Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Brazil

AI matters for healthcare in Brazil because the Brazilian Artificial Intelligence Plan (PBIA 2024–2028) aims to channel large public investment - R$23 billion by 2028 - into concrete fronts that can modernize the SUS, from faster diagnostic support and stroke triage to smarter medication procurement and anomaly detection in billing; Brazil's advantage is a nationwide health system that has decades of data on over 200 million people and ongoing efforts like the RNDS to connect records at scale, so locally trained algorithms can reflect real genetic and regional diversity rather than imported biases.

To turn policy into patient impact requires both clear regulation and practical skills for health teams and innovators - see analysis of PBIA and gaps in implementation - and short, job-focused training like Nucamp's AI Essentials for Work to build prompt-writing and AI-on-the-job skills for clinicians and managers.

Learn more about PBIA and explore the AI Essentials for Work bootcamp syllabus.

Bootcamp	Length	Cost (early bird)	Syllabus / Register
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work bootcamp syllabus • Register for the AI Essentials for Work bootcamp

“Brasil is committed to strengthening digital health and AI in health, in collaboration with the private sector, focusing on inclusion, equity, and social justice,” stated Ana Estela Haddad.

Selection prioritized real-world impact in Brazil by testing each candidate use case against a consistent rubric: alignment with PBIA's health fronts (diagnostic triage, medication procurement, anomaly detection), demonstrable access to SUS‑scale data and RNDS interoperability, evidence of clinical validation or measurable efficiency gains, technical feasibility given national infrastructure plans (including the planned supercomputer), conformity with LGPD and regulatory readiness, and potential for equitable scaling across regions and populations.

Sources such as the IBIS analysis of the PBIA helped weight criteria toward solutions that leverage Brazil's decades‑long SUS datasets and unique genetic diversity rather than imported models, while updates on the PBIA rollout guided attention to projects that could realistically tap shared computing and training investments (see the PBIA announcement and infrastructure notes).

Projects were scored on data provenance, validation studies, deployment pathway with public‑private partnerships, and required workforce training - because an AI alert only saves lives if clinicians trust and can act on it - and cases that promised to move from pilot to system‑wide use were ranked higher in the top 10 list.

Learn more in the IBIS review and the PBIA release from LNCC linked here.

“Why can't a country with 200 million people, a nation 524 years old with a globally respected intellectual foundation, create its own mechanisms instead of relying on AI from China, the United States, South Korea, or Japan? Why can't we have our own?”

Harpia Health Solutions' Delfos, positioned as an automated diagnostic imaging triage and prioritization tool, illustrates how Brazilian hospitals could speed the path from scan to action by combining image pipelines with robust natural‑language extraction from radiology reports; academic work shows that recurrent neural networks can automatically annotate free‑text radiology findings with surprisingly low error rates - a three‑layer RNN reached a word‑error rate of just 1.03% and achieved precision, recall and F1 around 0.967 (accuracy 0.982) when classifying fracture versus nonfracture - a level of performance that translates, in practical terms, to about one typo per 100 characters yet near‑clinical classifier reliability.

In Brazil this technical backbone becomes valuable when linked to national initiatives and datasets (RNDS) and to PBIA funding priorities for diagnostic triage: tighter report annotation makes it easier to flag urgent cases, prioritize transfer and inform ICU/bed forecasting in busy SUS hospitals (see research on RNN annotation in the AJR and how predictive capacity planning can cut costs and improve efficiency).

Responsible deployment will also hinge on LGPD‑aware governance and the EBIA/AI strategy channels that fund scaling and validation across diverse Brazilian populations.

Metric	Value
No. of layers (best model)	3
Word Error Rate (WER)	1.03%
Precision	0.967
Recall	0.967
Accuracy	0.982
F1 score	0.967

LABDAPS at USP has become a cornerstone for Brazilian AI in cardiology by focusing on high-quality local data, practical validation, and transfer‑learning strategies that acknowledge regional genetic and demographic diversity; the lab's work - led by Alexandre Dias Porto Chiavegatto Filho and described in a FAPESP review - showed that models trained on local datasets (including a Covid‑19 mortality study of 16,236 patients across 18 hospitals) outperform one‑size‑fits‑all algorithms, and the Telehealth Center's daily stream of roughly 4,000 ECGs has powered two Nature Communications studies: an automated 12‑lead ECG classifier (sensitivity ~80%, specificity ~99%) and an “ECG‑age” predictor linked to mortality risk, demonstrating how routine ECGs can be repurposed into population‑level risk signals; these results suggest a realistic path for SUS hospitals to deploy decision support that flags at‑risk patients and helps prioritize scarce cardiology resources, while underscoring the need for careful curation, LGPD‑compliant governance, and clinical validation before scaling (see the FAPESP overview of LABDAPS AI research and a recent PubMed meta-analysis of AI ECG performance for context).

