AIMC Topic: Bias
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Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.
Pharmacoepidemiology and drug safety
Jun 1, 2025
PURPOSE: The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that, we conducted a simulation study comparing disease risk scores (DRS) and propensity scores (PS) usi...
Assessing bias in AI-driven psychiatric recommendations: A comparative cross-sectional study of chatbot-classified and CANMAT 2023 guideline for adjunctive therapy in difficult-to-treat depression.
Psychiatry research
Jun 1, 2025
The integration of chatbots into psychiatry introduces a novel approach to support clinical decision-making, but biases in their recommendations pose significant concerns. This study investigates potential biases in chatbot-generated recommendations ...
RoBIn: A Transformer-based model for risk of bias inference with machine reading comprehension.
Journal of biomedical informatics
Jun 1, 2025
OBJECTIVE: Scientific publications are essential for uncovering insights, testing new drugs, and informing healthcare policies. Evaluating the quality of these publications often involves assessing their Risk of Bias (RoB), a task traditionally perfo...
Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives.
Journal of medical ethics
May 21, 2025
BACKGROUND: There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race).
Bias Detection in Histology Images Using Explainable AI and Image Darkness Assessment.
Studies in health technology and informatics
May 15, 2025
The study underscores the importance of addressing biases in medical AI models to improve fairness, generalizability, and clinical utility. In this paper, we present a novel framework that combines Explainable AI (XAI) with image darkness assessment ...
Electromechanical-assisted training for walking after stroke.
The Cochrane database of systematic reviews
May 14, 2025
RATIONALE: Walking difficulties are common after a stroke. During rehabilitation, electromechanical and robotic gait-training devices can help improve walking. As the evidence and certainty of the evidence may have changed since our last update in 20...
Artificial intelligence in dermatology and healthcare: An overview.
Indian journal of dermatology, venereology and leprology
May 13, 2025
Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branch...
Evaluation and Bias Analysis of Large Language Models in Generating Synthetic Electronic Health Records: Comparative Study.
Journal of medical Internet research
May 12, 2025
BACKGROUND: Synthetic electronic health records (EHRs) generated by large language models (LLMs) offer potential for clinical education and model training while addressing privacy concerns. However, performance variations and demographic biases in th...
Pitfalls and Best Practices in Evaluation of AI Algorithmic Biases in Radiology.
Radiology
May 1, 2025
Despite growing awareness of problems with fairness in artificial intelligence (AI) models in radiology, evaluation of algorithmic biases, or AI biases, remains challenging due to various complexities. These include incomplete reporting of demographi...
Generative AI mitigates representation bias and improves model fairness through synthetic health data.
PLoS computational biology
May 1, 2025
Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepresented subpopulations, such as those from specific ethnic backgrounds or genders, do not benefit equ...
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