (University of Leeds, UK | KU Leuven, Belgium)

Mathews Jacob (University of Iowa)

Alfonso Limon (Oneirix, Inc.)

Karen Panetta (Tufts University)

Ghulam Rasool (Moffett Cancer Center)

Stephen Smith (ClearSky Medical Diagnostics, Ltd.)