Paper Type
ERF
Abstract
Artificial intelligence (AI) is increasingly integrated into healthcare systems for diagnosis, treatment planning and healthcare cost. However, there is emerging evidence this fast adoption of AI perpetuates different biases in medical care. The paper examines different algorithmic biases in healthcare and how these biases are impacting different levels of AI decision making. Additionally, we propose a few solutions to address these biases to create a fair AI model. Addressing bias in healthcare AI is essential to ensuring equitable, safe, and effective medical technologies for all populations.
Paper Number
1865
Recommended Citation
Kolte, Prajakta and Sengupta, Soham, "From Innovation to Inequity? Evaluating Bias in AI-Based Healthcare Systems" (2026). AMCIS 2026 Proceedings. 26.
https://aisel.aisnet.org/amcis2026/sig_dsa/sig_dsa/26
From Innovation to Inequity? Evaluating Bias in AI-Based Healthcare Systems
Artificial intelligence (AI) is increasingly integrated into healthcare systems for diagnosis, treatment planning and healthcare cost. However, there is emerging evidence this fast adoption of AI perpetuates different biases in medical care. The paper examines different algorithmic biases in healthcare and how these biases are impacting different levels of AI decision making. Additionally, we propose a few solutions to address these biases to create a fair AI model. Addressing bias in healthcare AI is essential to ensuring equitable, safe, and effective medical technologies for all populations.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIG DSA