AI PERSONALIZED DRUG DOSING FOR RESPIRATORY MEDICATIONS

ABSTRACT Modern medicine is getting transformed by AI especially through respiratory care. The trend is to base drug dosing to individual patient profiles as we enter the era of personalized medicine. The application of AI, such as ML and deep learning technologies provides ideas on how improvements can be made to drug therapy, based on various factors, such as genetic variability, environmental factors, comorbidity, and physiological reactions. Proper doses of some respiratory problems, i.e., asthma, chronic obstructive airway disease (COPD) and bronchitis are also needed to consider their effectiveness and safety well. A common problem associated with the conventional dosing methods is that they do not take into consideration the inter-individual variability aspect, which consequently results in sub optimal therapeutic results. An analysis of AI and ML takes place in this review on the topic of personalized dosing in respiratory medicine. It identifies areas of respiratory medications that need personalized prescription and discusses the pharmacogenomic factors, ventures into the scope of different ML models applied in prediction and includes the consideration of real-life data integration. Moreover, it addresses the existing limitations and ethical issues as well as defining research gaps. This article offers a concise edifice of evidence-based knowledge on the ways the current AI-based strategies are transforming personalized dosing and patient outcomes in respiratory care by combining evidence-based information on more than 60 scholarly publications and sources. The review further emphasizes the importance of clinical validation, regulatory guidance, and integration of AI systems into existing healthcare frameworks. By highlighting the collaborative role of healthcare professionals, data scientists, and policymakers, it also points toward the future landscape where AI-driven personalized dosing may redefine therapeutic decision-making. Ultimately, the review underscores how precision dosing can reduce adverse effects, optimize therapeutic outcomes, and improve quality of life in patients with chronic respiratory diseases. KEYWORDS Artificial Intelligence in Pharmacotherapy, Personalized Drug Dosing, Respiratory Diseases Treatment, Machine Learning in Medicine, Pharmacogenomics, Precision Medicine for Asthma and COPD.