A review of AI and Data Science support for cancer management

Parimbelli, Enea; Wilk, Szymon; Cornet, Ronald; Śniatała, Pawel; Sniatala, Konrad; Savannah, Glaser,; Fraterman, Itske; Boekhout, Annelies H.; Ottaviano, Manuel; Peleg, Mor

doi:10.1016/j.artmed.2021.102111

Cited by 18 publications

(29 citation statements)

References 130 publications

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Section: Discussionmentioning

confidence: 99%

“…151 While traditional screening methods have advanced research in the field, AI and ML can accurately monitor the health statuses of cancer patients, which can support the management of patients. 152 In particular, early detection of cancer through the adoption of AI and ML stands as one of the innovations of the 21st century that can significantly help control the prevalence of cancer globally. 153 The mapping and visualizing analysis of the keywords related to AI and ML in cancer research provides in-depth WordCloud analysis of the top 100 keywords based on the frequency, thematic analysis, structure, and development status of the topics found on similarities into five distinguish clusters, systematically and intuitively reveal the subject structure and development status and make predictions subject in the fields.…”

Section: Implications For Cancer Research and Controlmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database

et al. 2022

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Introduction Cancer is a major public health problem and a global leading cause of death where the screening, diagnosis, prediction, survival estimation, and treatment of cancer and control measures are still a major challenge. The rise of Artificial Intelligence (AI) and Machine Learning (ML) techniques and their applications in various fields have brought immense value in providing insights into advancement in support of cancer control. Methods A systematic and thematic analysis was performed on the Scopus database to identify the top 100 cited articles in cancer research. Data were analyzed using RStudio and VOSviewer.Var1.6.6. Results The top 100 articles in AI and ML in cancer received a 33 920 citation score with a range of 108 to 5758 times. Doi Kunio from the USA was the most cited author with total number of citations (TNC = 663). Out of 43 contributed countries, 30% of the top 100 cited articles originated from the USA, and 10% originated from China. Among the 57 peer-reviewed journals, the “Expert Systems with Application” published 8% of the total articles. The results were presented in highlight technological advancement through AI and ML via the widespread use of Artificial Neural Network (ANNs), Deep Learning or machine learning techniques, Mammography-based Model, Convolutional Neural Networks (SC-CNN), and text mining techniques in the prediction, diagnosis, and prevention of various types of cancers towards cancer control. Conclusions This bibliometric study provides detailed overview of the most cited empirical evidence in AI and ML adoption in cancer research that could efficiently help in designing future research. The innovations guarantee greater speed by using AI and ML in the detection and control of cancer to improve patient experience.

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Section: Discussionmentioning

confidence: 99%

Section: Implications For Cancer Research and Controlmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database

et al. 2022

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“…Digital technology-based cancer care management has been implemented where necessary and is becoming a popular healthcare delivery format. 121 Three recommendations giving for future work to support cancer survivorship care and management: (1) nurses must understand the workflow of AI digital technologies that they used; (2) patients with cancer need to be meaningfully involved in all stage of development of AI DHTs; (3) exploring more potential applied settings in cancer survivorship care services.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

Pan

Zhang

et al. 2022

Asia-Pacific Journal of Oncology Nursing

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“…Two main sub-sections characterize a complete wearable medical device [ 81 ]: the hardware subsector, comprising sensor selection and characterization, noise removal from the collected data, and communication with a data processing subsystem. The other sub-sector, the software, comprises data processing and decisions based upon the collected data, in which AI techniques can be used for representing, modeling, and reasoning with medical evidence and knowledge, potentially demonstrating human-like capabilities for diagnosis, early detection, prevention and medical care guidance [ 82 ]. Therefore, in addition to remote and continuous motoring, prediction and recommendations with the data collected through wearable devices may potentially enhance SWS capabilities [ 81 ].…”

Section: Discussionmentioning

confidence: 99%

Smart Wearable Systems for the Remote Monitoring of Selected Vascular Disorders of the Lower Extremity: A Systematic Review

Souza

Escadas

Baxevani

et al. 2022

IJERPH

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This systematic review aims at providing an overview of the state of the art regarding smart wearable systems (SWS) applications to monitor the status of patients suffering from vascular disorders of the lower extremity. Peer-reviewed literature has been analyzed to identify employed data collection methods, system characteristics, and functionalities, and research challenges and limitations to be addressed. The Medline (PubMed) and SCOPUS databases were considered to search for publications describing SWS for remote or continuous monitoring of patients suffering from intermittent claudication, venous ulcers, and diabetic foot ulcers. Publications were first screened based on whether they describe an SWS applicable to the three selected vascular disorders of the lower extremity, including data processing and output to users. Information extracted from publications included targeted disease, clinical parameters to be measured and wearable devices used; system outputs to the user; system characteristics, including capabilities of remote or continuous monitoring or functionalities resulting from advanced data analyses, such as coaching, recommendations, or alerts; challenges and limitations reported; and research outputs. A total of 128 publications were considered in the full-text analysis, and 54 were finally included after eligibility criteria assessment by four independent reviewers. Our results were structured and discussed according to three main topics consisting of data collection, system functionalities, and limitations and challenges.

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A review of AI and Data Science support for cancer management

Cited by 18 publications

References 130 publications

Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database

Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database

Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

Smart Wearable Systems for the Remote Monitoring of Selected Vascular Disorders of the Lower Extremity: A Systematic Review

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