This inaugural issue of NOVA MEDICA INFORMATICA (NOVEM) establishes the empirical baseline for the Medicine-Driven Informatics paradigm. Founded upon a formal consortium between the School of Medicine and the School of Information Technology, this volume presents research that subjects technological innovation to clinical validation rather than the reverse. Contributions herein demonstrate that valid health informatics requires interdisciplinary fidelity: clinical constraints, ethical boundaries, and ground truth must guide computational frameworks to ensure tangible healthcare improvement.
 
The issue is organized around three integrated themes. First, Clinical Artificial Intelligence and Validation features studies that extend beyond retrospective accuracy to present prospective validation, external testing across heterogeneous populations, and rigorous error analysis, with particular emphasis on Explainable AI mechanisms that enable clinical audit of algorithmic inference. Second, Implementation Science and Workflow Integration addresses socio-technical barriers to adoption, evaluating how digital health tools affect clinician cognitive load, patient engagement, and operational efficiency, treating technical performance as a necessary precondition rather than a primary endpoint. Third, Ethical Frameworks and Data Integrity examines clinical data provenance, bias, privacy, and equity, proposing frameworks for ethical deployment that prevent innovation from exacerbating health disparities.
 
All manuscripts underwent dual-track peer review involving both clinical practitioners and information scientists. Selection prioritized methodological transparency, reproducibility, and explicit acknowledgment of limitations. Negative findings and failure analyses were considered equally valuable as positive outcomes, provided they yielded actionable insights into technological constraints. This issue asserts that health informatics is not an engineering problem alone, but a complex scientific challenge requiring sustained interdisciplinary collaboration.
 

NOVA MEDICA INFORMATICA operates on a triannual cycle. Following this inaugural release, subsequent issues are scheduled for March, July and November of each year. We invite the scientific community to contribute to this ongoing iterative process.
 


Editor-in-Chief

Professor Dr.med. Paul L Tahalele, MD, PhD, FICS, FCTS

NOVA MEDICA INFORMATICA (NOVEM)

(On behalf of the School of Medicine and the School of Information Technology)

Overview
This second issue of NOVA MEDICA INFORMATICA (NOVEM) marks a strategic expansion of our interdisciplinary consortium. While founded upon the collaboration between the School of Medicine and the School of Information Technology, this volume formally incorporates the School of Dental Medicine, reinforcing our commitment to holistic patient care through rigorous informatics. The contributions herein subject technological innovation to clinical validation across diverse specialties, adhering to the "Medicine-Driven Informatics" paradigm where clinical necessity dictates computational development.

Thematic Focus
The issue is organized around six peer-reviewed investigations that demonstrate methodological rigor, external validation, and critical evaluation of AI capabilities. The research spans hepatology, nutrition, oncology, dental radiology, emergency medicine, and obstetrics, unified by a demand for explainability and clinical safety.

Clinical Prediction and Survival Analysis
Studies in this section address complex multi-class prediction challenges in internal medicine and public health. Research presents optimized ensemble machine learning frameworks for survival prediction in primary biliary cirrhosis, tackling severe class imbalance through cost-sensitive learning. Additional work explores multi-class prediction of vitamin deficiency diseases, integrating socioeconomic determinants with biomarker profiles to enhance scalability in resource-constrained settings.

Oncology and Microbiome Informatics
Contributions apply compositional data analysis to gut microbiome profiles for the detection of colorectal neoplasia. This work emphasizes the geometric constraints of microbiome data, utilizing centered log-ratio transformations to avoid spurious correlations in machine learning pipelines.

Imaging and Diagnostic Safety
Reflecting the journal's stance on methodological skepticism, imaging studies provide critical assessments of deep learning deployment. Research evaluates semantic segmentation models for pediatric caries detection in panoramic X-rays, incorporating Explainable AI to ensure clinical interpretability. Conversely, safety assessments of lightweight object detection models for multi-anatomical fracture detection empirically demonstrate the accuracy-deployability paradox, arguing against autonomous deployment of underspecified models in emergency radiology.

Systematic Evidence Synthesis
A hierarchical bivariate meta-analysis of cardiotocography interpretation paradigms critically distinguishes between automated rule-replication and physiological interpretation. This review provides evidence that pathophysiology-anchored frameworks significantly reduce emergency cesarean rates compared to conventional visual guidelines and automated rule-based software.

Commitment to Rigor
All manuscripts underwent dual-track peer review involving clinical practitioners and information scientists. This issue asserts that health informatics is not merely an engineering challenge but a scientific endeavor requiring interdisciplinary fidelity. Negative findings and limitations are reported with transparency to prevent algorithmic overconfidence.

Publication Continuity
NOVA MEDICA INFORMATICA operates on a triannual cycle. Following this release, subsequent issues are scheduled for March, July, and November of each year. The next issue is scheduled for November 2026. We invite the scientific community to contribute to this ongoing iterative process.

 

Editor-in-Chief
Professor Dr.med. Paul L Tahalele, MD, PhD, FICS, FCTS

NOVA MEDICA INFORMATICA (NOVEM)
(On behalf of the School of Medicine, School of Information Technology, and School of Dental Medicine)