The Impact of Artificial Intelligence on Medicinal Applications

Karan Chawla

Abstract: Explainability is a long - standing issue in artificial intelligence, because traditional AI methods were easily understood and reproducible. Their inability to handle the uncertainties of the actual world was a drawback, though. Applications grew more and more successful when probabilistic learning was introduced, but they also became more and more opaque. The introduction of traceability and transparency in statistical black - box machine learning techniques, especially deep learning (DL), is the focus of explainable AI. We contend that explainable AI is not sufficient. Causability is necessary to bring medicine to a level of understandability. Causability includes metrics for the quality of explanations, just as usability includes measurements for the quality of usage.

Keywords: explainability, artificial intelligence, probabilistic learning, transparency, causability

How to Cite?: Karan Chawla, "The Impact of Artificial Intelligence on Medicinal Applications", Volume 13 Issue 3, March 2024, International Journal of Science and Research (IJSR), Pages: 1040-1043, https://www.ijsr.net/getabstract.php?paperid=SR24315003002, DOI: https://dx.doi.org/10.21275/SR24315003002