AI in Population Health: Exploring Sub-Fields and Applications
By Sumona Bose
February 1, 2024
Introduction
Artificial intelligence (AI) has influenced various aspects of healthcare, from genetics research to clinical care. However, its adoption in population health settings has been slower. In this article, we aim to shed light on different sub-fields of AI and their potential applications in population health, emphasizing the need for decision-makers to understand AI concepts. By exploring these sub-fields, we can harness the power of AI to make more informed decisions and improve public health outcomes. This article will explore the sub fields of AI in population and public health.
Understanding AI Concepts
AI methods offer the ability to analyze vast amounts of complex and diverse data, providing valuable insights and contributing to sense making. This capacity makes AI particularly appealing for health applications, including personalized medicine. However, the term “AI” encompasses various approaches and fields, leading to confusion. To bridge this knowledge gap, we will outline different AI sub-fields and their relevance to population health.
Exploring AI Sub-Fields
Machine Learning
Machine learning, a widely used sub-field of AI, has found success in clinical problem-solving. However, its application in population health and public health has been limited. Machine learning and traditional statistical approaches have the potential to leverage big data to understand and predict healthcare and population health outcomes. However, caution must be exercised to avoid biased data and unequal access to technology, which can perpetuate health inequities.
Figure 1: Population Health and Responsible AI
Natural Language Processing (NLP)
NLP focuses on understanding and processing human language. In population health, NLP can be used to analyze large volumes of text data, such as electronic health records and social media posts, to identify patterns and trends. This can aid in disease surveillance, early detection, and monitoring of public health concerns.
Computer Vision
Computer vision involves the interpretation of visual data, such as medical images and videos. In population health, computer vision can assist in the analysis of imaging data for disease diagnosis and monitoring. It can also be used for surveillance purposes, such as monitoring social distancing compliance during pandemics.
Predictive Analytics
Predictive analytics utilizes historical data to make predictions about future events. In population health, predictive analytics can help identify individuals at risk of certain diseases or adverse health outcomes. This information can guide targeted interventions and resource allocation to prevent or mitigate health issues.
Conclusion
The field of AI holds immense potential for improving population health outcomes. While AI has already made significant advancements in genetics research and clinical care, its application in population health settings has been slower. By understanding the various sub-fields of AI and their relevance to population health, decision-makers can harness the power of AI to make more informed decisions and address public health challenges. Exploring sub fields of AI in population health makes for a necessary discourse to positively intervene in population health.
💊 The jury is out on the pricing for Journavx®
Delve into our review of the recent ICER 2025 report on suzetrigine (Journavx®) to learn about the anticipated value relative to its clinical efficacy, safety profile and potential cost savings in tackling acute pain while addressing the opioid crisis.
Explore how suzetrigine paves the way for a safer, more effective approach to pain management and its implications on healthcare economics.
🚨 What happens when scientific research funding is threatened?
In his thought-provoking article, Atul Gawande highlights the dire implications of proposed federal funding cuts to elite institutions like Harvard. He argues that such actions could devastate not just innovation, but also patient care and public health across the nation.
Explore the complexities of research funding and the potential ripple effects on America’s scientific landscape. Don’t miss out on these critical insights!
🚀 Are we witnessing a new era in bladder cancer treatment?
The FDA’s recent approval of durvalumab as the first perioperative immunotherapy for muscle-invasive bladder cancer (MIBC) could revolutionize outcomes for patients facing this formidable diagnosis. With significant improvements in event-free survival and overall survival over standard chemotherapy, this groundbreaking treatment offers new hope 🎉.
Curious about how this could shape the future of cancer care? Dive into the full article to uncover the potential impacts on clinical practice and health economics.
#SyenzaNews #oncology #HealthEconomics
When you partner with Syenza, it’s like a Nuclear Fusion.
Our expertise are combined with yours, and we contribute clinical expertise and advanced degrees in
health policy, health economics, systems analysis, public finance, business, and project management.
You’ll also feel our high-impact global and local perspectives with cultural intelligence.
