AIMC Topic: Sensitivity and Specificity
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Selection of neuroendocrine markers in diagnostic workup of neuroendocrine neoplasms: The real-world data and machine learning model algorithms.
Cancer cytopathology
May 1, 2025
BACKGROUND: Accurate diagnosis of neuroendocrine neoplasms (NENs) is challenging, especially in poorly differentiated neuroendocrine carcinomas (NECs). This study was aimed to search the best or best combination of neuroendocrine markers in the diagn...
Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) ...
Development and Validation of a Sham-AI Model for Intracranial Aneurysm Detection at CT Angiography.
Radiology. Artificial intelligence
May 1, 2025
Purpose To evaluate a sham-artificial intelligence (AI) model acting as a placebo control for a standard-AI model for diagnosis of intracranial aneurysm. Materials and Methods This retrospective crossover, blinded, multireader, multicase study was co...
External Testing of a Commercial AI Algorithm for Breast Cancer Detection at Screening Mammography.
Radiology. Artificial intelligence
May 1, 2025
Purpose To test a commercial artificial intelligence (AI) system for breast cancer detection at the BC Cancer Breast Screening Program. Materials and Methods In this retrospective study of 136 700 female individuals (mean age, 58.8 years ± 9.4 [SD]; ...
Mitigation of outcome conflation in predicting patient outcomes using electronic health records.
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVES: Artificial intelligence (AI) models utilizing electronic health record data for disease prediction can enhance risk stratification but may lack specificity, which is crucial for reducing the economic and psychological burdens associated w...
Artificial Intelligence Assessment of Chest Radiographs for COVID-19.
Clinical lymphoma, myeloma & leukemia
May 1, 2025
BACKGROUND: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) is reported to have high sensitivity; how...
Incorporating computer vision on smart phone photographs into screening for inflammatory arthritis: results from an Indian patient cohort.
Rheumatology (Oxford, England)
May 1, 2025
OBJECTIVES: Convolutional neural networks (CNNs) are increasingly used to classify medical images, but few studies utilize smartphone photographs. The objective of this study was to assess CNNs for differentiating patients from controls and detecting...
Enhancing Oncological Surveillance Through Large Language Model-Assisted Analysis: A Comparative Study of GPT-4 and Gemini in Evaluating Oncological Issues From Serial Abdominal CT Scan Reports.
Academic radiology
May 1, 2025
RATIONALE AND OBJECTIVES: We aimed to compare the capabilities of two leading large language models (LLMs), GPT-4 and Gemini, in analyzing serial radiology reports, to highlight oncological issues that require further clinical attention.
Assessing the Performance of Artificial Intelligence Assistance for Prostate MRI: A Two-Center Study Involving Radiologists With Different Experience Levels.
Journal of magnetic resonance imaging : JMRI
May 1, 2025
BACKGROUND: Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences.
Application of deep learning models for accurate classification of fluid collections in acute necrotizing pancreatitis on computed tomography: a multicenter study.
Abdominal radiology (New York)
May 1, 2025
PURPOSE: To apply CT-based deep learning (DL) models for accurate solid debris-based classification of pancreatic fluid collections (PFC) in acute pancreatitis (AP).
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