History of artificial intelligence in medicine

Kaul, Vivek; Enslin, Sarah; Gross, Seth A.

doi:10.1016/j.gie.2020.06.040

Cited by 542 publications

(322 citation statements)

References 46 publications

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“…The massive advent of sophisticated biotechnologies has been accompanied by a parallel evolution of instruments in which these technologies were computationally implemented. These analytical methods are recently exploiting the beginning of the Artificial Intelligence framework that is revolutionizing the field of biomedicine with its diverse applicative approaches (Ramesh et al, 2004;Buch et al, 2018;Balkanyi and Cornet, 2019;Holzinger et al, 2019;Mintz and Brodie, 2019;Kaul et al, 2020). In the area of machine learning (Rajkomar et al, 2019;Sidey-Gibbons and Sidey-Gibbons, 2019;Alsuliman et al, 2020), a novel kind of algorithm-based methodologies have recently gained a profound interest.…”

Section: Generative Adversarial Network To Evaluate the Interactionsmentioning

confidence: 99%

Oncoimmunology Meets Organs-on-Chip

Mattei

Andreone

Mencattini

et al. 2021

Front. Mol. Biosci.

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Oncoimmunology represents a biomedical research discipline coined to study the roles of immune system in cancer progression with the aim of discovering novel strategies to arm it against the malignancy. Infiltration of immune cells within the tumor microenvironment is an early event that results in the establishment of a dynamic cross-talk. Here, immune cells sense antigenic cues to mount a specific anti-tumor response while cancer cells emanate inhibitory signals to dampen it. Animals models have led to giant steps in this research context, and several tools to investigate the effect of immune infiltration in the tumor microenvironment are currently available. However, the use of animals represents a challenge due to ethical issues and long duration of experiments. Organs-on-chip are innovative tools not only to study how cells derived from different organs interact with each other, but also to investigate on the crosstalk between immune cells and different types of cancer cells. In this review, we describe the state-of-the-art of microfluidics and the impact of OOC in the field of oncoimmunology underlining the importance of this system in the advancements on the complexity of tumor microenvironment.

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Section: Generative Adversarial Network To Evaluate the Interactionsmentioning

confidence: 99%

Oncoimmunology Meets Organs-on-Chip

Mattei

Andreone

Mencattini

et al. 2021

Front. Mol. Biosci.

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“…In the case of the applications, the Arterys model based in deep learning (DL) and approved by the United States Food and Drug Administration (FDA) in 2017 is an example of the increasing research in the field of healthcare. This figure was created and adapted using references [68,88,89].…”

Section: Machine Learning and Deep Learningmentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

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Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

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“…AI can be defined as a machine demonstrating human intelligence in terms of cognitive functioning, learning, and problem solving [18]. These machines, which were first comprised of "if, then rules, " have now become an aggregation of interconnected intricate algorithms that function like a human brain [19]. The way these algorithms are designed can be broken down into subsets within AI, the most common being machine learning (ML) (Figure 1) [20].…”

Section: What Is Artificial Intelligence (Ai)?mentioning

confidence: 99%