9.Y.2. Leveraging GenAI and Deep Learning for the Analysis of Public Health Data: A Global Perspective
Bibliographic record
Abstract
Abstract The evolution from Artificial Intelligence (AI) to Generative Artificial Intelligence (Gen AI) represents a significant advancement in technological capabilities and applications. Advancements in computing power, algorithmic complexity, and data availability have enabled Gen AI to exhibit broader cognitive abilities such as reasoning, problem-solving, and learning across diverse domains. Private sector investment and innovation is advancing capability in this space at a rapid pace, trending toward a complete paradigm shift towards machines capable of autonomously adapting and learning in dynamic environments, promising profound impacts across industries from education and finance to healthcare and beyond. Yet harnessing the power of GenAI remains out of reach for most public health entities. Questions like “How to start” and “What to do”, let alone concerns regarding cost, security, and ethics, often thwart an agency from endeavoring to even begin their journey to the program improvement they visualize through the lens and promise of AI. Conduent’s longstanding presence and pioneering work in AI across various sectors, has led to a simple, underlying philosophy: we leverage Gen AI technologies to assist and enable humans to improve business processes. Instead of developing Gen AI technologies ourselves, we focus our efforts on making advanced technologies accessible to our clients and enabling them to achieve their outcomes. This session focuses on accessibility and enablement for public health agencies looking to jump start their journey to AI and evolve their public health programs with the use of Gen AI, covering topics such as: • The evolution of GenAI in the private sector; • Visualizing objectives in public health outcomes and improved business processes; • What to do about data; • Using human intelligence to drive machine learning; • Managing security, ethics, and equity; • Roadmap your Journey to Gen AI. For Conduent, Gen AI is not intended to replace humans but to harness its potential to support and empower humans in creating innovative, additive opportunities that enhance business process outcomes. In the public health space, its impact is immense and far reaching. Let’s shift from “How do I start” to tackling the questions that improve programs and their outcomes. So how will you optimize your program with Gen AI? Let’s get started. Key messages • Private sector Gen AI capability is expanding at a rapid pace yet many public health agencies are struggling just to start. Conduent offers an informed perspective to jump start your Gen AI journey. • Accessibility and enablement for public health agencies looking to jump start their journey to AI and evolve their public health programs with the use of GenAI. Speakers/Panelists Mark Marostica Conduent Public Health Solutions, Florham Park, USA Dianna Lydiard Conduent, Florham Park, USA Tarun Khatri Conduent, Burlington, Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".