Cultural Competence

AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit's activation via adjoint operators.

Adjoint operators have been found to be effective in the exploration of CNN's inner workings (Wan an...

Human selection bias drives the linear nature of the more ground truth effect in explainable deep learning optical coherence tomography image segmentation.

Supervised deep learning (DL) algorithms are highly dependent on training data for which human grade...

Your robot therapist is not your therapist: understanding the role of AI-powered mental health chatbots.

Artificial intelligence (AI)-powered chatbots have the potential to substantially increase access to...

Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?

Artificial intelligence (AI)-based diagnostic tools can offer numerous benefits to the field of hist...

Adaptive bias-variance trade-off in advantage estimator for actor-critic algorithms.

Actor-critic methods are leading in many challenging continuous control tasks. Advantage estimators,...

Chatting Beyond ChatGPT: Advancing Equity Through AI-Driven Language Interpretation.

Medical interpretation is an underutilized resource, despite its legal mandate and proven efficacy i...

Deep learning assisted sparse array ultrasound imaging.

This study aims to restore grating lobe artifacts and improve the image resolution of sparse array u...

The value of standards for health datasets in artificial intelligence-based applications.

Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosi...

Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care.

BACKGROUND: Artificial intelligence (AI) is a rapidly evolving field which will have implications on...

A step toward building a unified framework for managing AI bias.

Integrating artificial intelligence (AI) has transformed living standards. However, AI's efforts are...

Deep learning with citizen science data enables estimation of species diversity and composition at continental extents.

Effective solutions to conserve biodiversity require accurate community- and species-level informati...

Natural language processing for mental health interventions: a systematic review and research framework.

Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of obj...

Humans inherit artificial intelligence biases.

Artificial intelligence recommendations are sometimes erroneous and biased. In our research, we hypo...

Algorithmic bias and research integrity; the role of nonhuman authors in shaping scientific knowledge with respect to artificial intelligence: a perspective.

Artificial intelligence technologies were developed to assist authors in bettering the organization ...

Machine Learning: A New Approach for Dose Individualization.

The application of machine learning (ML) has shown promising results in precision medicine due to it...

Exploring infection clinicians' perceptions of bias in Large Language Models (LLMs) like ChatGPT: A deep learning study.

New deep learning-based methods for visualizing ecosystem properties using environmental DNA metabarcoding data.

Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity p...

AI pitfalls and what not to do: mitigating bias in AI.

Various forms of artificial intelligence (AI) applications are being deployed and used in many healt...

Adv-BDPM: Adversarial attack based on Boundary Diffusion Probability Model.

Deep neural networks have become increasingly significant in our daily lives due to their remarkable...

Mitigating underreported error in food frequency questionnaire data using a supervised machine learning method and error adjustment algorithm.

BACKGROUND: Food frequency questionnaires (FFQs) are one of the most useful tools for studying and u...

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data.

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by ...