Artificial intelligence in clinical decision support systems for oncology

Wang, Lu; Chen, Xinyi; Zhang, Lu; Li, Long; Huang, YongBiao; Sun, Yujun; Yuan, Xianglin

doi:10.7150/ijms.77205

Cited by 29 publications

(11 citation statements)

References 72 publications

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“…They provide real-time insights, aiding healthcare providers in selecting the most effective treatments, predicting potential side effects, and identifying opportunities for intervention. AI-driven decision support enhances the precision and individualization of cancer care, ultimately improving patient outcomes and quality of life [ 9 ].…”

Section: Reviewmentioning

confidence: 99%

Revolutionizing Oncology: A Comprehensive Review of Digital Health Applications

Borkar,

Chakole,

Prasad

et al. 2024

Cureus

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Digital health is poised to revolutionize the field of oncology, offering innovative solutions that enhance diagnostics, treatment, and patient care. This comprehensive review delves into the multifaceted landscape of digital health in oncology, encompassing its definition, significance, applications, benefits, challenges, ethical considerations, and future trends. Key findings highlight the potential for early detection, personalized treatment, enhanced care coordination, patient empowerment, accelerated research, and cost efficiency. Ethical concerns surrounding privacy, equitable access, and responsible data use are discussed. Looking ahead, the future of digital health in oncology is bright, driven by advancements in artificial intelligence, virtual and augmented reality, predictive analytics, global collaboration, and evolving regulations. This review underscores the need for collaboration among stakeholders and a patient-centered approach to harness the transformative power of digital health, promising a future where the burden of cancer is lessened through innovation and compassionate care.

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Section: Reviewmentioning

confidence: 99%

Revolutionizing Oncology: A Comprehensive Review of Digital Health Applications

Borkar,

Chakole,

Prasad

et al. 2024

Cureus

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“…Clinical decision support: AI can help physicians and other healthcare professionals make better decisions by providing real-time information and alerts based on patient data ( 39 – 42 ). The progress of AI application is particularly pronounced in intensive care ( 31 ), surgery ( 43 ), oncology ( 44 ) clinical decision support in infectious diseases ( 45 ) but also in other areas of medicine.…”

Section: Artificial Intelligence In Medicine and Healthcarementioning

confidence: 99%

Artificial Intelligence in Medicine and Dentistry

Vodanović¹,

Subašić²,

Milošević³

et al. 2023

Acta Stomatol Croat

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Introduction Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. Objective Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. Conclusion The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

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“…In medicine, handcrafted radiomic models use data analytics to extract many features from medical images and is made up of several steps: (1) segmentation of the target lesion with manual segmentation by radiologists or with automatic and semi-automatic tools, (2) feature extraction to obtain multiple quantitative metrics and parameters from medical images, (3) feature selection with the aim of reducing the number of extracted features by avoiding correlated or redundant metrics, (4) analysis/classification by creating a predictive model using machine and deep learning approaches and (5) the validation of the results [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Statistical features define the individual voxel values distribution, the associations between neighboring voxels allowing for extraction from medical image features linked to lesion heterogeneity and the quantification of successive voxels with equal intensities along certain directions. Higher order statistical metrics are acquired through the application of filters or mathematical transformations to the images [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

Fusco,

Granata,

Simonetti

et al. 2024

Current Oncology

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The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019–2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.

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Artificial intelligence in clinical decision support systems for oncology

Cited by 29 publications

References 72 publications

Revolutionizing Oncology: A Comprehensive Review of Digital Health Applications

Revolutionizing Oncology: A Comprehensive Review of Digital Health Applications

Artificial Intelligence in Medicine and Dentistry

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

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