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Artificial Intelligence for Health

Artificial intelligence offers new opportunities to improve decision-making and optimize health services, provided that its implementation is guided by ethical principles, scientific evidence, and a people-centered approach.

Within the framework of the digital transformation of the health sector, artificial intelligence is emerging as a key component for data analysis, knowledge generation, and decision support in increasingly complex and interconnected systems. 

Its adoption is part of a broader approach that promotes interoperability, the strategic use of information, and capacity-building across the Region.

At the same time, its implementation raises important challenges. The use of artificial intelligence in public health must aim to maximize its benefits without compromising people’s rights.

This involves ensuring transparency, data protection, equity, and human oversight in automated processes, always with the goal of contributing to more integrated, timely, and trustworthy health systems that promote health and well-being for all people.

Useful links
Assessment and readiness

Tools for Public Health

Provides insights into the level of readiness for implementing artificial intelligence in health, identifying strengths and gaps in key areas such as governance, data, and technical capacities.

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Digital literacy in AI

Digital literacy in AI
 

Strengthens the capacities of health teams to understand, assess, and apply artificial intelligence, promoting its responsible use and supporting decision-making processes.
 

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Technical documentation on AI

Brings together resources and guidance on artificial intelligence in public health, including ethical principles, conceptual frameworks, and experiences to support its implementation.
 

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Digital transformation

PAHO advances this approach through the principles for the digital transformation of the health sector, promoting people-centered, secure and interoperable technologies that contribute to strengthening health systems across the Region. 

Building on this approach, PAHO supports countries in developing the capacities, tools and frameworks needed to implement artificial intelligence safely and sustainably.
 

Principles for the Use of Artificial Intelligence in Health

The use of artificial intelligence in public health should be guided by ethical and technical principles that maximize its benefits without compromising people’s rights.

Click on each card to learn about the principles that guide the responsible development and use of artificial intelligence in public health.

AI components and subfields that can benefit public health

Artificial intelligence is based on a range of components and subfields that make it possible to process data, recognize patterns, interpret information and generate predictions. These capabilities can be applied across multiple areas of public health.

These include technologies designed for data analysis, understanding human language, process automation and interaction with people. Their application can strengthen epidemiological surveillance, improve access to information and support clinical and management decision-making.


Below are some of these components. Scroll through the carousel to learn more about them.

Natural Language Processing (NLP)

Enables computers to read, interpret and extract meaning from human language in text or conversations.

Applications in public health

  • Analysis of behaviour on social media

  • Processing of data generated by patients and users

  • Identification of health trends and needs

Natural Language Understanding (NLU)

A subfield of NLP focused on understanding texts and human expressions through the analysis of grammar, syntax and meaning.

Applications in public health

  • Automated interpretation of clinical information

  • Analysis of medical records and health documentation

  • Identification of patterns in clinical texts

Natural Language Generation (NLG)

Enables the transformation of structured data into text or responses that people can easily understand.

Applications in public health

  • Automated report generation

  • Anonymization of electronic health records

  • Generation of content and clinical summaries

Speech Analysis

Enables the analysis of spoken language to recognize patterns, interpret meaning and detect characteristics of human speech.

Applications in public health

  • Analysis of behaviour and emotional states

  • Assessment of symptoms related to voice or speech

  • Remote monitoring and clinical decision-support tools

Machine Learning

Enables the identification of patterns and the generation of predictions from large volumes of data using algorithms trained on previous information.

Applications in public health

  • Prediction of diseases and risk factors

  • Analysis of relationships between genetics, environment and health

  • Models for epidemiological surveillance and decision-making

Deep Learning

A subfield of machine learning based on deep neural networks that can learn complex patterns from large volumes of data.

Applications in public health

  • Automated disease detection

  • Advanced analysis of clinical images

  • Prediction of health conditions using electronic health records

Digital Platforms for Decision-Making

Enable the analysis of complex information and support decision-making processes through predictive models and automated analysis.

Applications in public health

  • Support for clinical and management decision-making

  • Modelling of health scenarios

  • Integration of multiple variables and information systems

Cognitive Search

Integrates technologies such as artificial intelligence and natural language processing to analyse information from multiple sources.

Applications in public health

  • Search engines for health information

  • Analysis of scientific evidence and technical documentation

  • Rapid access to relevant information for decision-making

Robotics

An interdisciplinary field that develops intelligent machines and systems capable of assisting with or automating human tasks.

Applications in public health

  • Automated disinfection of spaces

  • Distribution of medicines and supplies

  • Monitoring and measurement of vital signs

Virtual Agents (Chatbots)

Applications capable of simulating conversations with people through written or spoken language.

Applications in public health

  • Promotion of healthy behaviours

  • Mental health and remote support

  • Tobacco control and patient follow-up

Computer Vision

Enables the interpretation of images and videos through models capable of recognizing objects, patterns and visual features.

Applications in public health

  • Analysis of medical images

  • Support for clinical diagnosis

  • Predictive modelling for respiratory diseases and other conditions

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