Cultural Competence

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups.

OBJECTIVES: To assess fairness and bias of a previously validated machine learning opioid misuse cla...

Invited Commentary: Quantitative Bias Analysis Can See the Forest for the Trees.

The accompanying article by Jiang et al. (Am J Epidemiol. 2021;190(9):1830-1840) extends quantitativ...

Black Boxes and Bias in AI Challenge Autonomy.

The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability.

Covering: up to the end of 2020. The machine learning field can be defined as the study and applicat...

Reducing Bias in Healthcare Artificial Intelligence.

Artificial intelligence, bias, and patients' perspectives.

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences.

In the field of artificial intelligence, a combination of scale in data and model capacity enabled b...

[A convolutional neural network based model for assisting pathological diagnoses on thyroid liquid-based cytology].

To develop a convolutional neural network based model for assisting pathological diagnoses on thyro...

Deep generative selection models of T and B cell receptor repertoires with soNNia.

Subclasses of lymphocytes carry different functional roles to work together and produce an immune re...

DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction.

The development of deep sequencing technologies has led to the discovery of novel transcripts. Many ...

Regression on imperfect class labels derived by unsupervised clustering.

Outcome regressed on class labels identified by unsupervised clustering is custom in many applicatio...

The risk of racial bias while tracking influenza-related content on social media using machine learning.

OBJECTIVE: Machine learning is used to understand and track influenza-related content on social medi...

Addressing Artificial Intelligence Bias in Retinal Diagnostics.

PURPOSE: This study evaluated generative methods to potentially mitigate artificial intelligence (AI...

On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

A review, recently published in this journal by Fang (2019), showed that methods trained for the pre...

Deep learning and cancer biomarkers: recognising lead-time bias - Authors' reply.

Deep learning and cancer biomarkers: recognising lead-time bias.

Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.

The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection ra...

CheXclusion: Fairness gaps in deep chest X-ray classifiers.

Machine learning systems have received much attention recently for their ability to achieve expert-l...

Rethinking the Approach to Artificial Intelligence for Medical Image Analysis: The Case for Precision Diagnosis.

To date, widely generalizable artificial intelligence (AI) programs for medical image analysis have ...

MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care.

The rise of digital data and computing power have contributed to significant advancements in artific...

Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning.

Accumulating evidence demonstrates the impact of bias that reflects social inequality on the perform...