Surveys

Does an App a Day Keep the Doctor Away? AI Symptom Checker Applications, Entrenched Bias, and Professional Responsibility.

The growing prominence of artificial intelligence (AI) in mobile health (mHealth) has given rise to a distinct subset of apps that provide users with diagnostic information using their inputted health status and symptom information-AI-powered symptom checker apps (AISympCheck). While these apps may potentially increase access to health care, they raise consequential ethical and legal questions. Th...

A comprehensive survey on the use of deep learning techniques in glioblastoma.

Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene expression, driven by alterations in epigenetic mechanisms. The integration of artificial intelligence, inclusive of machine learning algorithms, has...

Classification of short and long term mild traumatic brain injury using computerized eye tracking.

Accurate, and objective diagnosis of brain injury remains challenging. This study evaluated useability and reliability of computerized eye-tracker ass...

Decoupled Unbiased Teacher for Source-Free Domain Adaptive Medical Object Detection.

Source-free domain adaptation (SFDA) aims to adapt a lightweight pretrained source model to unlabeled new domains without the original labeled source ...

A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods.

The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of ...

Knowledge, attitudes and practices of using Indocyanine Green (ICG) fluorescence in emergency surgery: an international web-based survey in the ARtificial Intelligence in Emergency and trauma Surgery (ARIES)-WSES project.

Fluorescence imaging is a real-time intraoperative navigation modality to enhance surgical vision and it can guide emergency surgeons while performing...

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders.

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past stu...

Telephone follow-up based on artificial intelligence technology among hypertension patients: Reliability study.

Artificial intelligence (AI) telephone is reliable for the follow-up and management of hypertensives. It takes less time and is equivalent to manual f...

Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data.

Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. Th...

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 ...

Digital data and personality: A systematic review and meta-analysis of human perception and computer prediction.

In recent years, our increasing use of technology has resulted in the production of vast amounts of data. Consequently, many researchers have analyzed...

ADHD classification with cross-dataset feature selection for biomarker consistency detection.

Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. While numerous intelligent methods are applied...

Quality-driven deep cross-supervised learning network for semi-supervised medical image segmentation.

Semi-supervised medical image segmentation presents a compelling approach to streamline large-scale image analysis, alleviating annotation burdens whi...

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 appl...

Machine learning approach for the development of a crucial tool in suicide prevention: The Suicide Crisis Inventory-2 (SCI-2) Short Form.

The Suicide Crisis Syndrome (SCS) describes a suicidal mental state marked by entrapment, affective disturbance, loss of cognitive control, hyperarous...

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...

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numer...

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability.

Posture analysis is important in musculoskeletal disorder prevention but relies on subjective assessment. This study investigates the applicability an...

Research on a machine learning-based adaptive and efficient screening model for psychological symptoms of community correctional prisoners.

Community correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools fo...