AIMC Topic: Humans

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Bias Sensitivity in Diagnostic Decision-Making: Comparing ChatGPT with Residents.

Journal of general internal medicine
BACKGROUND: Diagnostic errors, often due to biases in clinical reasoning, significantly affect patient care. While artificial intelligence chatbots like ChatGPT could help mitigate such biases, their potential susceptibility to biases is unknown.

Evaluation of ChatGPT as a Reliable Source of Medical Information on Prostate Cancer for Patients: Global Comparative Survey of Medical Oncologists and Urologists.

Urology practice
INTRODUCTION: No consensus exists on performance standards for evaluation of generative artificial intelligence (AI) to generate medical responses. The purpose of this study was the assessment of Chat Generative Pre-trained Transformer (ChatGPT) to a...

Artificial intelligence algorithms enhance urine cytology reporting confidence in postoperative follow-up for upper urinary tract urothelial carcinoma.

International urology and nephrology
PURPOSE: In Taiwan, the incidence of urothelial carcinoma of the upper urinary tract (UTUC) is high and intravesical recurrence is approximately 22%-47%. Thus, postoperative cystoscopy and urine cytology follow-up, which require experienced cytologis...

Enhancing medical imaging education: integrating computing technologies, digital image processing and artificial intelligence.

Journal of medical radiation sciences
The rapid advancement of technology has brought significant changes to various fields, including medical imaging (MI). This discussion paper explores the integration of computing technologies (e.g. Python and MATLAB), digital image processing (e.g. i...

Academic-related stressors predict depressive symptoms in graduate students: A machine learning study.

Behavioural brain research
BACKGROUND: Graduate students face higher depression rates worldwide, which were further exacerbated during the COVID-19 pandemic. This study employed a machine learning approach to predict depressive symptoms using academic-related stressors.

A machine-learning-based algorithm for bone marrow cell differential counting.

International journal of medical informatics
BACKGROUND: Differential counting (DC) of different cell types in bone marrow (BM) aspiration smears is crucial for diagnosing hematological diseases. However, a clinically applicable method for automatic DC has yet to be developed.

MultiThal-classifier, a machine learning-based multi-class model for thalassemia diagnosis and classification.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: The differential diagnosis between iron deficiency anemia (IDA) and thalassemia trait (TT) remains a significant clinical challenge. This study aimed to develop a machine learning-based multi-class model to differentiate among Microcytic-...

iBhb-Lys: Identify lysine β-hydroxybutyrylation sites using autoencoder feature representation and fuzzy SVM algorithm.

Analytical biochemistry
Lysine β-hydroxybutyrylation (Kbhb) is newly discovered β-hydroxybutyrylate-derived histone modification which has been associated with the pathogenesis of many human diseases. To further elucidate the biological significance and molecular mechanism ...

Collaboration between clinicians and vision-language models in radiology report generation.

Nature medicine
Automated radiology report generation has the potential to improve patient care and reduce the workload of radiologists. However, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of artificial i...

Healthy nutrition and weight management for a positive pregnancy experience in the antenatal period: Comparison of responses from artificial intelligence models on nutrition during pregnancy.

International journal of medical informatics
BACKGROUND: As artificial intelligence AI-supported applications become integral to web-based information-seeking, assessing their impact on healthy nutrition and weight management during the antenatal period is crucial.