Harnessing AI and Machine Learning for Accurate Diagnosis of Disorders of Consciousness

Sample Disciplinary Discourse Analysis There is no debate that can dismiss the influence technology holds in a person’s daily life. Technology has humanity in a rather pleasant chokehold even while it allows for revolutionary progression in society. From telecommunications capabilities that help connect workers from different countries, to facilitating leisurely pursuits, like enjoying a TV series on a smartphone, technology is clearly the mainstay of many of our activities. It is evident that technology has lifted weights off many shoulders. But how does technology affect the medical field? Technology impacts this industry primarily through the use of artificial intelligence (AI). AI assisted with making medical procedures more proficient, facilitating quicker creation of medicines, enabling more accurate observation of patients’ health, and much more. But what about AI’s predictive capabilities? Many experts are now starting to venture into this question with current technology, more specifically, through machine learning. “Machine learning (ML) is a discipline of artificial intelligence (AI) that provides machines with the ability to automatically learn from data and past experiences while identifying patterns to make predictions with minimal human intervention” (Kanade, 2022). This essay will analyze the writing conventions used in two articles from the Medical Informatics journal titled BMC medical informatics and decision making, vol.23 (1) (2023). These include “Machine learning and network analysis for diagnosis and prediction in disorders of consciousness” (Narayanan et al.) and “MRI-based brain tumor detection using convolutional deep