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DTSTAMP:20241017T183512Z
LOCATION:Harbor Ballroom A
DTSTART;TZID=America/Los_Angeles:20241017T095500
DTEND;TZID=America/Los_Angeles:20241017T101500
UID:ncs_NCS 2024_sess110_ccrnt216@linklings.com
SUMMARY:Addressing Bias in AI/ML: Ensuring Fairness and Equity in Neurocri
 tical Care
DESCRIPTION:Breakout Session\n\nMurad Megjhani (Columbia University Irving
  Medical Center)\n\nBias in Artificial Intelligence and Machine Learning (
 AI/ML) algorithms presents a significant challenge in neurocritical care, 
 potentially leading to inequitable patient outcomes and treatment disparit
 ies. This session will delve into the complex issue of bias in AI/ML model
 s deployed in neurocritical care settings. Attendees will explore the vari
 ous sources of bias, including data collection practices, algorithm design
 , and societal factors, and their impact on decision-making processes. Thr
 ough case studies and real-world examples, participants will gain insights
  into how bias manifests in AI/ML applications, such as differential diagn
 ostic accuracy across demographic groups or unequal access to healthcare r
 esources. Moreover, the session will discuss strategies for detecting, mit
 igating, and preventing bias in AI/ML models, ranging from algorithmic fai
 rness techniques to diverse and representative dataset curation. By foster
 ing a deeper understanding of bias in AI/ML, this session aims to empower 
 attendees to champion fairness and equity in neurocritical care through re
 sponsible AI implementation.\n\nTrack: Delivery, Quality and Safety\n\nFoc
 us Area: APP Practice, Diversity, Equity, and Inclusion, Global Neurocriti
 cal Care, Informatics, Patient Education, Provider Education Topics (eg fe
 llowship training, competency assessment, etc)\n\nTarget Audience: Introdu
 ctory\n\nSession Chair: Cássia Shinotsuka (Instituto Estadual do Cérebro, 
 Instituto Nacional de Infectologia)
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