Introduction to artificial intelligence in medicine

Mintz, Yoav; Brodie, Ronit

doi:10.1080/13645706.2019.1575882

Cited by 503 publications

(288 citation statements)

References 37 publications

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“…The power of AI technologies is evident in innovations in processing huge amounts of data, deep-learning algorithms, and expansion of machine learning capacity to learn in less time than it takes a human being. 3 AI applications in health care include faster and often more accurate diagnosis through image recognition, pathology detection, and diagnostic assessment. 3…”

Section: Ai Powermentioning

confidence: 99%

“…For example, while an AI application is executing tasks, clinicians might be able to spend time communicating with patients, expressing compassion, and delivering care with competence and satisfaction. 2,3 Incorporating AI-based decision aids could also boost clinicians' confidence in care management decisions. 3…”

Section: Quality Care and Patient-clinician Relationshipsmentioning

confidence: 99%

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Representing Communication, Compassion, and Competence in the Era of AI

2019

AMA Journal of Ethics

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Section: Ai Powermentioning

confidence: 99%

Section: Quality Care and Patient-clinician Relationshipsmentioning

confidence: 99%

Representing Communication, Compassion, and Competence in the Era of AI

2019

AMA Journal of Ethics

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“…Artificial neural network is a mathematical model, which stimulates the biological structure of neural networks. It possesses the features of parallel knowledge or information processing and storage distribution, strong self-learning and organizing, and a better adaptive ability (28)(29)(30). Radial basis function (RBF) is one of ANN methods having a better classification ability and mapping capability, faster concourse speed, and a unique optimal approximation.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of an Artificial Neural Network for the Variation in Immunity to Salmonella Infection Among Sudanese and Chinese Populations and the Relationship Between HLA-DQB1 and Antibody: A Preliminary Study

Tamomh

Liu

2020

Jundishapur J Microbiol

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Background: Salmonella enterica serovar typhi infection is a worldwide bacterial disease still remains a public health problem and the resistance to the infection and clearance of the bacteria may differ between Sudanese and Chinese populations. Objectives: We aimed to evaluate the difference in immunity to Salmonella infection and the relationship of HLA-DQB1 and antibody in the two populations using an artificial neural network (ANN). Methods: An ANN was constructed by data collected from resolved Salmonella-infected patients. The variation in immunity to Salmonella in the two groups was compared using the Mann-Whitney U test. The relationship between human leukocyte antigen (HLA) gene and the antibody was assessed by Spearman correlation. Binary Logistic regression assessed whether variables were independent in the two groups. The quantitative variations in immunity to S. typhi in the two groups were assessed by ANN using SPSS software version 21.0. Results: A high variation in immunity to S. typhi between Sudanese and Chinese in affinity (P < 0.05), and no differences in other antibody parameters (P > 0.05). There was a high variation in HLA-DQB1*03 (P < 0.05), and no differences with other HLA-DQB1 alleles (P > 0.05). A clear correlation was observed between HLA-DQB1*05 and the antibody (P < 0.05). The standardized percentage of the effect of HLA-DQB1 gene on the variation of immunity in the two groups for HLA-DQB1*06 (100%) was much higher than the other HLA-DQB1 alleles. Conclusions: A high variation in the HLA-DQB1 gene between Sudanese and Chinese populations may be associated with the difference in immunity against S. typhi infection.

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“…It is a discipline on how to make computers simulate certain thinking processes and intelligent behaviors of human beings, such as learning, reasoning, thinking and planning, etc.. AI has been applied in almost all subjects of natural science and social science. The application of AI in the medical eld has also been reported [3]. It has made important progress in the screening, positioning and analysis of pulmonary nodules [4], thyroid nodules [5], intracranial aneurysms [6] and other diseases.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial intelligence image-assisted diagnosis system in chest CT examination of COVID-19

Zhang¹,

Wang²,

Ding³

et al. 2020

Preprint

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Purpose The purpose of this study is to evaluate the application efficiency of artificial intelligence (AI) image-assisted diagnosis system in chest CT examination of corona virus disease 2019 (COVID-19). Methods A total of 33 cases of COVID-19 patients who underwent chest CT in Hefei Binhu Hospital between January 2020 and March 2020 were retrospectively included. All patients were tested positive for novel coronavirus nucleic acid by fluorescent reverse transcription-polymerasechain reaction (RT-PCR). The pneumonia screening function of the AI image-assisted diagnosis system was employed for the 103 chest CT examinations of the 33 cases. The diagnosis of four senior radiologists were used as the standard for synchronous under blind state. The sensitivity, specificity, misdiagnosis rate, missed diagnosis rate and other evaluation indexes of the COVID-19 performed by the AI image-assisted diagnosis system were analyzed, and an dynamic evaluation on the CT reexamination was conducted. Results Out of the 103 chest CT examinations, there were 88 cases of true positive, 1 case of false positive, 12 cases of true negative and 2 cases of false negative. The sensitivity was 97.78% (88/90); the specificity was 92.31% (12/13); the positive predictive value was 98.88% (88/89); the negative predictive value was 85.71% (12/14); the accuracy was 97.09% (100/103); the Youden index was 90.09%; the positive likelihood ratio was 12.711 and the negative likelihood ratio was 0.024. There were 790 identified lesions in these CT examinations in total, of which 569 were true positive and 221 were false positive. There were also 64 missed diagnosis markers. The detection rate of all lesions was 89.89% and the rate of false positives was 27.97%. In the last CT scan, the lesion size were smaller and the percentage of lesions in total lung volume along with the mean density of lesions was lower than that of the first CT scan. Conclusion The AI image-assisted diagnosis system has certain clinical application value in the early diagnosis and follow-up evaluation of chest CT examination of COVID-19.

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Introduction to artificial intelligence in medicine

Cited by 503 publications

References 37 publications

Representing Communication, Compassion, and Competence in the Era of AI

Representing Communication, Compassion, and Competence in the Era of AI

Quantitative Assessment of an Artificial Neural Network for the Variation in Immunity to Salmonella Infection Among Sudanese and Chinese Populations and the Relationship Between HLA-DQB1 and Antibody: A Preliminary Study

Application of artificial intelligence image-assisted diagnosis system in chest CT examination of COVID-19

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