All Awards

Maurício Barreto and colleagues of Fiocruz in Brazil, together with Alexa Heeks and colleagues of the Health Foundation of South Africa in South Africa, will employ real-world data from two large countries of the Global South to develop a common data model of infectious diseases affecting pregnant women to identify causes and aid intervention development. Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS), together with the Western Cape Provincial Health Data Centre (WCPHDC), have built data systems to utilize routinely collected health data for exploring disease impacts. They will leverage these data systems to explore the impact of gestational syphilis in Bahia, Brazil, and tuberculosis in the Western Cape province of South Africa, and the coverage and effects of screening interventions. Teams will include data curators, analysts and scientists, who will perform data discovery and processing, alongside epidemiologists, clinicians and public health specialists, who will perform epidemiological analyses and community engagements.

Henrique Araujo Lima of the Universidade Federal de Minas Gerais in Brazil will develop a tool to systematically assess the accuracy and clarity of responses generated by Large Language Models (LLMs) to common questions on maternal health to increase their value in settings with limited healthcare access. To improve LLMs, it is essential to ensure the information they provide is both reliable and understandable, and for purposes such as health, LLMs will only be successful if both healthcare providers and users are confident about their benefits. They will collect the most common types of questions about maternal health in English, Portuguese, and Urdu, and submit them to the LLM. The quality of the answers will then be evaluated by medical experts from the U.S., Brazil and Pakistan, and the readability of the answers will be evaluated by individuals and a software model.

João Paulo Souza of the Fundação de Apoio ao Ensino, Pesquisa e Assistência in Brazil will determine whether Large Language Models (LLMs) can be utilized as accurate information sources to guide healthcare provider decision-making. Frontline health workers must make real-life care decisions by distinguishing between relevant and irrelevant information and contextualizing it to their setting. This is particularly challenging in remote areas with limited healthcare specialists. To support them, an information program, the Formative Second Opinion (FSO), was developed to produce curated evidence summaries based on a large repertoire of real-life clinical queries. An updated version of this program is now being developed to combine a mobile messaging platform with LLMs for regions with limited internet connectivity and computer access. Using a mixed-methods study approach, they will evaluate the accuracy of the evidence summaries generated by ChatGPT-4 and those created by humans to 450 randomly selected clinical questions.

Livia Oliveira-Ciabati of the Sociedade Beneficente Israelita Brasileira Albert Einstein Hospital in Brazil will leverage AI to produce guidance and monitoring tools for less-experienced or overworked health professionals providing prenatal care via telemedicine to people from minority groups and people with greater social vulnerabilities. During the COVID-19 pandemic, Brazil's maternal mortality rate jumped to 110 deaths per 100,000 live births, which is far from their target of reaching 30 deaths per 100,000 live births by 2030. To increase the quality of prenatal care, they will integrate LLMs with an API for converting and preparing voice data and develop a simple interface to present suggestions and results to the healthcare professional. The training database will be developed using protocols defined and validated by the scientific community, including from the Global South. They will test their model with health professionals by comparing the professional decisions with the AI's suggestions which they will then follow up with a randomized clinical trial.

Hugo Morales of Munai Health in Brazil will integrate OpenAI's ChatGPT-4 and other Large Language Models (LLMs) with Munai's Clinical Intelligence platform to help frontline healthcare providers adhere to guidelines for antimicrobial therapy and reduce antimicrobial resistance. Antibiotic-resistant bacteria cause over 20% of infections in Brazil. However, the antimicrobial stewardship programs designed to address this consist of complex protocols and there is little training for health workers in low-resource settings. The Munai platform incorporates machine learning into an application and web interface that is currently connected to 29 hospitals and hosts data from over 12 million medical encounters. They will incorporate conversational AI tools into the platform to enhance accessibility and usability. They will then convert institutional antimicrobial therapy protocols into a machine-readable format and incorporate patient data to allow for more personalized responses. The LLM will be evaluated by a prospective study engaging 20 physicians in a two-part simulated clinical trial.

Enrica Duncan of Mapa Do Acolhimento in Brazil will use AI to improve the influx of volunteer psychologists and lawyers to their support network, which provides mental health and legal support to women at risk of gender-based violence. In 2022, one woman died every six hours from gender-based violence in Brazil. They have built a network of 10,000 volunteers who have supported over 5,000 women. To expand this network, they have developed core training content using an inter-sectional feminist framework and will produce an equitable AI model to evaluate volunteers' backgrounds to better understand their skills and experiences. They will also incorporate localized knowledge to better serve all regions of Brazil. Machine learning will also be used to determine the optimal format to deliver training for each individual based on their engagement and responsiveness. These efforts aim to enhance the volunteer experience, improve knowledge absorption, and reduce turnover.

Henrique Dias of the Instituto de Inteligencia Artificial na Saude in Brazil will determine whether AI can produce an accurate hospital discharge summary to ensure that essential information is passed to the next healthcare provider and patient care is maintained. Discharge summaries are often incomplete, unclear, or delayed in terms of their delivery due to the document construction process. They will test two AI models to produce discharge summaries - one trained by health professionals completing electronic medical records, and the other trained with 46 GB of data in Portuguese, corresponding to 38 million clinical notes from 70 hospitals. They will perform a retrospective, non-inferiority, single-blind study to compare the quality and speed of the discharge summaries produced by both AI models with those produced by medical professionals.