Artificial Intelligence in Medical Applications

Chan, Yung-Kuan; Chen, Yung-Fu; Pham, Tuan D.; Chang, Wen-Shin; Hsieh, Ming-Yuan

doi:10.1155/2018/4827875

Cited by 42 publications

(25 citation statements)

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“…This appears to be an erroneous perception. Evidence suggests that the AI tools would rather improve the practice of the profession including high-quality diagnosis and minimal errors [ 2 , 4 , 5 , 27 ]. A study [ 27 ] also indicated that AI tools provide better diagnostic decisions (thus, it takes into consideration the results from every medical technical result, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence (AI) is a key component of these complex algorithms and is currently applied innovatively in healthcare because of its reported advantages and the potential to improve patient care [ 2 , 4 ]. AI has been widely used in many clinical circumstances to diagnose, treat and predict the outcomes [ 5 ]. Specifically, healthcare and research applications include prediction of disease prognosis and response to treatment, drug development, remote patient observations, medical data management, digital patient consultation and in some cases administrative hospital management [ 6 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives

2021

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Purpose Studies have documented the clinical potentials of artificial intelligence (AI) in medical imaging practice to improving patient care. This study aimed to qualitatively explore the perception of radiographers relating to the integration of AI in medical imaging practice in Africa. Methods The study employed a qualitative design using an open-ended online instrument administered between March and August 2020. Participants consisted of radiographers working within Africa during the time of the study. Data obtained were analysed using qualitative content analysis. Six themes of concerns were generated: expectant tool; career insecurity; cost of new technology, equipment preservation and data insecurity; service delivery quality; need for expanding AI awareness. Results A total of 475 valid responses were obtained. Participants demonstrated a positive outlook about AI in relation to clinical quality improvement, competent diagnosis, radiation dose reduction and improvement in research. They however expressed concerns relating to the implementation of this technology, including job security and loss of core professional radiographer skills and roles. In addition, concerns regarding AI equipment maintenance, lack of awareness about AI and education and training opportunities were evident. Conclusion Awareness of the importance of AI in medical imaging practice was acknowledged; however, concerns relating to job security, data protection must be given critical attention for successful implementation of these advanced technologies in medical imaging in Africa. Inclusion of AI modules in the training of future radiographers is highly recommended.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives

2021

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“…Additional investigation in high-risk populations incorporating new data from genomics and gene expression studies in developing new genetic risk scores may be helpful. Likewise, new statistical approaches based on data mining and artificial intelligence transform future prediction techniques ( Chan et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Genetic information improves the prediction of major adverse cardiovascular events in the GENEMACOR population

et al. 2021

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The inclusion of a genetic risk score (GRS) can modify the risk prediction of coronary artery disease (CAD), providing an advantage over the use of traditional models. The predictive value of the genetic information on the recurrence of major adverse cardiovascular events (MACE) remains controversial. A total of 33 genetic variants previously associated with CAD were genotyped in 1587 CAD patients from the GENEMACOR study. Of these, 18 variants presented an hazard ratio >1, so they were selected to construct a weighted GRS (wGRS). MACE discrimination and reclassiﬁcation were evaluated by C-Statistic, Net Reclassification Index and Integrated Discrimination Improvement methodologies. After the addition of wGRS to traditional predictors, the C-index increased from 0.566 to 0.572 (p=0.0003). Subsequently, adding wGRS to traditional plus clinical risk factors, this model slightly improved from 0.620 to 0.622 but with statistical significance (p=0.004). NRI showed that 17.9% of the cohort was better reclassified when the primary model was associated with wGRS. The Kaplan-Meier estimator showed that, at 15-year follow-up, the group with a higher number of risk alleles had a significantly higher MACE occurrence (p=0.011). In CAD patients, wGRS improved MACE risk prediction, discrimination and reclassification over the conventional factors, providing better cost-effective therapeutic strategies.

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“…Medical diagnosis support systems (MDSS) have also gained particular interest in recent years. These smart tools constitute an important aid for medical professionals to gain time, effort, and accuracy [14,15]. The diagnosis of most diseases can be aided by ML and DL algorithms, such as brain tumor detection using the CNN technique [16,17], diabetes mellitus prediction [18,19], patients with atherosclerosis disease classification [20][21][22], and detecting pneumonia on lung images [23,24].…”

Section: Introductionmentioning

confidence: 99%

Combining CNN and Grad-Cam for COVID-19 Disease Prediction and Visual Explanation

Moujahid¹,

Cherradi²,

Al-Sarem³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Artificial Intelligence in Medical Applications

Cited by 42 publications

References 0 publications

Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives

Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives

Genetic information improves the prediction of major adverse cardiovascular events in the GENEMACOR population

Combining CNN and Grad-Cam for COVID-19 Disease Prediction and Visual Explanation

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