Explainable AI Models for Clinical Decision Support Systems
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P112Keywords:
Clinical Decision Support Systems (CDSS), Artificial Intelligence (AI), Explainable Artificial Intelligence (XAI), Machine Learning (ML)Abstract
The data-driven world has led to the intervention of artificial intelligence and clinical decision support systems technologies in the healthcare sector in the decision-making process. However supportive these advanced systems are, they suffer from an issue of “black-box” or unexplainably of their working process, which hinders their utilisation in the health sector. Explainable Artificial Intelligence can mitigate such challenges by providing automation models that are transparent to healthcare individuals about their working procedures, which promises the clinical trust in healthcare systems and aids them in efficient decision-making. The evaluation of the data and information through statistical analysis has outlined that explainable artificial intelligence is the epitome of clinical data accuracy. In addition to this, the evaluation of the data through graphical representation has also outlined that, specifically, gender-based specialisation, the male patients mostly face problems due to respiratory conditions, along with heart conditions, while most of the female patients have faced diabetes. Explainable artificial intelligence has the capability in enhancing the data accuracy of the patients and developing treatments according to it
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References
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