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Develop a model that can address the issue of implicit biases in healthcare. You will apply different AI techniques to identify and mitigate biases in emergency room scenarios for specific patient groups.
The problem that this Omdena-Learnroll Challenge is trying to solve is the problem of implicit biases in healthcare, specifically in emergency room scenarios. Implicit biases can have a significant impact on patient care, leading to disparities in treatment and outcomes based on factors such as race, gender, or socioeconomic status. By using artificial intelligence to identify these biases in clinical notes, social media, medic-medic conversations, and other available datasets, the challenge is attempting to mitigate their impact and promote fairness and equality in healthcare. The web-based tool that is being developed as part of this challenge will provide triage staff and clinicians with the ability to make informed and unbiased decisions in emergency situations, ensuring that all patients receive the best possible care.
This Omdena-Learnroll Challenge will develop a proof-of-concept (POC) to identify/create an AI model that can accurately help in:
A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.
And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.
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