AI aids in enhancing health outcomes by swiftly and precisely detecting and diagnosing diseases. In this chart we see the relations between CVD, cancer, diabetes or CRD through the mortality between exact ages 30 and 70 (%).

This perspective offers a real-time view of the worldwide news on health-related topics, providing us with a media perspective on the progress at the different fronts of SDG 3.

 

Developed in collaboration with 

A SDG Live Report is what it is all about! Here, you can have a real-time perspective of the state of the world in regard to AI and sustainability, tracking some of the main issues to address in the near future.

 

The impact of AI on the progress of Sustainable Development Goal 3 (SDG 3), which aims to ensure healthy lives and promote well-being for all at all ages, has been profound and multifaceted. Between 2015 and 2023, there have been 2,548,307 scientific publications exploring the impact of AI on health and well-being, highlighting AI’s role in revolutionizing healthcare through advancements in medical imaging, diagnostics, personalized medicine, and predictive analytics. These studies underscore AI’s potential to enhance disease detection, improve treatment outcomes, and optimize healthcare delivery systems. Media exposure, with 53,626 news articles, reflects a growing public interest and awareness of AI’s transformative effects on health. Additionally, the development of 2,056 AI policies targeting SDG 3 demonstrates a significant commitment from policymakers to harness AI for public health improvement, focusing on ethical considerations, data privacy, and equitable access to AI-driven healthcare solutions.

Looking ahead, the next 5 to 10 years are poised to witness significant advancements in AI applications for health, driven by the ongoing growth in research, media engagement, and supportive policies. We can expect AI to play a crucial role in advancing telemedicine, enhancing remote patient monitoring, and enabling real-time health data analysis. These developments will be particularly beneficial in addressing healthcare disparities, providing access to quality healthcare in underserved regions, and managing public health crises. Moreover, AI’s integration into drug discovery and genomics will accelerate the development of personalized treatments and vaccines, improving patient outcomes and reducing healthcare costs. As AI technologies become more sophisticated and widely adopted, collaboration between governments, healthcare providers, and tech companies will be essential to ensure that AI-driven innovations contribute to a healthier and more equitable world, aligning with the goals of SDG 3.

 

Developed in collaboration with the European Commission project 

Here is your window to the world’s progress in improving global health. This dashboard is configurable and can be integrated into other systems to bring actionable technology into the systems of research institutions, governments and enterprises that are willing to make a difference.

 

 

Developed in collaboration with the European Commission project 

Avoiding data bias in AI systems is crucial to ensure fair, accurate, and equitable outcomes, preventing the reinforcement of existing inequalities and enabling more trustworthy and inclusive technologies. Here you will see a dashboard analysing the bias related to the data ingested in this observatory for SDG 3.

For analysis we use OECD AI Policy documents. Some of those documents are very large, and we split each document into smaller parts (so called “chunks”), which can contain multiple paragraphs. The reason for this is to prepare data for easier analysis with large language models, so called Retrieval-Augmented Generation (RAG). RAG is an advanced technique that combines retrieval-based methods with generative models to improve the performance of tasks such as question answering, text generation, and other natural language processing (NLP) applications. For each chunk then the sentiment is computed based on VADER (Valence Aware Dictionary and sEntiment Reasoner) methodology. Since VADER is known to have weak multilingual capabilities, all the documents were machine translated into English first.

While the results of this procedure are reliant not only upon the accuracy of the sentiment analysis tool, but also upon the accuracy of machine translation, it is important to stress that sentiment analysis is less sensitive to common machine translation problems than other usages, because sentiment analysis usually focuses on identifying the polarity (positive, negative, neutral) of a text rather than understanding its full semantic content. Also, sentiments in text are often expressed redundantly, which can help mitigate the impact of translation errors. As a result, minor translation errors that do not alter the overall sentiment and do not significantly impact the sentiment analysis is possible.

For the purpose of this analysis, they computed the average sentiment of (chunks of) AI policy documents for each country. We are presenting the visualisation of average sentiment of countries’ AI policy documents on the map. Since AI policy documents are mostly documents of legal nature (acts, policies, regulatory and governance frameworks), the sentiment should be mostly neutral, however, the analysis shows that there are country differences.

VADER computes positive, negative and neutral sentiment. Each of those values are between 0 and 1. The score indicates the proportion of text that is considered positive, negative and neutral. The sum of negative, positive, and neutral sentiment scores always equals 1, however in practice the sum of three sentiment scores can sometimes slightly exceed or fall below 1 due to floating-point precision errors or rounding issues that occur during computation.

Developed in collaboration with the European Commission project