Greetings, fellow Nawala! May you always be in good health.
This is the IAES Nawala from the Institute of Advanced Engineering and Science. Today we will share news about the role of AI in improving healthcare service. The use of artificial intelligence (AI) in healthcare includes reducing the risk of delays in primary, secondary, and tertiary healthcare. Apio et al. (2023) reviewed 22 articles that included retrospective, prospective, and case-control studies. It was found that AI has the potential to improve patient satisfaction by reducing waiting times and supporting the healthcare system. However, further research is needed to validate the existing evidence and understand how AI can improve patient outcomes. Nonetheless, the use of AI in healthcare shows promise in delivering better services.
A systematic review of artificial intelligence-based methods in healthcare
Anthony Lirase Apio, Jonathan Kissi, Emmanuel Kusi Achampong
Artificial intelligence (AI) in healthcare has enormous potential for transforming healthcare. AI is the ability of machines to learn and exhibit close to human levels of cognition in various specific ways. Leveraging AI software to support activities will improve patient satisfaction which is inextricably tied to the length of time patients spend in waiting queues. Literature searches were conducted in PubMed, Research Gate, BMC Health Services Research, JMIR Publications and Cochrane Central to find related documentation that was published between January 2011 and April 2021. The studies featured and reported on AI technologies that had been used in primary, secondary, or tertiary healthcare situations directed towards reducing waiting times. A total of 22 articles were primarily used, including 8 retrospective studies, 4 prospective studies and 3 case-control studies. AI technologies have enormous potential in the creation of a future with more reliable healthcare systems. It is however clear that more studies in the field are required to validate the existing evidence of its potential. AI in healthcare is crucial to reducing patients’ time at healthcare facilities. The use of AI can also help improve patient outcomes and more research should be geared toward that.
Danuaji et al. (2023) conducted a study at RSUD dr. Moewardi in Surakarta, Indonesia. They developed an AI framework that can assist physician assistants with initial screening in assessing the risk of cerebrovascular disease. The AI can accurately measure carotid intima-media thickness from ultrasound images. The results showed that the screening results of this AI are valid and reliable in assessing the risk of cerebrovascular disease associated with carotid plaque.
Rivan Danuaji, Subandi Subandi, Stefanus Erdana Putra, Muhammad Hafizhan
Carotid plaque is a biomarker of generalized atherosclerosis, and may predict ischemic stroke. Carotid intima-media thickness (C-IMT) measurement with ultrasonography imaging could capture the condition of carotid plaque. However, manual measurement of C-IMT is observer- dependent, resulting in observer bias and low reproducibility. In this study, we develop artificial intelligence (AI) framework that could automatically measure the C-IMT, and compared it with C-IMT measured by board of expert. This is a retrospective study done in Dr. Moewardi General Hospital, Surakarta, Indonesia. Carotid B-mode ultrasonography images were measured by panel of expert and by AI. After annotation process on Neurabot platform, AI could detect region of interest (ROI), and would do segmentation on the area to measure C-IMT autonomously. Dependent T-test was used to evaluate validity, and Cronbach’s alpha was used to find the reliability of C-IMT measured by panel of expert and AI. There was strong correlation (r=0.874; p=0.014) on dependent t-test for C-IMT measured by AI with C-IMT measured by board of expert. The internal consistency reliability coefficients (Cronbach’s alpha) were 0.938 and 0.909, for pretest and posttest, respectively. We also analyzed the test-retest reliability by comparing pretest and posttest score with dependent t-test, and we observed strong correlation with r=0.871 (p=0.000). AI developed on Neurabot platform are valid and reliable to measure C-IMT.
In another study, Sohaib and Adewunmi (2023) developed an AI model to detect lung cancer at an early stage with high accuracy using ANN-based deep learning. The model achieved 94% accuracy and a minimum loss rate of 0.1%. The developed model can identify several risk factors for lung cancer, such as squamous cell carcinoma, adenocarcinoma, and large cell carcinoma. The findings show that artificial intelligence (AI) approaches can effectively identify and predict lung cancer risk factors.
Artificial intelligence based prediction on lung cancer risk factors using deep learning
Muhammad Sohaib, Mary Adewunmi
In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records, we reviewed a number of current research studies on lung cancer and its various stages. We identified that lung cancer is one of the significant research issues in predicting the early stages of cancer disease. This research aimed to develop a model that can detect lung cancer with a remarkably high level of accuracy using the deep learning approach (convolution neural network). This method considers and resolves significant gaps in previous studies. We compare the accuracy levels and loss values of our model with VGG16, InceptionV3, and Resnet50. We found that our model achieved an accuracy of 94% and a minimum loss of 0.1%. Hence physicians can use our convolution neural network models for predicting lung cancer risk factors in the real world. Moreover, this investigation reveals that squamous cell carcinoma, normal, adenocarcinoma, and large cell carcinoma are the most significant risk factors. In addition, the remaining attributes are also crucial for achieving the best performance.
The above articles are just a small part of the research on the role of AI in healthcare. To get more information, readers can visit the page and read articles for FREE through the following links: https://ijphs.iaescore.com/ and https://ijict.iaescore.com/.
By: I. Busthomi