MaternaAI: Enhancing Equitable Maternal Healthcare in Kerala with Fairness-Aware and Explainable Learning Models

Fairness-Aware Federated Learning Framework for ICU Mortality Prediction

Accurate prediction of ICU mortality is important for clinical decision-making and resource allocation. In the Federated Learning (FL) approach, privacy is maintained using decentralized hospital data to train machine learning models. Biases in hospital data may increase health inequalities. To address the issue, a new fairness-aware FL approach is developed. The two main metrics, Equalized Odds and Demographic Parity, are integrated in the FL optimization process. The proposed FL model uses adaptive client weighting, thereby minimizing bias propagation within the model. The approach is tested using the MIMIC-IV database. The experimental results demonstrate that the proposed model reduces subgroup differences without compromising predictive accuracy. Ablation studies prove that the combination of fairness regularization and client weighting gives better results. Statistical analysis validates that the achieved fairness improvement is clinically meaningful and robust. The findings conclude that fairness-aware federated learning for predicting critical care outcomes is a strong and viable framework balancing privacy, accuracy, and fairness. This proves to be an important step in the development of ethical AI in healthcare.

FairMoE-Health: Fairness-Aware Mixture of Experts for Equitable Multimodal Clinical Prediction.

Mixture-of-Experts (MoE) models for multi modal clinical prediction route patients to specialized ex pert networks based on input modalities, but we show that this routing mechanism introduces a previously un recognized source of demographic bias: because modality availability (e.g., whether a chest X-ray exists) correlates with race, gender, and insurance status, the gating network learns routing patterns that systematically differ across demographic groups. We propose FairMoE Health, a fairness-aware MoE framework that intervenes at three architectural levels: adversarial debiasing of encoded representations reduces demographic information before it reaches the gating network; adversarial debiasing of gating weights further discourages demographic leakage into routing decisions; and equalized odds regularization provides a prediction-level safety net. We introduce the Routing Disparity (RD) metric to quantify demographic imbalance in expert assignments. On three clinical prediction tasks from MIMIC-IV (mortality, length-of-stay, readmission), FairMoE-Health consistently reduces racial Equalized Odds Difference (EOD) and RD across all three tasks, with only small AUROC decreases. The Routing Disparity metric and multi-level debiasing framework introduced here generalize to MoE systems operating on demographically heterogeneous populations, providing both an audit tool and an architectural intervention for a bias mechanism that existing fairness methods leave unaddressed.

Can Zero-Shot LLMs Predict Child Malnutrition? A Fairness and Temporal Robustness Study

Child malnutrition remains a major public health challenge in low- and middle-income countries, particularly in South Asia, where early identification of vulnerable children is critical for timely intervention and resource allocation. This study aims to evaluate the feasibility, fairness, and temporal robustness of using a pretrained large language model (LLM) in a zero-shot setting for child stunting prediction using population health survey data. Using Bangladesh Demographic and Health Survey (BDHS) data collected between 2007 and 2022, we transformed maternal, child, healthcare, and household characteristics into semantically interpretable prompt-based representations and evaluated GPT-4o-mini for zero-shot stunting prediction, comparing its performance against a random forest baseline and assessing fairness across demographic and socioeconomic groups as well as temporal robustness across survey waves. The results demonstrate that zero-shot inference using GPT-4o-mini achieved comparable balanced accuracy to the supervised baseline while exhibiting substantially higher sensitivity for identifying stunting cases, relatively consistent performance across child sex groups, and stable predictive behaviour across BDHS waves; however, important fairness disparities were observed across residence and household wealth categories, highlighting the need for further investigation before deployment of foundation models in public health prediction settings.

MAPPE: Rethinking and Improving Fairness in LLMs for Medicine via Minimax Preference-based Prompt Evolution

This paper introduces universal fairness, a clinically grounded definition that reframes fairness as maximizing subgroup-aware diagnostic performance under attribute-conditioned health disparities, and proposes MAPPE, a training-free minimax prompt optimization framework that theoretically promotes universal fairness.

Fairness Audit and Debiasing of Machine Learning Models for Sepsis Mortality Prediction Across Demographic Subgroups: A Multi-Metric Study with Accuracy–Fairness Tradeoff Analysis

Machine learning (ML) models for sepsis mortality prediction are increasingly deployed in intensive care units, yet performance across demographic subgroups remains poorly evaluated, and algorithmic disparities can directly exacerbate health inequities in critical care settings. We trained three ML models—logistic regression (LR), XGBoost, and a multilayer perceptron (MLP)—on a 10,000-patient cohort calibrated to published MIMIC-IV sepsis statistics, and performed a four-metric fairness audit (equalized odds difference [EOD], demographic parity difference, predictive parity gap, and subgroup calibration error) across race/ethnicity, sex, and insurance type. Per-group threshold optimisation was applied for debiasing, and an accuracy–fairness Pareto tradeoff was quantified.

Predicting asthma-related hospitalizations in underserved and rural populations in Algeria