Metric / Finding	Value
Telehealth Center ECGs per day	~4,000
12‑lead ECG automated diagnosis (sensitivity)	80%
12‑lead ECG automated diagnosis (specificity)	99%
Covid‑19 mortality study (patients / hospitals)	16,236 patients / 18 hospitals

“Instead, AI's powerful analytical capabilities will be used as a tool to aid physicians in decision-making,” says Alexandre Dias Porto Chiavegatto Filho.

The Genomas Brasil Program is the federal push to make genomic‑driven precision medicine practical within the SUS by building a Brazilian reference genome and a linked national genomic‑clinical database that reflect the country's unique admixture and regional diversity; by doing so the program seeks to accelerate diagnosis for rare diseases (the Brazilian Rare Genomes Project already validated an effective whole‑genome sequencing method for diagnosis), expand installed scientific capacity, and train the SUS workforce so genomics moves from research benches into routine risk stratification and care pathways.

Backed by Ministry of Health financing channels and public–private partnerships, the initiative also aims to strengthen a domestic genomics industry and create the data infrastructure needed to turn population‑scale sequencing into actionable signals for prevention and treatment.

For program specifics, see the Genomas Brasil program summary and a broader World of Genomics review of Brazil's genomic projects that places Brazil's projects in demographic and policy context.

Program Objective	Short description
Establish reference genome	Create a Brazilian population reference to reflect admixed ancestries
National genomic & clinical database	Integrate genomic data with clinical records for research and care
Increase scientific capacity	Expand sequencing capability and national intellectual capital
Strengthen industry	Support competitiveness of domestic genomic products and inputs
Train SUS workforce	Prepare clinicians and labs for genomic medicine in public health

CIIA‑Saúde at UFMG can shepherd Brazil's growing evidence that machine‑learning models meaningfully improve maternal and neonatal risk detection: recent Brazilian studies used tree‑based algorithms (Random Forest, XGBoost, CatBoost, LightGBM) to predict low birth weight from cohort and routine prenatal data, demonstrating a practical path from prenatal records to early‑warning lists that flag which pregnancies need extra monitoring and NICU preparedness weeks before delivery; these models - described in open‑access BMC articles - also create the data inputs needed for hospital predictive capacity planning so beds and neonatal staff are ready when a high‑risk birth is likely (see the Araraquara cohort study and the tree‑based LBW evaluation).

Linking those predictive signals to SUS workflows and demand forecasting tools turns dispersed prenatal forms into a color‑coded triage board that can spare a newborn from delayed care - a simple change with big downstream impact.

For technical details and implementation context, read the BMC papers on tree‑based LBW prediction and the Araraquara cohort, and learn how predictive capacity planning helps Brazilian hospitals anticipate ICU and bed demand.

Study	Dataset / Location	Algorithms	Journal (Year)
Utilization of tree-based machine learning models for predicting low birth weight cases	Brazil (multi‑institution affiliations: UPE, UFAPE, UFCG)	Tree‑based models	BMC Pregnancy Childbirth (2025)
Predicting low birth weight risks in pregnant women in Brazil (Araraquara cohort)	Araraquara cohort, Brazil	Random Forest, XGBoost, CatBoost, LightGBM	BMC Pregnancy Childbirth (2025)

The PBIA targets medication procurement, logistics and supply optimization as a concrete way to turn R$23 billion in AI funding into faster, safer care across the SUS: the plan allocates R$435 million for near‑term actions and explicitly lists “AI for Medication Purchasing Decisions” among its priority fronts, aiming to use RNDS‑connected data to predict stockouts, root out procurement inefficiencies and guide smarter distribution to remote clinics; on the ground, small wins already point the way - an open‑source pharmacist assistant developed by NoHarm has quadrupled prescription processing in Amazon towns like Caracaraí and flagged more than 50 dangerous errors, showing how AI can keep medicines flowing and patients safe (see the IBIS analysis of PBIA and the Rest of World report on NoHarm).

