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      Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

      Author(s): , , , , , , ,
      Publication date Created:
      Publication date (Electronic):
      Journal: Cancers
      Publisher: MDPI AG

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          Abstract

          Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.

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          Radiomics: Images Are More than Pictures, They Are Data

          Robert Gillies, Paul E Kinahan, Hedvig Hricak (2015)
          This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
          Article 0 comments Cited 1968 times – based on 0 reviews     Review now
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            Machine learning: Trends, perspectives, and prospects.

            M. I. Jordan, T. M. Mitchell (2015)
            Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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              Machine Learning in Medicine.

              Rahul Deo (2015)
              Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
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                Author and article information

                Journal
                Journal ID (publisher-id): CANCCT
                Title: Cancers
                Abbreviated Title: Cancers
                Publisher: MDPI AG
                ISSN (Electronic): 2072-6694
                Publication date Created: October 2021
                Publication date (Electronic): October 07 2021
                Volume: 13
                Issue: 19
                Page: 5010
                Article
                DOI: 10.3390/cancers13195010
                PubMed ID: 34638495
                SO-VID: e60a5a2c-6b40-4b37-8875-2e82b47fa259
                Copyright © © 2021
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                https://creativecommons.org/licenses/by/4.0/

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