Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care
Artificial intelligence (AI) in medical imaging is revolutionizing healthcare by enhancing screening, diagnostics, and clinical care. This integration is crucial as the global healthcare industry strives to enhance accuracy, efficiency, and accessibility. AI and machine learning (ML) extend far beyo...
Збережено в:
| Формат: | Online |
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| Мова: | Англійська |
| Опубліковано: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Предмети: | |
| Онлайн доступ: | ONIX_20250812T110751_9783725835010_45 |
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| _version_ | 1869525473010122752 |
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| collection | Directory of Open Access Books |
| description | Artificial intelligence (AI) in medical imaging is revolutionizing healthcare by enhancing screening, diagnostics, and clinical care. This integration is crucial as the global healthcare industry strives to enhance accuracy, efficiency, and accessibility. AI and machine learning (ML) extend far beyond simple automation, ushering in an era of precision medicine with diagnostics and treatments tailored to individual patients. Advances in deep learning (DL) further enable rapid analysis of complex imaging data, supporting clinical decisions, reducing diagnostic variability, and facilitating early detection—ultimately leading to improved outcomes. Despite its promise, integrating AI into clinical settings faces challenges. Effective AI models require high-quality, annotated training datasets; yet, the scarcity of such data and the imbalance between cases and controls hinder model development. Moreover, real-world medical datasets often include noisy and incomplete data, which can compromise algorithm reliability and introduce biases. The Special Issue “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care” highlights groundbreaking advancements in AI/ML for medical imaging. It addresses challenges posed by limited and imperfect data while presenting innovative methodologies for image-based screening, diagnostics, and management. By showcasing original research and reviews, this issue provides valuable insights into state-of-the-art AI applications poised to effectively address global health challenges. |
| format | Online |
| id | doab-20.500.12854ir-165289 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1652892025-08-12T09:17:56Z Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care Antani, Sameer Xue, Zhiyun Rajaraman, Sivaramakrishnan Artificial intelligence Image-based screening and diagnostics Computer-aided diagnosis Machine learning and deep learning Approaches for learning noise invariant features Approaches to handling data imbalanced training scenarios Learning with noisy/corrupted data or uncertain labels Weakly supervised, semi-supervised, and self-supervised learning Learning in real-world and open-environment scenarios Cardiothoracic and pulmonary diseases Radiographic imaging Computed tomography (CT) Chest X-rays (CXRs) Echo ultrasound thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Artificial intelligence (AI) in medical imaging is revolutionizing healthcare by enhancing screening, diagnostics, and clinical care. This integration is crucial as the global healthcare industry strives to enhance accuracy, efficiency, and accessibility. AI and machine learning (ML) extend far beyond simple automation, ushering in an era of precision medicine with diagnostics and treatments tailored to individual patients. Advances in deep learning (DL) further enable rapid analysis of complex imaging data, supporting clinical decisions, reducing diagnostic variability, and facilitating early detection—ultimately leading to improved outcomes. Despite its promise, integrating AI into clinical settings faces challenges. Effective AI models require high-quality, annotated training datasets; yet, the scarcity of such data and the imbalance between cases and controls hinder model development. Moreover, real-world medical datasets often include noisy and incomplete data, which can compromise algorithm reliability and introduce biases. The Special Issue “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care” highlights groundbreaking advancements in AI/ML for medical imaging. It addresses challenges posed by limited and imperfect data while presenting innovative methodologies for image-based screening, diagnostics, and management. By showcasing original research and reviews, this issue provides valuable insights into state-of-the-art AI applications poised to effectively address global health challenges. 2025-08-12T09:17:54Z 2025-08-12T09:17:54Z 2025 book ONIX_20250812T110751_9783725835010_45 9783725835010 9783725835027 https://directory.doabooks.org/handle/20.500.12854/165289 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10888 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-3502-7 10.3390/books978-3-7258-3502-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725835010 9783725835027 258 open access |
| spellingShingle | Artificial intelligence Image-based screening and diagnostics Computer-aided diagnosis Machine learning and deep learning Approaches for learning noise invariant features Approaches to handling data imbalanced training scenarios Learning with noisy/corrupted data or uncertain labels Weakly supervised, semi-supervised, and self-supervised learning Learning in real-world and open-environment scenarios Cardiothoracic and pulmonary diseases Radiographic imaging Computed tomography (CT) Chest X-rays (CXRs) Echo ultrasound thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care |
| title | Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care |
| title_full | Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care |
| title_fullStr | Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care |
| title_full_unstemmed | Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care |
| title_short | Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care |
| title_sort | artificial intelligence in image based screening diagnostics and clinical care |
| topic | Artificial intelligence Image-based screening and diagnostics Computer-aided diagnosis Machine learning and deep learning Approaches for learning noise invariant features Approaches to handling data imbalanced training scenarios Learning with noisy/corrupted data or uncertain labels Weakly supervised, semi-supervised, and self-supervised learning Learning in real-world and open-environment scenarios Cardiothoracic and pulmonary diseases Radiographic imaging Computed tomography (CT) Chest X-rays (CXRs) Echo ultrasound thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| topic_facet | Artificial intelligence Image-based screening and diagnostics Computer-aided diagnosis Machine learning and deep learning Approaches for learning noise invariant features Approaches to handling data imbalanced training scenarios Learning with noisy/corrupted data or uncertain labels Weakly supervised, semi-supervised, and self-supervised learning Learning in real-world and open-environment scenarios Cardiothoracic and pulmonary diseases Radiographic imaging Computed tomography (CT) Chest X-rays (CXRs) Echo ultrasound thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| url | ONIX_20250812T110751_9783725835010_45 |