Cultural Competence

Implications of Large Language Models for Quality and Efficiency of Neurologic Care: Emerging Issues in Neurology.

Large language models (LLMs) are advanced artificial intelligence (AI) systems that excel in recognizing and generating human-like language, possibly serving as valuable tools for neurology-related information tasks. Although LLMs have shown remarkable potential in various areas, their performance in the dynamic environment of daily clinical practice remains uncertain. This article outlines multip...

Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology.

In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area for its technological advancements and clinical application. In this review we explore the complex issue of data bias, specifically addressing those encountered during the development and implementation of AI tools in cardiology. We dissect the origins and effects of these biases, which challenge thei...

Prospective evaluation of artificial intelligence (AI) applications for use in cancer pathways following diagnosis: a systematic review.

The role of artificial intelligence (AI) in cancer care has evolved in the face of ageing population, workforce shortages and technological advancemen...

Gender Bias in Artificial Intelligence-Written Letters of Reference.

OBJECTIVE: Letters of reference (LORs) play an important role in postgraduate residency applications. Human-written LORs have been shown to carry impl...

A survey of recent methods for addressing AI fairness and bias in biomedicine.

OBJECTIVES: Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and s...

Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Health Care Professionals.

As the health care industry increasingly embraces large language models (LLMs), understanding the consequence of this integration becomes crucial for ...

DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes.

Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DL...

Characterising global antimicrobial resistance research explains why One Health solutions are slow in development: An application of AI-based gap analysis.

The global health crisis posed by increasing antimicrobial resistance (AMR) implicitly requires solutions based a One Health approach, yet multisector...

Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh.

Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first st...

Contrastive Prototype-Guided Generation for Generalized Zero-Shot Learning.

Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen classes, while only samples from seen classes are available for training....

Unsupervised Sentence Representation Learning with Frequency-induced Adversarial tuning and Incomplete sentence filtering.

Pre-trained Language Model (PLM) is nowadays the mainstay of Unsupervised Sentence Representation Learning (USRL). However, PLMs are sensitive to the ...

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine th...

Inherent Bias in Large Language Models: A Random Sampling Analysis.

There are mounting concerns regarding inherent bias, safety, and tendency toward misinformation of large language models (LLMs), which could have sign...

Understanding Bias in Artificial Intelligence: A Practice Perspective.

In the fall of 2021, several experts in this space delivered a Webinar hosted by the American Society of Neuroradiology (ASNR) Diversity and Inclusion...

Prediction of polyspecificity from antibody sequence data by machine learning.

Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimized to bind a specific target. Taking advantage of...

TNCB: Tri-Net With Cross-Balanced Pseudo Supervision for Class Imbalanced Medical Image Classification.

In clinical settings, the implementation of deep neural networks is impeded by the prevalent problems of label scarcity and class imbalance in medical...

Does Pollyanna hypothesis hold true in death narratives? A sentiment analysis approach.

Pollyanna hypothesis claims that human beings have a universal tendency to use positive words more frequently and broadly than negative words. The pre...

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis.

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regar...

[Ethics and artificial intelligence].

The introduction of artificial intelligence (AI) into radiology promises to enhance efficiency and improve diagnostic accuracy, yet it also raises man...

A scoping review of fair machine learning techniques when using real-world data.

OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, ...