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      Artificial intelligence in clinical decision support systems for oncology

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          Abstract

          Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.

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          Artificial intelligence in healthcare: past, present and future

          Fei Jiang, Yong Jiang, Hui Zhi … (2017)
          Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.
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            Effect of clinical decision-support systems: a systematic review.

            Tiffani J Bright, Anthony Wong, Ravi Dhurjati … (2012)
            Despite increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and reducing costs, evidence to support widespread use is lacking. To evaluate the effect of CDSSs on clinical outcomes, health care processes, workload and efficiency, patient satisfaction, cost, and provider use and implementation. MEDLINE, CINAHL, PsycINFO, and Web of Science through January 2011. Investigators independently screened reports to identify randomized trials published in English of electronic CDSSs that were implemented in clinical settings; used by providers to aid decision making at the point of care; and reported clinical, health care process, workload, relationship-centered, economic, or provider use outcomes. Investigators extracted data about study design, participant characteristics, interventions, outcomes, and quality. 148 randomized, controlled trials were included. A total of 128 (86%) assessed health care process measures, 29 (20%) assessed clinical outcomes, and 22 (15%) measured costs. Both commercially and locally developed CDSSs improved health care process measures related to performing preventive services (n= 25; odds ratio [OR], 1.42 [95% CI, 1.27 to 1.58]), ordering clinical studies (n= 20; OR, 1.72 [CI, 1.47 to 2.00]), and prescribing therapies (n= 46; OR, 1.57 [CI, 1.35 to 1.82]). Few studies measured potential unintended consequences or adverse effects. Studies were heterogeneous in interventions, populations, settings, and outcomes. Publication bias and selective reporting cannot be excluded. Both commercially and locally developed CDSSs are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse. This review expands knowledge in the field by demonstrating the benefits of CDSSs outside of experienced academic centers. Agency for Healthcare Research and Quality.
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              Digital pathology and artificial intelligence

              Muhammad Khalid Khan Niazi, Anil V Parwani, Metin N. Gurcan (2019)
              In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Int J Med Sci
                Journal ID (iso-abbrev): Int J Med Sci
                Journal ID (publisher-id): ijms
                Title: International Journal of Medical Sciences
                Publisher: Ivyspring International Publisher (Sydney )
                ISSN (Electronic): 1449-1907
                Publication date Collection: 2023
                Publication date (Electronic): 1 January 2023
                Volume: 20
                Issue: 1
                Pages: 79-86
                Affiliations
                [1 ]Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
                [2 ]Department of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
                Author notes
                ✉ Corresponding authors: Xianglin Yuan, Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China. Address: Jie Fang road 1095, Wuhan, China. E-mail address: yuanxianglin@ 123456hust.edu.cn ; Yinan Sun, Department of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China. Address: Jie Fang road 1095, Wuhan, China. E-mail address: sunyinan@ 123456hust.edu.cn .

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                Publisher ID: ijmsv20p0079
                DOI: 10.7150/ijms.77205
                PMC ID: 9812798
                PubMed ID: 36619220
                SO-VID: ee529b15-0dbe-4d67-b960-769d15c4a4ce
                Copyright © © The author(s)
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                Date received : 17 July 2022
                Date accepted : 1 December 2022
                Categories
                Subject: Review

                ScienceOpen disciplines: Medicine
                Keywords: ai,cdss,wfo,tumor treatment,mdt
                Data availability:
                ScienceOpen disciplines: Medicine
                Keywords: ai, cdss, wfo, tumor treatment, mdt

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