IEEE Healthcare Summit (IHS)

Integrating BHI and AI to Combat Pandemics

The first IEEE Healthcare Summit commenced on IEEE Day (4 October 2021). Multiple sessions have been recorded for asynchronous participation, available until 31 December 2022. For complimentary registration, click here.

The COVID-19 Data Hackathon, Phase-2, has been ongoing and will continue until 5 Dec. 2022. For all teams who are interested in participating, follow the instructions on this page

On 12 March 2020, the World Health Organization (WHO) declared the COVID-19 (COronaVIrus Disease 2019) outbreak a pandemic. COVID-19 has spread to more than 200 countries, territories, and international conveyances, infected more than 635 million people and taken more than 6.5 million lives globally. This event was initiated to report ongoing progress made in the fight against COVID-19 and to aid in better preparing for future pandemics by integrating Artificial Intelligence (AI) methods and tools with Biomedical and Health Informatics (BHI):

  • Translational bioinformatics: use genomics and proteomics to do SARS-CoV-2 subtyping, and build tools to develop targeted vaccine or drug, to do early screening to limit outbreak, and to perform evolution trajectory prediction;
  • Sensor informatics: use real-time wearable sensor data to monitor asymptomatic and mild-symptom home-based patients, to treat severe-symptom patients in hospitals, and COVID-related sequelae (e.g. neurological and cardiac disease);
  • Imaging informatics: use CT, X-rays, MRI, ultrasound, and other imaging modalities with RT-PCR data to improve diagnosis, prognosis, and monitoring of patients with COVID-19, or other infectious diseases in the event of future pandemics;
  • Clinical informatics: use multimodal data to find effective clinical care workflows for critically ill patients, to track the occurrence of COVID-related sequelae for long term follow-up, and to perform a risk assessment and decision making;
  • Public health informatics: use epidemiological models to analyze outbreak data for supporting population health management, resource supply chains, and future care preparation;
  • Mental health informatics: collect and analyze mental health data during the pandemic to model behavior changes caused by the pandemic, and to evaluate the consequences of policies for future preparedness;
  • Mixed VR/AR and Robotics: integrate BHI with VR, AR, and robotics to effectively visualize omic, imaging, sensor, and population pandemic data, to train medical robots, and to assist public health policymaking for preparedness; and
  • Fairness and ethics: use pandemic data to identify regional, racial, and ethnic disparities in infection and vaccination rates and the causal factors of the disparities, for the preparedness of future pandemics and fair resource allocation.

Topics included, but were not limited to the following:

  • Collection, harmonization, and sharing of pandemic-related multi-modality data
  • Integrating VR and AR with BHI and AI for visualizing pandemic-related multi-modality data for modeling of virus propagation, recurrence, and virulence from epidemiological observations
  • Bioinformatics for rational drug design and vaccine development based on viral subtypes
  • Sensor informatics for monitoring infected patients at home or in ICU
  • Cost-effective in silico modeling of clinical trials in anti-pandemic drug and vaccine development
  • Big Data-enabled citizen-mediated public health policymaking
  • Integrating AI and public health informatics for policy decision-making support and fair resource allocation
  • Medical imaging informatics (chest X-ray, CT scans, histopathology, etc.) for precision detection and diagnosis
  • Integrating BHI with medical robotics for minimizing infections and for surgical intervention planning during pandemic care
  • AI-driven care pathway planning for comorbid patients, and for treatment evaluation and outcome prediction
  • AI-driven rapid testing of the virus in humans for pandemic preparedness