Responsible AI Framework for Tuberculosis Screening: Enhancing Transparency and Trust in AI Integration

The Role of AI in TB Screening

Tuberculosis (TB) screening is a cornerstone of public health efforts worldwide, yet resource limitations and diagnostic challenges often hinder effective implementation. Artificial intelligence (AI) has shown immense potential in enhancing TB detection, but concerns about transparency, bias, and performance variability have slowed its adoption. DeepTek’s Responsible AI (RAI) framework was introduced to address these challenges—ensuring AI-powered TB screening is accurate, accountable, and bias-free.

The Study: Monitoring AI in Real-Time

To assess the effectiveness and reliability of AI-based TB screening, DeepTek’s RAI platform was used to evaluate the model across six districts in Tamil Nadu—Kanchipuram, Salem, Tiruchirapalli, Pudukottai, Vellore, and Tirunelveli—from April 2023 to March 2024. The study focused on:

✅ AI Model Performance: Sensitivity, Specificity, Negative Predictive Value (NPV), and Area Under the Receiver Operating Characteristic Curve (AUROC) were analyzed to evaluate AI efficiency across diverse settings.

✅ Bias & Drift Monitoring: The RAI framework actively monitored confounding variables, such as patient demographics, imaging modalities, and temporal variations to track AI performance shifts.

✅ Site-Specific Insights: Data was analyzed to ensure AI remained consistent, reliable, and unbiased across diverse populations and settings.

Key Findings: AI That Delivers

When the radiologist’s opinion was considered the reference standard, the results were extremely promising, reinforcing the reliability of AI-driven TB screening:

🔹 High Sensitivity & Accuracy: Sensitivity remained above 99%, with an AUROC exceeding 97%—confirming that AI effectively identified TB cases across all sites.

🔹 Surpassing WHO Benchmarks: Specificity consistently exceeded 88%, surpassing the World Health Organization’s (WHO) Target Product Profile (TPP) standards for TB detection.

🔹 No Evidence of AI Drift: Across all sites, model performance remained stable, with no significant drift or degradation over time.

🔹 Manageable Bias Levels: Bias was low to medium but within acceptable limits, highlighting the RAI framework’s ability to detect and mitigate performance deviations before they become significant issues.

Why This Matters: Trusting AI in Healthcare

AI adoption in public health screening requires continuous monitoring, ethical considerations, and regulatory compliance. DeepTek’s RAI framework is a critical step forward, ensuring AI systems are:

✅ Reliable: Performance tracking prevents errors and ensures consistent accuracy.

✅ Transparent: Drift and bias monitoring provide insights for ongoing improvements.

✅ Actionable: Post-market surveillance allows healthcare professionals to make informed decisions based on real-world AI performance.

The Future of Responsible AI in TB Detection

DeepTek’s Responsible AI is more than just a monitoring tool—it’s a bridge between cutting-edge AI and ethical, effective healthcare practices. By instilling transparency and trust, RAI paves the way for AI’s seamless integration into global TB elimination efforts.

As AI continues to reshape public health initiatives, responsible AI frameworks will be key to ensuring equitable, bias-free, and highly effective medical screening technologies.

The future of AI in healthcare is not just about innovation—it’s about responsibility, reliability, and trust.

To know more, visit : https://www.deeptek.ai/responsible-ai