Version 1
: Received: 30 September 2024 / Approved: 1 October 2024 / Online: 3 October 2024 (08:30:04 CEST)
How to cite:
Avanzo, M.; Stancanello, J.; Pirrone, G.; Drigo, A.; Retico, A. The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning. Preprints2024, 2024100025. https://doi.org/10.20944/preprints202410.0025.v1
Avanzo, M.; Stancanello, J.; Pirrone, G.; Drigo, A.; Retico, A. The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning. Preprints 2024, 2024100025. https://doi.org/10.20944/preprints202410.0025.v1
Avanzo, M.; Stancanello, J.; Pirrone, G.; Drigo, A.; Retico, A. The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning. Preprints2024, 2024100025. https://doi.org/10.20944/preprints202410.0025.v1
APA Style
Avanzo, M., Stancanello, J., Pirrone, G., Drigo, A., & Retico, A. (2024). The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning. Preprints. https://doi.org/10.20944/preprints202410.0025.v1
Chicago/Turabian Style
Avanzo, M., Annalisa Drigo and Alessandra Retico. 2024 "The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning" Preprints. https://doi.org/10.20944/preprints202410.0025.v1
Abstract
Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 40s with the first abstract models of intelligent machines. Soon later in the 50s and 60s machine learning algorithms such as neural networks and decision trees ignited large enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. The renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician’s decision, and later with neural networks for the detection or classification of malignant lesions in medical images. More recently, the use of natural language processing, recurrent neural networks, transformers and generative models has both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including text analysis of electronic health records, image self-labelling and self-reporting. Thanks to its versatility and impressive results and fueled by the availability of powerful computing resources and open-source libraries, AI is one of the most promising resources for frontier research and applications in medicine.
Keywords
Artificial Intelligence; Medical Imaging; Neural Networks; Machine learning; Deep learning
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
