Michele Avanzo

Dr. Michele Avanzo

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Research Focus & Metrics

Academic Career Length

23Years (2004-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 May 2020 in Medical Physics
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Journal Article
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MAMichele Avanzo
Michele Avanzo
LWLise Wei
Lise Wei
JSJoseph Stancanello
Joseph Stancanello
MVMartin Vallières
Martin Vallières
ARArvind Rao
Arvind Rao
INIssam El Naqa
Issam El Naqa

1 June 2017 in Physica Medica
Verified
Journal Article
Closed access
MAMichele Avanzo
Michele Avanzo
JSJoseph Stancanello
Joseph Stancanello
Issam El NaqaIssam El Naqa
Issam El Naqa

4 May 2020 in Strahlentherapie und Onkologie
Verified
Journal Article
Closed access
PDF file
MAMichele Avanzo
Michele Avanzo
JSJoseph Stancanello
Joseph Stancanello
GPGiovanni Pirrone
Giovanni Pirrone
GSGiovanna Sartor
Giovanna Sartor

1 March 2021 in Physica Medica
Verified
Journal Article
Closed access
MAMichele Avanzo
Michele Avanzo
MPMassimiliano Porzio
Massimiliano Porzio
LLLeda Lorenzon
Leda Lorenzon
LMLisa Milan
Lisa Milan
RSRoberto Sghedoni
Roberto Sghedoni
GMGiovanni Mettivier
Giovanni Mettivier

1 April 2014 in International Journal of Radiation Oncology - Biology - Physics (IJROBP)
Verified
Journal Article
Closed access
MTMarco Trovo
Marco Trovo
EMEmilio Minatel
Emilio Minatel
EDElena Durofil
Elena Durofil
Jerry PoleselJerry Polesel
Jerry Polesel
MAMichele Avanzo
Michele Avanzo
MTMauro G. Trovo
Mauro G. Trovo

1 November 2024 in Cancers
Verified
Journal Article
Open access
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MAMichele Avanzo
Michele Avanzo
JSJoseph Stancanello
Joseph Stancanello
GPGiovanni Pirrone
Giovanni Pirrone
ADAnnalisa Drigo
Annalisa Drigo
ARAlessandra Retico
Alessandra Retico
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 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant 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. This 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, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as ‘black boxes’ that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising resources for frontier research and applications in medicine, in particular for oncological applications.