# PneumoNet: Artificial Intelligence Assistance for Pneumonia Detection on X-Rays
## 3 Pneumonet Framework
### XAI Module
The regulatory readiness of the PneumoNet framework for clinical deployment requires careful consideration of compliance with international standards, such as CE marking for the European market and FDA approval for the United States. Currently, PneumoNet has not (yet) undergone formal conformity assessments for CE marking under the EU Medical Device Regulation (MDR, EU 2017/745) or FDA clearance/approval processes (e.g., 510 (k) or PMA). These processes involve rigorous evaluation of the system's safety, efficacy, and quality management system (QMS), typically aligned with ISO 13485 standards [40]. For CE marking, PneumoNet would likely be classified as a Class IIa or IIb medical device due to its diagnostic support function, requiring Notified Body audits of technical documentation and QMS. Similarly, for FDA compliance, PneumoNet would likely require 510 (k) clearance as a Class II device, demonstrating substantial equivalence to a predicate device, or potentially PMA for novel diagnostic applications. The absence of these certifications limits PneumoNet's current marketability in regulated regions such as the EU and U.S.

To achieve regulatory readiness, future development of PneumoNet should, of course, prioritise compliance with CE and FDA requirements. This includes preparing comprehensive technical documentation, conducting clinical evaluations to validate safety and performance, and implementing a QMS compliant with ISO 13485. For CE marking, collaboration with a Notified Body will be essential to assess conformity with MDR's GeneralSafety and Performance Requirements. For FDA compliance, a regulatory strategy should be developed to determine the appropriate pathway and compile evidence of substantial equivalence or clinical efficacy. Additionally, integrating post-market surveillance plans, as required by both CE and FDA regulations, will ensure ongoing safety and effectiveness. Addressing these regulatory requirements will enhance PneumoNet's credibility and facilitate its adoption in clinical settings, enabling broader market access and alignment with global healthcare standards. A second limitation of the PneumoNet framework is the absence of calibration analysis to assess how well the predicted probabilities from the PCM align with the actual risk of pneumonia. The current implementation, as evidenced in the source code available at [[https://github.com/kucaantunes/PneumoNet-Automatic-Pneumonia-Detection-on-X-rays](https://github.com/kucaantunes/PneumoNet-Automatic-Pneumonia-Detection-on-X-rays)]([https://github.com/kucaantunes/PneumoNet-Automatic-Pneumonia-Detection-on-X-rays](https://github.com/kucaantunes/PneumoNet-Automatic-Pneumonia-Detection-on-X-rays)) (accessed on 13 January 2025), does not evaluate calibration metrics such as the Brier score, which quantifies the mean squared difference between predicted probabilities and true outcomes, or calibration plots, which visually depict the correspondence between predicted and observed risks. Without these assessments, the confidence scores produced by PCM (e.g., 0.85 for a pneumonia prediction) may be misaligned with true probabilities, potentially leading to misleading outputs that could cause clinicians to over- or underestimate disease likelihood. This lack of calibration analysis undermines the reliability of PneumoNet's predictions in clinical settings, where accurate risk assessment is critical for informed decision-making, particularly in ambiguous cases such as early-stage or atypical pneumonia. To address this limitation, future iterations of PneumoNet should incorporate rigorous calibration evaluations across all datasets [8; 9; 10]. Calculating the Brier score would provide a quantitative measure of prediction accuracy, while calibration plots would visually confirm whether predicted probabilities align with observed outcomes, ideally following a 45-degree line.