Development and Cost Analysis of a Lung Nodule Management Strategy Combining Artificial Intelligence and Lung-RADS for Baseline Lung Cancer Screening

Adams, Scott; Mondal, Prosanta; Penz, Erika; Tyan, Chung-Chun; Lim, Hyun Ju; Babyn, Paul

doi:10.1016/j.jacr.2020.11.014

Cited by 19 publications

(9 citation statements)

References 20 publications

(32 reference statements)

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“…In the absence of a stable internet connection, the AI algorithm can analyze images directly on a smartphone, which are then uploaded to a cloud server and assessed by a remote specialist when internet is available. AI applications to address health disparities in oncology in HICs was a less explored topic, with some articles discussing algorithms to selectively target disadvantaged populations [ 120 , 121 ]. For instance, given the high prevalence of oral cancer in South Asian populations [ 155 ], 1 study used ML to develop a quantitative cytology program to selectively improve oral cancer screening among South Asians living in British Columbia, Canada [ 120 ].…”

Section: Resultsmentioning

confidence: 99%

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

Istasy¹,

Lee²,

Iansavichene³

et al. 2022

J Med Internet Res

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Background The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. Objective We aimed to conduct a scoping review of the literature to address the question, “What are the current and potential impacts of AI technologies on health equity in oncology?” Methods Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. Results Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. Conclusions Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI’s ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.

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Section: Resultsmentioning

confidence: 99%

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

Istasy¹,

Lee²,

Iansavichene³

et al. 2022

J Med Internet Res

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“…CT combined with AI resulted in a negative incremental cost-effectiveness ratio (ICER) compared to CT only, showing lower costs and higher effectiveness ( 69 ). The lowest net cost savings using AI-informed management strategies are estimated at $72 per screened patient ( 70 ).…”

Section: Discussionmentioning

confidence: 99%

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

et al. 2022

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BackgroundArtificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC.MethodsScientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords.ResultsThe total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified.ConclusionAAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.

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“…Adams et al explored the integration of AI malignancy predictions into the management of lung nodules by a simulation study, in which they up-and downgraded Lung-RADS scores rated by radiologists according to the malignancy prediction of the algorithm. They also analyzed the economic effects and estimated a saving of USD 72 per screened patient [34]. More research has to be performed on the integration of AI into lung nodule management and the cost-effectiveness.…”

Section: Shortcomings In Research On Ai Assistance For Lung Nodule As...mentioning

confidence: 99%

The Effects of Artificial Intelligence Assistance on the Radiologists’ Assessment of Lung Nodules on CT Scans: A Systematic Review

Ewals

Borne

Jacobs

2023

JCM

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To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists, many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are currently being implemented in clinical practice, but the question is whether radiologists and patients really benefit from the use of these novel tools. This study aimed to review how AI assistance for lung nodule assessment on CT scans affects the performances of radiologists. We searched for studies that evaluated radiologists’ performances in the detection or malignancy prediction of lung nodules with and without AI assistance. Concerning detection, radiologists achieved with AI assistance a higher sensitivity and AUC, while the specificity was slightly lower. Concerning malignancy prediction, radiologists achieved with AI assistance generally a higher sensitivity, specificity and AUC. The radiologists’ workflows of using the AI assistance were often only described in limited detail in the papers. As recent studies showed improved performances of radiologists with AI assistance, AI assistance for lung nodule assessment holds great promise. To achieve added value of AI tools for lung nodule assessment in clinical practice, more research is required on the clinical validation of AI tools, impact on follow-up recommendations and ways of using AI tools.

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Development and Cost Analysis of a Lung Nodule Management Strategy Combining Artificial Intelligence and Lung-RADS for Baseline Lung Cancer Screening

Cited by 19 publications

References 20 publications

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

The Effects of Artificial Intelligence Assistance on the Radiologists’ Assessment of Lung Nodules on CT Scans: A Systematic Review

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