91 research outputs found
Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: Present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review
Background: The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world.Main text: Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted.Conclusion: AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care
Artificial intelligence in digital pathology: a diagnostic test accuracy systematic review and meta-analysis
Ensuring diagnostic performance of AI models before clinical use is key to
the safe and successful adoption of these technologies. Studies reporting AI
applied to digital pathology images for diagnostic purposes have rapidly
increased in number in recent years. The aim of this work is to provide an
overview of the diagnostic accuracy of AI in digital pathology images from all
areas of pathology. This systematic review and meta-analysis included
diagnostic accuracy studies using any type of artificial intelligence applied
to whole slide images (WSIs) in any disease type. The reference standard was
diagnosis through histopathological assessment and / or immunohistochemistry.
Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We
identified 2976 studies, of which 100 were included in the review and 48 in the
full meta-analysis. Risk of bias and concerns of applicability were assessed
using the QUADAS-2 tool. Data extraction was conducted by two investigators and
meta-analysis was performed using a bivariate random effects model. 100 studies
were identified for inclusion, equating to over 152,000 whole slide images
(WSIs) and representing many disease types. Of these, 48 studies were included
in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI
94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was
substantial heterogeneity in study design and all 100 studies identified for
inclusion had at least one area at high or unclear risk of bias. This review
provides a broad overview of AI performance across applications in whole slide
imaging. However, there is huge variability in study design and available
performance data, with details around the conduct of the study and make up of
the datasets frequently missing. Overall, AI offers good accuracy when applied
to WSIs but requires more rigorous evaluation of its performance.Comment: 26 pages, 5 figures, 8 tables + Supplementary material
The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision
Diagnostic medical imaging is a key tool in medical care. In recent years, thanks to advances in computer vision research, a subfield of artificial intelligence, it has become possible to use medical imaging to train and test machine learning models. Among the algorithms investigated, there has been a boom in the use of neural networks since they allow a higher level of automation in the learning process. The areas of medical imaging that have developed the most applications are X-rays, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasonography and pathology. In fact, the COVID-19 pandemic has reshaped the research landscape, especially for radiological and resonance imaging. Notwithstanding the great progress that has been observed in the field, obstacles have also arisen that had to be overcome to continue to improve applications. These obstacles include data protection and the expansion of available datasets, which involves a large investment of resources, time and academically trained manpower
Inteligencia artificial en medicina: Selección de métodos, aplicaciones y consideraciones (Parte II)
AI in Oncology - Precision Therapy & Prognosis
Artificial intelligence (AI) has strong logical reasoning abilities and the ability to learn on its own, and it can mimic the human brain's thought process. Machine learning and other AI technologies have the potential to greatly enhance the existing method of anticancer medicine development. However, AI currently has several limits. This study investigates the evolution of artificial intelligence technologies in anti-cancer therapeutic research, such as deep learning and machine learning. At the same time, we are optimistic about AI's future
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