Artificial intelligence versus clinicians

Rampton, Vanessa

doi:10.1136/bmj.m1326

Cited by 15 publications

(9 citation statements)

References 8 publications

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“…Artificial intelligence (AI), described as the science and engineering of making intelligent machines (1), is a broad term that implies the use of a computer to model intelligent behavior with minimal human intervention, generally at a speed and scale that exceed human capability (2)(3)(4)(5). With the achievement of computer science, AI is involved in clinical practice, including tracking data (6,7), diagnosis (8), and support of decision making (9,10).…”

Section: Introductionmentioning

confidence: 99%

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

Liu

Chen

et al. 2021

Front. Med.

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Objective: Clinical trials contribute to the development of clinical practice. However, little is known about the current status of trials on artificial intelligence (AI) conducted in emergency department and intensive care unit. The objective of the study was to provide a comprehensive analysis of registered trials in such field based on ClinicalTrials.gov.Methods: Registered trials on AI conducted in emergency department and intensive care unit were searched on ClinicalTrials.gov up to 12th January 2021. The characteristics were analyzed using SPSS21.0 software.Results: A total of 146 registered trials were identified, including 61 in emergency department and 85 in intensive care unit. They were registered from 2004 to 2021. Regarding locations, 58 were conducted in Europe, 58 in America, 9 in Asia, 4 in Australia, and 17 did not report locations. The enrollment of participants was from 0 to 18,000,000, with a median of 233. Universities were the primary sponsors, which accounted for 43.15%, followed by hospitals (35.62%), and industries/companies (9.59%). Regarding study designs, 85 trials were interventional trials, while 61 were observational trials. Of the 85 interventional trials, 15.29% were for diagnosis and 38.82% for treatment; of the 84 observational trials, 42 were prospective, 14 were retrospective, 2 were cross-sectional, 2 did not report clear information and 1 was unknown. Regarding the trials' results, 69 trials had been completed, while only 10 had available results on ClinicalTrials.gov.Conclusions: Our study suggest that more AI trials are needed in emergency department and intensive care unit and sponsors are encouraged to report the results.

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Section: Introductionmentioning

confidence: 99%

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

Liu

Chen

et al. 2021

Front. Med.

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“…[ 22 ] and related its editorial Ref. [ 23 ]). We propose that our information theory approach represents a simple way for resource‐challenged healthcare ecosystems to make informed decisions on reagent and testing platform investments.…”

Section: Discussionmentioning

confidence: 99%

Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology

et al. 2022

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Aims Resource‐strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings. Methods We used simple information theory calculations on a brain cancer simulation model and real‐world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H&E and Olig2 stained images obtained from digital slides. An auto‐adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH ‐mutant tumors. Results Information theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53 , and ATRX status have significant information for the diagnosis of IDH ‐mutant tumors. The predictive models may facilitate the reduction of false‐positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing. Conclusions We posit that this approach provides an improvement on the cIMPACT‐NOW workflow recommendations for IDH ‐mutant tumors and a framework for future resource and testing allocation.

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“…Finally, although ML can make accurate predictions, it is ultimately up to health care workers to make decisions based on their patient’s characteristics, clinical condition, and expectations [3,52]. This is particularly important in the setting of organ transplantation, where issues of ethics and justice are of the utmost importance and cannot be categorized as mere computational variables.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Machine learning in liver transplantation: a tool for some unsolved questions?

et al. 2021

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Summary Machine learning has recently been proposed as a useful tool in many fields of Medicine, with the aim of increasing diagnostic and prognostic accuracy. Models based on machine learning have been introduced in the setting of solid organ transplantation too, where prognosis depends on a complex, multidimensional and nonlinear relationship between variables pertaining to the donor, the recipient and the surgical procedure. In the setting of liver transplantation, machine learning models have been developed to predict pretransplant survival in patients with cirrhosis, to assess the best donor‐to‐recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a narrative review on the role of machine learning in the field of liver transplantation, highlighting strengths and pitfalls, and future perspectives.

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Artificial intelligence versus clinicians

Abstract: Evolving technologies may yet provide the medicine that patients expect

Cited by 15 publications

References 8 publications

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology

Machine learning in liver transplantation: a tool for some unsolved questions?

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