AIMC Topic: Female

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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
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...

Artificial intelligence and machine learning in ocular oncology, retinoblastoma (ArMOR).

Indian journal of ophthalmology
PURPOSE: To test the accuracy of a trained artificial intelligence and machine learning (AI/ML) model in the diagnosis and grouping of intraocular retinoblastoma (iRB) based on the International Classification of Retinoblastoma (ICRB) in a larger coh...

Unveiling Public Stigma for Borderline Personality Disorder: A Comparative Study of Artificial Intelligence and Mental Health Care Providers.

Personality and mental health
Generative artificial intelligence (GAI) programs can identify symptoms and make recommendations for treatment for mental disorders, including borderline personality disorder (BPD). Despite GAI's potential as a clinical tool, stereotypes are inherent...

Evaluation of minor labial salivary gland focus score in Sjögren's disease using deep learning: a tool for more efficient diagnosis and future tissue biomarker discovery.

Journal of autoimmunity
BACKGROUND: Sjögren's Disease (SjD) is histopathologically characterized by focal sialadenitis in minor labial salivary gland biopsies (mLSGB), which is evaluated by utilizing the focus score (FS). Focus score ≥1 identification is a critical step of ...

Implementing artificial intelligence in breast cancer screening: Women's preferences.

Cancer
BACKGROUND: Artificial intelligence (AI) could improve accuracy and efficiency of breast cancer screening. However, many women distrust AI in health care, potentially jeopardizing breast cancer screening participation rates. The aim was to quantify c...

Critical view of safety assessment in sentinel node dissection for endometrial and cervical cancer: artificial intelligence to enhance surgical safety and lymph node detection (LYSE study).

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
OBJECTIVE: This study aims to evaluate the feasibility of video-based assessment rate of Critical Views of Safety criteria for sentinel lymph node dissection in endometrial and cervical cancer. Goal of these Critical Views of Safety is to help standa...

Patient Perception of Artificial Intelligence Use in Interpretation of Screening Mammograms: A Survey Study.

Radiology. Imaging cancer
Purpose To assess patient perceptions of artificial intelligence (AI) use in the interpretation of screening mammograms. Materials and Methods In a prospective, institutional review board-approved study, all patients undergoing mammography screening ...

Automatic Segmentation and Molecular Subtype Classification of Breast Cancer Using an MRI-based Deep Learning Framework.

Radiology. Imaging cancer
Purpose To build a deep learning framework using contrast-enhanced MRI for lesion segmentation and automatic molecular subtype classification in breast cancer. Materials and Methods This retrospective multicenter study included patients with biopsy-p...

Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas.

NMR in biomedicine
Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiomas, SFTs exhibit a myoinositol peak on magnetic resonance spectroscopy (MRS). This study aimed to de...

Evaluation of AI Summaries on Interdisciplinary Understanding of Ophthalmology Notes.

JAMA ophthalmology
IMPORTANCE: Specialized ophthalmology terminology limits comprehension for nonophthalmology clinicians and professionals, hindering interdisciplinary communication and patient care. The clinical implementation of large language models (LLMs) into pra...