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The Impact of Artificial Intelligence and Machine Learning in Organ Retrieval and Transplantation: A Comprehensive Review.
Curr Res Transl Med
January 2025
Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
View Article and Find Full Text PDFCan Ordinary AI-Powered Tools Replace a Clinician-Led Fracture Clinic Appointment?
Cureus
December 2024
Orthopaedics, University Hospitals Birmingham National Health Service (NHS) Foundation Trust, Birmingham, GBR.
Introduction Artificial intelligence (AI)-powered tools are increasingly integrated into healthcare. The purpose of the present study was to compare fracture management plans generated by clinicians to those obtained from ChatGPT (OpenAI, San Francisco, CA) and Google Gemini (Google, Inc., Mountain View, CA).
View Article and Find Full Text PDFDifferentiating Cystic Lesions in the Sellar Region of the Brain Using Artificial Intelligence and Machine Learning for Early Diagnosis: A Prospective Review of the Novel Diagnostic Modalities.
Cureus
December 2024
Department of Technology and Clinical Trials, Advanced Research, Deerfield Beach, USA.
This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.
View Article and Find Full Text PDFAbdominal myosteatosis measured with computed tomography predicts poor outcomes in patients with glioblastoma.
Neurooncol Adv
November 2024
Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, Saint Louis, Missouri, USA.
Background: Alterations in cellular metabolism affect cancer survival and can manifest in metrics of body composition. We investigated the effects of various body composition metrics on survival in patients with glioblastoma (GBM).
Methods: We retrospectively analyzed patients who had an abdominal and pelvic computed tomography (CT) scan performed within 1 month of diagnosis of GBM (178 participants, 102 males, 76 females, median age: 62.
State of Lung Cancer in Egypt: Moving Towards Improved Guidelines for Prevention, Screening, Treatment, and Clinical Care Programs.
JTO Clin Res Rep
January 2025
Icahn School of Medicine at Mount Sinai, New York, New York.
Lung cancer remains a leading cause of cancer-related mortality globally and presents significant challenges in Egypt. In 2023, the first annual meeting of the Thoracic Oncology Multidisciplinary Faculty, organized by the Egyptian Cancer Research Network and the Egyptian Society of Respiratory Neoplasms, was held in Cairo, Egypt. The meeting aimed to address gaps in lung cancer management across Egypt and the broader Middle East and North Africa region.
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