AI-Powered Diagnostics and Personalized Treatment: Enhancing Patient Outcomes in Modern Healthcare
DOI:
https://doi.org/10.52783/jns.v14.2241Keywords:
AI-Powered Diagnostics, Personalized Treatment, Machine Learning, Deep Learning, Medical Imaging, Predictive Analytics, Patient Outcomes, Healthcare Innovation, Ethical AI, Data PrivacyAbstract
This paper examines how the use of artificial intelligence (AI) can further the cause of medicine in healthcare, analyzing the functionality of diagnosis and treatment through AI. The study focuses on how AI technologies can dramatically transform the conventional models of treatment by providing the precise identification of diseases and delivering customized treatment approaches depending on a patient. Big data analytics, the use of sophisticated data analysis in care delivery, use of artificial intelligence incorporated in machine learning and deep learning is used in identification and predicting of health conditions based on analysis of patient data in many aspects including micrographic imaging, genetic and other records. The framing of the methodology involves the merging of the existing diagnostic systems with AI models to comprise image analytical mechanisms for assessment of medical images, natural language processing for clinical text data analysis, and processing of predictive analysis of diseases progression. The successes of these AI systems are even evaluated in terms of accuracy, sensitivity, specificity and the F1-score and in all these areas they outcompete the conventional diagnostic techniques. Algorithms adopted in the study also suggest that AI-based diagnostics improve the efficiency of diagnostics besides improving their effectiveness in terms of time and costs. Similarly, this work supports the applicability of individualized treatment plans tailored from computer based algorithms, which use patient data to identify the best course of action. Based on the studies presented, combining the approach of AI solutions in medicine has demonstrated positive results, and the technology’s application remains applicable to various medical fields. It is for these reasons that this study will conclude by highlighting various ethical concerns and three major queiesionss affecting the use of AI in the healthcare industry including privacy, biasness in algorithm, and regulatory measures. It sees a future where AI is a pivotal determinant of healthcare improvement; where its realisation can bring about efficient, affordable and personalised solutions for patients leading to overall betterment of patient care.
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