First round of Abstract Submissions is open now..!
Don't miss the opportunity! Submit your abstract for the first round by November 28, 2025.
First round of Abstract Submissions is open now..!
Don't miss the opportunity! Submit your abstract for the first round by November 28, 2025.
Precision Medicine is a healthcare approach that tailors disease prevention and treatment to each individual. It considers a person's unique genetic makeup, environment, and lifestyle to help determine the most effective therapy. Instead of using the same treatment for everyone, it focuses on giving the right treatment to the right patient at the right time.
Digital health uses technology to improve healthcare delivery and health outcomes by integrating digital tools like mobile apps, wearables, and telehealth into all aspects of medicine, from prevention and diagnosis to treatment and monitoring. It aims to make healthcare more efficient, personalized, and accessible, encompassing areas such as mHealth, health information technology, telemedicine, and digital therapeutics.
Pharmacogenomics examines how a person’s genetic variations affect the way they respond to medications, making it an essential component of personalized healthcare. By understanding these genetic differences, clinicians can choose the most suitable drugs and determine optimal dosages, enhancing treatment success and minimizing harmful side effects. Personalized medicine takes this concept further by also considering environmental influences and lifestyle habits to develop treatment plans tailored to each individual.
Epigenetics can be used for disease prediction by identifying chemical modifications on DNA (like DNA methylation) that act as biomarkers for diseases like cancer, neurodegenerative disorders, and cardiovascular disease. These epigenetic markers can predict risk, indicate a disease's progression, and suggest how an individual might respond to a specific treatment, offering a more personalized approach to healthcare by showing how environmental and lifestyle factors affect gene expression.
AI and ML are transforming precision medicine by analyzing vast, complex datasets to create personalized diagnoses, treatments, and risk predictions. Applications include using ML for early disease detection and risk modelling, tailoring treatment plans based on a patient's unique profile, and accelerating drug discovery by identifying novel targets and predicting drug effectiveness. These technologies enable a shift from one-size-fits-all approaches to highly customized, data-driven healthcare.
Precision Oncology is a medical approach that uses genomic information to guide treatment, and it is the foundation for targeted cancer therapies, which are drugs designed to attack specific molecules driving cancer growth. Instead of broad chemotherapy, precision oncology matches an individual's tumor to a specific therapy, which aims to be more effective and cause fewer side effects. This involves testing for specific genetic mutations, proteins, or other biomarkers in a patient's tumor to identify which targeted therapy will be most effective.
Personalized Healthcare is a proactive approach that creates a customized health plan for each person to improve well-being and prevent disease. It uses predictive technologies to identify individual health risks and encourages patient involvement in managing their health for better outcomes.
Clinical trials are research studies that evaluate the safety and effectiveness of new drugs, treatments, and medical devices in humans. They are a crucial part of drug development, which is a multi-stage process that includes preclinical testing in labs and animals before moving to human trials. Clinical trials are divided into phases (Phase 1, 2, 3, and 4), each with a different purpose, from testing safety in small groups to confirming effectiveness and monitoring side effects in large populations after approval.
Data Security and Informatics infrastructure for precision medicine require specialized bioinformatic and big data platforms to integrate, store, and analyse diverse, sensitive patient data while ensuring privacy. Key components include secure cloud computing, big data technologies like Hadoop, and methods such as anonymization, de-identification, and blockchain to protect patient information and comply with regulations like HIPAA. Infrastructure also relies on robust analytical tools and common data models for reproducible workflows and knowledge discovery.
Multi Omics is a biological analysis approach that integrates data from multiple "omes" (biological datasets) to provide a more comprehensive understanding of biological processes and diseases. By combining data such as the genome (genes), transcriptome (RNA), epigenome (DNA modifications), and proteome (proteins), researchers can gain deeper insights into the complex relationships between genotype, environment, and. phenotype. This holistic view helps identify new drug targets, improve diagnostic capabilities, and understand disease mechanisms at a systems level.
Clinical pharmacology is the scientific study of drugs in humans, focusing on their effects, safe and effective use, and development. It bridges the gap between basic science and medicine by researching drug action (\(pharmacodynamics\)) and how the body processes drugs (\(pharmacokinetics\)). The discipline is essential for the development and regulation of new medicines, optimizing existing treatments, and understanding adverse reactions.
Disease monitoring is the process of detecting and tracking the progression of illnesses through a combination of surveillance systems, laboratory testing, and data analysis to identify trends and respond to outbreaks. This includes public health surveillance for epidemic-prone diseases, as well as clinical monitoring for chronic conditions to manage symptoms and treatment effectiveness. Key components include a tiered reporting structure, rapid response teams, and laboratory capacity to support diagnosis and confirmation.
Translational exploration is the process of rephrasing scientific discoveries from introductory exploration into clinical operations and healthcare interventions. It bridges the gap between laboratory findings and clinical practice, rephrasing knowledge from bench to bedside. Translational exploration encompasses colorful stages, including preclinical studies, clinical trials, perpetration of wisdom, and dispersion of substantiation-grounded practices. It aims to accelerate the restatement of exploration findings into palpable benefits for cases, communities, and public health. By fostering collaboration between experimenters, clinicians, assiduity mates, and policymakers, translational exploration facilitates the development of innovative curatives, diagnostics, and preventative strategies. It plays a vital part in advancing the perfect drug, substantiated healthcare, and population health, driving invention and perfecting healthcare issues.
Electronic Health Records (EHRs) play a key role in precision medicine by storing a patient’s complete medical and genetic information digitally. When combined with tools like artificial intelligence (AI) and natural language processing (NLP), EHRs help analyze large datasets to find patterns, predict diseases, and create personalized treatments. However, issues like data accuracy, system compatibility, and patient privacy still need attention.
Molecular profiling is the analysis of a tumor's genes, proteins, or other molecules to identify specific alterations that drive a disease. In precision medicine, these findings are used to create personalized treatment plans by matching patients with targeted therapies, diagnostic information, or prognostic guidance based on the unique molecular makeup of their disease.
A biomarker is any measurable characteristic that indicates a biological or pathogenic process or a response to a treatment. These can be simple measurements like blood pressure, lab results from blood or urine, or more complex molecular and cellular signals like genes and proteins. They are used in medicine to help with diagnosis, monitoring disease, and guiding treatment.
The microbiome is becoming a central part of precision medicine, enabling personalized nutrition plans by analyzing an individual's unique gut microbes to predict how they will respond to certain foods. This approach moves beyond a "one-size-fits-all" model by tailoring dietary recommendations to optimize an individual's health based on their specific microbiome composition, which influences metabolism, immunity, and disease risk. Integrating microbiome data with other -omics data, like genomics, provides a more complete picture for developing personalized and effective health strategies.
Future trends in precision medicine include AI integration for data analysis and drug discovery, the expansion of multi-omics to create comprehensive patient profiles, and the scaling of cell and gene therapies beyond rare diseases. Other key areas are the increased use of digital health technologies like wearables and telehealth, advances in genomic sequencing, and a focus on data privacy, equity, and ethics.