International Journal of Biomedical Engineering and Clinical Science

Special Issue

Artificial Intelligence/Machine Learning Applications and Clinical Decision Support Systems in Clinical/Genomic Data

Submission deadline: Oct. 15, 2024
Status: Open for Submission
Artificial intelligence (AI) applications, which imitate human intelligence through computers by modelling the functioning of the human brain, have been used for various purposes in many fields since 1969. In addition to providing practicality especially in medical interventions and other branches of science, AI applications also enable the collection, analysis and interpretation of large data, the amount of which is increasing day by day. For this reason, its use is becoming widespread and its importance is increasing day by day. Machine learning (ML), a subfield of artificial intelligence, includes a set of methods that aim to make predictions about new data when exposed to new data by performing data-driven learning. AI/ML methods, which have a wide range of applications in health, constitute the basic infrastructure of applications in the identification of genetic diseases, early diagnosis of cancer diseases and identification of patterns in medical imaging. The high success rates achieved with the developments in AI/ML methods provide an opportunity to develop clinical decision support systems that can produce reliable results in medical data.
In the light of this information, the first aim of this special issue is to enable researchers to demonstrate the clinical usability of the results obtained by applying AI/ML methods to clinical/genomic data. Another aim of the issue is to demonstrate the success of AI/ML models in clinical/genomic data and to show the applicability of AI/ML methods to clinical/genomic data in a healthy way by opening new horizons for researchers working in this field. For this reason, within the scope of this special issue, studies (original research articles, reviews and case report series) with AI/ML methods that aim to identify risk factors to be made with clinical data, provide biomarker discovery for early diagnosis and treatment, and produce results within the scope of personalised medicine and treatment will be gladly accepted.
The aim of this special issue is to scientifically support the developments in this field by integrating AI/ML methods and medical studies.Your contributions will play a crucial role in advancing knowledge in this field.

Potential topics include, but are not limited to:

  1. Artificial intelligence
  2. Machine learning
  3. Classification
  4. Decision support system
  5. Risk factor
  6. Explainable artificial intelligence
  7. Black box model
  8. Variable importance
  9. Local Interpretable model-agnostic explanations (LIME)
  10. Shapley additive explanations (SHAP)
  11. Genomic data
Lead Guest Editor
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