Clinician Bias

Clinician bias, encompassing both conscious and unconscious prejudices, significantly impacts healthcare equity by influencing diagnosis, treatment, and prognosis. Current research focuses on identifying and mitigating this bias in various contexts, including the development and application of machine learning models (like deep learning and transformer-based models) for clinical decision support and the analysis of electronic health records for detecting and correcting biased language. Understanding and addressing these biases is crucial for improving healthcare outcomes and reducing disparities across different demographic groups, particularly for underserved populations.

Papers

October 9, 2024

Detecting Bias and Enhancing Diagnostic Accuracy in Large Language Models for Healthcare
Pardis Sadat Zahraei, Zahra Shakeri
Healthcare System Chat Model Subtle Bias Clinician Bias Bias Testing

August 8, 2024

Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinking
Inês Martins, João Matos, Tiago Gonçalves, Leo A. Celi, A. Ian Wong, Jaime S. Cardoso
Machine Learning Global Impact Algorithmic Bias Clinician Bias

July 7, 2024

CLIMB: A Benchmark of Clinical Bias in Large Language Models
Yubo Zhang, Shudi Hou, Mingyu Derek Ma, Wei Wang, Muhao Chen, Jieyu Zhao
Large Language Model New Benchmark Intrinsic Bias Speed Climbing Training Clinician Bias

May 31, 2024

The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes
Alissa A. Valentine, Lauren A. Lepow, Alexander W. Charney, Isotta Landi
Large Language Model Mental Health Clinical Note Implicit Sentiment User Sentiment Exceptional Point Best View Negative Information Clinician Bias

May 17, 2023

Echoes of Biases: How Stigmatizing Language Affects AI Performance
Yizhi Liu, Weiguang Wang, Guodong Gordon Gao, Ritu Agarwal
Electronic Health Record Artificial Intelligence Model Responsible AI Topic Bias Acoustic Echo Artificial Intelligence Performance Language Level Performance Disparity Clinician Bias

January 26, 2023

Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations
Mingquan Lin, Yuyun Xiao, Bojian Hou, Tingyi Wanyan, Mohit Manoj Sharma, Zhangyang Wang, Fei Wang, Sarah Van Tassel, Yifan Peng
Deep Learning Deep Learning Model Fundus Image Glaucoma Diagnosis Vulnerable Population Clinician Bias

Clinician Bias

Papers

Detecting Bias and Enhancing Diagnostic Accuracy in Large Language Models for Healthcare

Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinking

CLIMB: A Benchmark of Clinical Bias in Large Language Models

The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes

Echoes of Biases: How Stigmatizing Language Affects AI Performance

Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations