article · International Medical Science Research Journal
Artificial intelligence is advancing personalised medicine by matching medical treatments to individual patient profiles. Machine learning models examine complex datasets, such as genetic details, electronic health records, and real-time monitoring streams, to uncover patterns that inform tailored care plans. In pharmacogenomics, this analysis predicts how genetic variations alter drug metabolism, efficacy, and toxicity, helping clinicians identify optimal medications and doses while cutting adverse drug reactions. Beyond direct patient care, computational tools support drug repurposing and development by identifying alternative therapeutic applications and forecasting adverse effects ahead of clinical trials. Predictive analytics can also track ongoing responses to adjust dosages dynamically, which is especially useful for conditions like cancer, hypertension, and diabetes. Wider adoption remains subject to resolving data privacy issues, building regulatory frameworks, and ensuring equal access.
Prescribing medicine often relies on trial and error, which risks severe side effects or ineffective treatment. Using artificial intelligence to process individual genetic and clinical records allows therapies and dosages to be fine-tuned to specific biological profiles. This lowers the chance of adverse drug events, speeds up treatment optimisation for chronic illnesses, and reduces the time required to develop or repurpose medicines.
Potential applications include clinical decision support systems for healthcare providers, real-time dosage management software for chronic diseases, and computational platforms for pharmaceutical drug repurposing. As the abstract describes these capabilities conceptually rather than presenting a tested product or trial data, the work appears to be at an early review or conceptual stage rather than near market deployment.
AI-generated from the published abstract. Always read the original work before citing.
Artificial intelligence (AI) is transforming personalized medicine by enhancing drug efficacy and reducing adverse effects, promising a new era of precision healthcare. This paper explores the role of AI in revolutionizing drug therapies by tailoring treatments to individual patient profiles, thereby optimizing therapeutic outcomes and minimizing risks. AI leverages vast amounts of medical data, including genetic information, electronic health records (EHRs), and real-time health monitoring data, to create comprehensive patient profiles. Machine learning algorithms analyze these profiles to identify patterns and correlations that might not be apparent to human practitioners. This enables the development of personalized treatment plans that consider a patient's unique genetic makeup, lifestyle, and existing health conditions. One of the critical applications of AI in personalized medicine is pharmacogenomics, which studies how genes affect a person’s response to drugs. AI can analyze genetic variations that influence drug metabolism, efficacy, and toxicity, allowing healthcare providers to predict which medications and dosages will be most effective for individual patients. This reduces the trial-and-error approach traditionally used in prescribing medications, thereby enhancing drug efficacy and reducing the incidence of adverse drug reactions (ADRs). AI also plays a significant role in drug repurposing and development. By analyzing existing drug data and patient outcomes, AI can identify new therapeutic uses for existing medications and predict potential side effects before clinical trials, accelerating the drug development process and reducing costs. Moreover, AI-driven predictive analytics can continuously monitor patient responses to treatment, adjusting drug dosages in real-time to maintain optimal therapeutic levels. This is particularly beneficial for managing chronic conditions such as diabetes, hypertension, and cancer, where maintaining the correct drug dosage is crucial for effective disease management. Despite its promise, the integration of AI in personalized medicine faces challenges, including data privacy concerns, the need for robust regulatory frameworks, and ensuring equitable access to AI-driven healthcare innovations. Addressing these challenges requires collaborative efforts from healthcare providers, researchers, policymakers, and technology developers. In conclusion, AI is at the forefront of personalized medicine, enhancing drug efficacy and reducing adverse effects by tailoring treatments to individual patient profiles. Continued advancements in AI technologies and supportive regulatory policies will be crucial in realizing the full potential of personalized medicine, ultimately leading to more effective and safer healthcare solutions. Keywords: AI, Drug Efficacy, Personalized Medicine, Enhancing, Reducing Adverse Effect.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.51594/imsrj.v4i8.1453
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
