From genomics to wellness: Exploring how generative AI's is applied in preventive healthcare

The phrase “Prevention is better than cure” emphasises the need for a shift in focus towards preventive care. As the name suggests, preventive medicine strives to preserve health, thereby reducing the necessity for treatment. It encompasses early intervention and long-term decision-making, with an emphasis on preventable lifestyle conditions. Need of the hour is a transition from a disease centered model to one focused on health, where success is evaluated by positive health outcomes rather than the quantity of medical services rendered.

The evolution of preventive healthcare

Technology plays a key role in this transition. Health information and technology are used to develop and refine population health strategies for better preventive care. Data and analytics tools help understand patient populations needing the most interventions, customizing strategies to individual needs. This shift is about redefining health, establishing a proactive, personalized system that prioritizes overall well-being. It’s a move towards a healthier future, emphasizing prevention over cure.

Big tech companies like AWS, Azure, GCP, and NVIDIA are leading the integration of gen AI into preventive healthcare. Our partners offer cloud platforms, ML tools to support genomic data processing as well as predictive healthcare models, revolutionizing preventive care through early disease detection and personalized treatment plans.

Unravelling the genetic basis of health and disease

Gen AI in healthcare revolutionizes disease risk assessment, personalized health management, and preventive care. By using genomic data and AI models, providers offer precise, individualized care, improving outcomes and reducing chronic disease burdens. Ethical frameworks and data privacy are crucial to protect patient rights and ensure equitable access.

Next-Generation Sequencing (NGS) has revolutionized genomics by identifying genetic mutations linked to diseases, crucial in projects like the Cancer Genome Atlas. Personal genomics services provide insights into ancestry, traits, and health risks. Genome-wide association studies identify  Single-nucleotide polymorphism (SNPs) linked to conditions like diabetes, heart disease, and Alzheimer’s, empowering proactive lifestyle changes or preventive treatments.

Polygenic Risk Scores (PRS) assess disease predisposition based on genetic makeup, aiding in predicting complex diseases. PRS helps gen AI models transform disease prediction, enabling tailored treatments and improved patient outcomes.

The Power of gen AI in analysing genomic data

AI and ML are transforming genomics by analysing large-scale data. Gen AI algorithms interpret complex genetic patterns, advancing predictive healthcare analytics. They generate synthetic genomic data when real data is limited. An example is the 100,000 Genomes Project in the UK that uses AI to identify rare genetic disorders, enabling timely interventions and improving outcomes, highlighting AI’s role in predictive and preventive healthcare.

Personalized health management in the digital age

In the digital age, personalized health recommendations are driven by genomic data and AI insights. AI algorithms provide tailored recommendations for diet, exercise, and medication, optimizing health outcomes based on genetic makeup. For example, Fitbit tracks health metrics and provides insights to help users achieve fitness goals. Engaging patients in proactive health management is crucial for preventing chronic diseases. Digital platforms and mobile apps motivate individuals with features like goal setting, progress tracking, reminders, and social support networks, fostering a sense of ownership and accountability in their health plans.

Security and ethical considerations in navigating the intersection of genomics and AI

Ensuring data security and patient autonomy is crucial for handling sensitive genomic data. Informed consent in genomic medicine empowers patients to make informed decisions. Addressing bias and ensuring equity in genomic and AI-driven healthcare requires diverse, representative data for fair health outcomes.

Gen AI in healthcare faces technological and regulatory challenges, including data integration and AI model accuracy. A responsible framework, ethical guidelines, interdisciplinary collaboration, and education for healthcare professionals are essential for successful gen AI integration.

Considering the limitations of present gen AI models in Preventive Healthcare, these models can be employed for predicting preventive measures and lifestyle changes. They play an important role in suggesting further medical intervention in case of any conerning observations in health parameters. However, it is imperative to note that these predictions cannot replace any medical diagnosis and advisories by medical fraternity.  

Our data-driven approach and the role of gen AI

At Cognizant, we integrate data and technology to enhance healthcare. Our robust data architectures ensure integrity, security, and accessibility. Advanced analytics transform raw data into actionable insights, revealing disease patterns and treatment outcomes. General AI analyzes complex data, predicts health risks, and suggests preventive measures. Generative AI personalizes healthcare by providing tailored advice and treatment plans, empowering individuals to take control of their health.

Conclusion

In conclusion, GenAI in healthcare transforms disease risk assessment, personalized health management, and preventive care. Leveraging genomic data and AI models, providers deliver precise, individualized care, improving outcomes and reducing chronic disease burdens. Ethical frameworks and data privacy are essential to protect patient rights and ensure equitable access.

Authors:

Sukriti Goyal, Ph.D., AIA, Cognizant  LinkedIn
Satish Gupta, Ph.D., Director, AIA, Life Science, Cognizant LinkedIn
Sean Heshmat, AIA Practice Lead, Cognizant LinkedIn