PBIA Item	Detail
Total investment (2024–2028)	R$23 billion
Immediate actions fund	R$435 million (short‑term deliverables)
Relevant PBIA front	AI for Medication Purchasing Decisions / logistics optimization

“This plan is bold and viable, robust and feasible, carried out with public investment with sovereignty and autonomy to make our country's intelligence count,” said Luciana Santos.

Regulators are moving too: ANVISA's new AI tools for impurity qualification promise faster, more accurate drug reviews, which can shorten time‑to‑market and stabilize supplies.

Yet the PBIA's promise for procurement hinges on solving real gaps - interoperable records, sovereign compute, LGPD‑compliant governance, regulatory sandboxes and targeted workforce training - so AI investments translate into pills on shelves, not just pilot dashboards; for implementation details and policy context, review the IBIS PBIA assessment and ANVISA innovation notes.

For a nationwide payer like DATASUS, automated anomaly detection is less a novelty and more a practical lever to cut leakage and speed audits: ensemble and tree‑based methods that excel in billing datasets - think Random Forests, Gradient Boosting and stacked Voting Classifiers - can prioritize suspicious claims so human reviewers focus on the riskiest few instead of scanning millions of rows; recent work shows a Voting Classifier can push AUC toward 0.995 in utility billing experiments, while broader reviews of methods (Isolation Forest, LOF, autoencoders, LSTM) explain how point, contextual and collective anomalies are captured across time series and transactional feeds.

See the IEEE study on anomaly detection in billing data for model and ensemble details and MindBridge's practical rundown of anomaly techniques and real‑world audit use cases for implementation patterns.

The payoff is tangible: a system that surfaces a single suspect claim among a sea of routine entries - literally finding the needle in a haystack - and routes it into a graded workflow so investigators spend time where savings and compliance impact are highest.

Study / Metric	Value
Voting Classifier (IEEE SCOReD 2024) - AUC	0.995229
Common algorithms discussed	Random Forest, Gradient Boosting, XGBoost, CatBoost, Isolation Forest, LSTM, Autoencoders

The Telehealth Center at UFMG powers the Telehealth Network of Minas Gerais (TNMG), a public telecardiology backbone that has scaled from a regional pilot into a national lifeline - supporting 14 of Brazil's 27 states and 1,320 municipalities (72.1% with ≤20,000 inhabitants) and issuing nearly 9.9 million ECG reports through October 2024; the service operates 24/7, pairs synchronous eConsults with certified cardiologists, and flags urgent cases so emergency reports reach clinicians in a median of just 47 seconds, a pace that can make the difference between rapid reperfusion and delayed care.

Built-in decision‑support (automatic Glasgow measurements, “intelligent” reporting rules), real‑time dashboards and continuous training keep quality high while the TNMG's large digital ECG archive fuels research and AI work (CODE studies and deep‑learning ECG‑age models) that aim to triage normal tracings, surface technical issues, and prioritise critical reports.

For a concise review of TNMG's national upscaling, see the Telehealth Network of Minas Gerais analysis in BMJ Global Health and the PubMed summary of telehealth quality in Minas Gerais, and explore how predictive capacity planning can turn these signals into better bed and ICU forecasting.

Metric	Value
States covered	14 of 27
Municipalities supported	1,320
Municipalities ≤20,000 inhabitants	72.1%
Total ECG reports (through Oct 2024)	9,877,764
ECGs reported per month (approx.)	150,000
Median time - elective report	41 min 30 s
Median time - emergency report	47 s

The Hoobox Sadia module frames remote patient monitoring for Brazilian elder care around proven building blocks: an intelligent video central monitoring architecture (realtime video feeds, instant event alerts, PTZ control and event positioning on a digital map) that scales from a single clinic to multi‑tier deployments, consumer‑grade remote cameras with motion detection and two‑way audio for home visits, and the sensor and imaging approaches (motion sensors, environmental sensors, camera analysis, even robotic patrols) identified in a recent scoping review of fall‑detection technologies; together these pieces make it realistic to detect a fall or the early signs of a pressure ulcer before an emergency escalates, turning a static ward into an active safety net.

