Abstract
Precision medicine, also known as personalized medicine, aims to tailor medical care to individual characteristics, genetic information, and lifestyle for more accurate disease risk predictions and personalized therapies. Traditional methods in precision medicine, such as clinical assessments, laboratory testing, and pathology testing, can be enhanced with AI models to improve accuracy, precision, and personalization. Genomic analysis, disease prediction, drug discovery, and imaging analysis are key components of precision medicine. Wearable devices support continuous monitoring for proactive intervention. ML algorithms like random forest and K-means clustering are used for prediction and early diagnosis of heart disease. A deep learning model for Alzheimer's disease diagnosis and a recommended application for maintaining health details are also suggested. Recursive feature elimination is used in disease prediction and treatment policy for diabetes.
TopBackground
Monitoring in Real Time with Wearable Technology: Continuous monitoring and proactive healthcare are made possible by wearable devices that contain sensors, which offer real-time health data. By analysing data collected by wearables, AI systems can provide valuable insights into patients' health and make recommendations based on their unique needs. In this article, we look at how wearable tech could revolutionize preventative healthcare and precision medicine. There are a number of issues that arise with the use of AI and ML in precision medicine, such as worries about algorithm bias, data privacy, and the interpretability of complicated models. The section delves into these difficulties and discusses the ethical issues related to AI in healthcare.
Health care is poised for a revolutionary shift as precision medicine and artificial intelligence (AI) come together. Through the use of precision medicine, phenotypes of individuals with uncommon therapeutic responses or special healthcare requirements can be identified. Artificial intelligence (AI) uses complex computing and inference to draw conclusions, allows the system to learn and reason, and augments physician decision-making with its own intelligence. The most pressing problems in precision medicine, according to recent literature, involve the integration of nongenomic and genomic factors with patient-specific data on symptoms, medical history, and lifestyle choices in order to provide more accurate personalised diagnoses and prognoses (Johnson, 2021).
Key Terms in this Chapter
Deep Learning: A subset of machine learning involving neural networks with many layers, enabling the analysis of complex patterns in large data sets.
Genomics: The study of the complete set of DNA (including all of its genes) in a person or other organism.
Clinical Decision Support Systems (CDSS): Computer-based systems that analyze data within electronic health records to provide prompts and reminders to assist healthcare providers in decision-making.
Artificial Intelligence (AI): The simulation of human intelligence in machines that are programmed to think and learn like humans.
Phenotyping: The process of predicting an organism's phenotype using genetic information, often used in precision medicine to determine disease risk and treatment strategies.
Machine Learning: A subset of AI that involves the use of algorithms and statistical models to enable computers to improve their performance on tasks through experience.
Precision Medicine: A medical approach that customizes healthcare, with medical decisions, treatments, practices, or products tailored to the individual patient.
Next-Generation Sequencing: A method used to determine the precise sequence of nucleotides in a piece of DNA, allowing for high-throughput genetic analysis.
Epigenomics: The study of changes in gene expression that do not involve changes to the underlying DNA sequence – a change in phenotype without a change in genotype.
Radiomics: The extraction of large amounts of quantitative features from medical images using data-characterization algorithms.