Bias in Medical AI: Identifying Risks and Ensuring Fairness
Online 
Artificial Intelligence is revolutionizing healthcare, enabling faster diagnoses, personalized treatments, and improved patient care. From detecting diseases in medical images to predicting patient risks, AI has the potential to enhance healthcare efficiency and accuracy like never before. However, this powerful technology also comes with challenges, one of the most critical being bias in AI models.
Bias in AI can emerge from multiple sources, including unrepresentative datasets, flawed algorithmic design, and systemic inequalities in healthcare. If left unaddressed, these biases can lead to significant disparities in medical decision-making, negatively impacting healthcare outcomes.
AI in medicine should be a tool for progress, not a source of new inequalities. Ensuring fairness in AI means improving the quality, diversity, and transparency of medical AI systems so that all patients, regardless of race, gender, or socioeconomic status, receive equitable healthcare.
The webinar will explore these challenges, featuring expert insights and case studies towards building fair, ethical, and unbiased AI systems in medicine.
The webinar is organized in the framework of the Horizon Project ENFIELD – European Lighthouse to Manifest Trustworthy and Green AI – and it will take place on the Teams platform (via this link).
The ENFIELD Project is co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or DG CNECT. Neither the European Union nor the granting authority can be held responsible for them.
