Paper ID: 2403.09762
Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts
Prashant Kumar Nag, Amit Bhagat, R. Vishnu Priya, Deepak kumar Khare
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare, with a particular focus on the incorporation of Natural Language Processing and deep learning technologies. We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes based on textual information derived from clinical narratives, patient feedback on medications, and online health discussions. The review demonstrates noteworthy progress in the precision of algorithms used for sentiment classification, the prognostic capabilities of AI models for neurodegenerative diseases, and the creation of AI-powered systems that offer support in clinical decision-making. Remarkably, the utilization of AI applications has exhibited an enhancement in personalized therapy plans by integrating patient sentiment and contributing to the early identification of mental health disorders. There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures. Nevertheless, the potential of AI to revolutionize healthcare practices is unmistakable, offering a future where healthcare is not only more knowledgeable and efficient but also more empathetic and centered around the needs of patients. This investigation underscores the transformative influence of AI on healthcare, delivering a comprehensive comprehension of its role in examining emotional content in healthcare texts and highlighting the trajectory towards a more compassionate approach to patient care. The findings advocate for a harmonious synergy between AI's analytical capabilities and the human aspects of healthcare.
Submitted: Mar 14, 2024