AIMC Topic: Diagnostic Errors

Clear Filters Showing 81 to 90 of 104 articles

Automated mutual exclusion rules discovery for structured observational codes in echocardiography reporting.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Structured reporting in medicine has been argued to support and enhance machine-assisted processing and communication of pertinent information. Retrospective studies showed that structured echocardiography reports, constructed through point-and-click...

Improving diagnosis in health care: laboratory medicine.

Diagnosis (Berlin, Germany)
Accurate and timely diagnosis remains one of the most complex and challenging processes in medicine. Diagnostic errors pose a significant burden on patients and healthcare systems, with laboratory-related errors playing a substantial role, especially...

Artificial Intelligence-Assisted Daily Quality Control System for the Histologic Diagnosis of Gastrointestinal Endoscopic Biopsies: A 1-Year Experience.

Archives of pathology & laboratory medicine
CONTEXT.—: Seegene Medical Foundation, one of the major clinical laboratories in South Korea, developed SeeDP, an artificial intelligence (AI)-based postanalytic daily quality control (QC) system that reassesses all gastrointestinal (GI) endoscopic b...

Challenging cases of hyponatremia incorrectly interpreted by ChatGPT.

BMC medical education
BACKGROUND: In clinical medicine, the assessment of hyponatremia is frequently required but also known as a source of major diagnostic errors, substantial mismanagement, and iatrogenic morbidity. Because artificial intelligence techniques are efficie...

AI and XAI second opinion: the danger of false confirmation in human-AI collaboration.

Journal of medical ethics
Can AI substitute a human physician's second opinion? Recently the published two contrasting views: Kempt and Nagel advocate for using artificial intelligence (AI) for a second opinion except when its conclusions significantly diverge from the initi...

Radiomic analysis of cohort-specific diagnostic errors in reading dense mammograms using artificial intelligence.

The British journal of radiology
OBJECTIVES: This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach.

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Journal of psychopathology and clinical science
Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors...

Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting.

Radiology
Background Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose To estimate the proportion of unremarkable chest radiograph...

Ethical considerations for artificial intelligence in dermatology: a scoping review.

The British journal of dermatology
The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about sk...