For technical context see the Delta intelligent video central monitoring platform and the JAMDA scoping review of fall‑detection systems, while market forecasts show growing demand for these tools worldwide.

In short, when paired with LGPD‑aware governance and local SUS workflows, a Hoobox Sadia–style setup can route a true alarm to a caregiver in minutes instead of after the fact, preventing harm and avoiding costly hospitalizations.

Metric	Value (USD)
Fall detection systems market (2023)	447.2 million
Projected market (2030)	748.4 million

When telehealth calls, lab results and routine surveillance are stitched together into a national fabric, Brazil gains a true early‑warning system: a JMIR case study demonstrated how syndromic surveillance using structured telehealth data tracked signals during the first COVID‑19 wave, and a traveling SARS‑CoV‑2 laboratory showed how mobile labs can feed timely results into the federal healthcare network to reach vulnerable populations quickly (BMC Public Health).

Complementary work on influenza and RSV highlights how integrated virology surveillance turns seasonal blips into interpretable trends that guide resource allocation, and field studies of arbovirus lab syndromic surveillance have even uncovered co‑circulation of dengue, Zika and chikungunya in single communities - a reminder that a single positive sample can signal a broader outbreak.

Linking those streams to a RNDS‑style data backbone, supported by expanding genomic capacity and public–private sequencing partnerships, creates the practical pipeline for epidemic detection: from noisy clinic signals to prioritized alerts that help SUS managers target testing, mobile labs and vaccination drives before hospitals overflow.

Solutions like Onkos Diagnósticos Moleculares' mirTHYpe are an ideal fit for Brazil's push toward AI‑assisted pathology because they can translate small, actionable molecular signatures into clearer decisions at the point of fine‑needle aspiration (FNA); peer research shows microRNA panels can distinguish benign from malignant nodules and even predict prognosis, with a four‑miRNA signature reported in World Journal of Gastrointestinal Oncology and an FNA study where a decision rule based on miR‑146b, miR‑155 and miR‑221 correctly classified 86 of 88 nodules (97.73%), while cross‑validation gave 78.41% reliability (sensitivity 79.07%, specificity 77.77%) - a level of performance that turns ambiguous cytology into a usable clinical nudge.

In practical terms, that means a lab workflow that once produced an “indeterminate” result can now flag which patients most need surgery or closer follow‑up, a change that feels like converting fuzzy grainy film into a focused portrait.

Bringing these assays into SUS hospitals will also require the governance, LGPD and strategic alignment steps highlighted in Brazil's national AI guidance to move from promising paper studies to trustworthy, scalable diagnostics (World Journal of Gastrointestinal Oncology four‑miRNA signature study, Endocrine Abstracts miRNA FNA decision model, EBIA national AI strategy for Brazilian healthcare).

Finding	Value / Note
Decision model classification	86 / 88 nodules (97.73%)
Cross‑validation reliability	78.41%
Sensitivity	79.07%
Specificity	77.77%

Beginners who want to help Brazil turn PBIA promises into patient impact should start with achievable, responsible steps: learn practical AI‑at‑work skills and prompt design so clinical teams can use tools safely (see the Nucamp AI Essentials for Work bootcamp syllabus), pair that learning with focused study of LGPD and emerging health regulation, and pursue small, measurable pilots in primary care or telehealth that link to RNDS datasets and clear outcome metrics; IBIS's review of the PBIA shows the roadmap is funding‑ready but still needs stronger data infrastructure, regulatory sandboxes and workforce training, so novices should look for collaborations with universities, startups and SUS managers to test models under clinical oversight (see the IBIS analysis: AI in Public Health - PBIA review).

In short: build skills, respect privacy, start small, measure impact - and aim to turn “an AI alert” into a patient seen in time rather than an unexplained dashboard spike.

PBIA Item	Value / Target
Total investment (2024–2028)	R$23 billion
Immediate actions fund	R$435 million
Training goal	20,000 professionals per year (by 2028)

“Brasil is committed to strengthening digital health and AI in health, in collaboration with the private sector, focusing on inclusion, equity, and social justice,” stated Ana Estela Haddad.