Silvia Seoni

30 papers receiving 837 citations

Hit Papers

Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023) 2023 · 97 citations
972022202620232024100200300400

Peers

Silvia Seoni
Comparison fields: 5 of 145
  • Health Informatics 155
  • Health Information Management 70
  • Artificial Intelligence 281
  • Radiology, Nuclear Medicine and Imaging 179
  • Cognitive Neuroscience 108
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Vajira Thambawita Norway
Inga Strümke Norway
Zhenxing Xu United States
Steven A. Hicks Norway
Erico Tjoa Singapore
Julián Acosta United States
Michalis Zervakis Greece
Hui Wen Loh Singapore
Safal Shetty United States
Melissa Berthelot United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Silvia Seoni

Since Specialization
Citations

This map shows the geographic impact of Silvia Seoni's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Silvia Seoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Silvia Seoni more than expected).

Fields of papers citing papers by Silvia Seoni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Silvia Seoni. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Silvia Seoni. The network helps show where Silvia Seoni may publish in the future.

Co-authors

The 25 scholars most cited alongside Silvia Seoni, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Silvia Seoni Line = papers co-authored together Silvia Seoni links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
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Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
Hit paper breakdown →
202397
14 20238
15 202252
16 20224
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18
Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)
Hit paper breakdown →
2022438
19 20215
20 20204

About Silvia Seoni

Silvia Seoni is a scholar working on Health Informatics, Biophysics, Applied Psychology, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence, having authored 33 papers that have together received 865 indexed citations. Recurring topics across this work include Ultrasound Imaging and Elastography (6 papers), AI in cancer detection (5 papers), Photoacoustic and Ultrasonic Imaging (5 papers), Cutaneous Melanoma Detection and Management (4 papers), ECG Monitoring and Analysis (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Machine Learning in Healthcare (3 papers) and Explainable Artificial Intelligence (XAI) (3 papers). The work is most often cited by research in Health Informatics (155 citations), Health Information Management (70 citations), Artificial Intelligence (281 citations), Radiology, Nuclear Medicine and Imaging (179 citations) and Cognitive Neuroscience (108 citations). Silvia Seoni has collaborated with scholars based in Italy, Australia and Singapore. Frequent co-authors include Filippo Molinari, U. Rajendra Acharya, Prabal Datta Barua, Hui Wen Loh, Chui Ping Ooi, Massimo Salvi, Kristen M. Meiburger, Jahmunah Vicnesh, Salvador García and Oliver Faust. Their work appears in journals such as Information Fusion, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Computer Methods and Programs in Biomedicine, Computers in Biology and Medicine and Ultrasonics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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