Aims & Scope

Applied data intelligence for meaningful health questions

JADIH is designed as a transversal scientific forum for rigorous work at the intersection of health, data, analytical methods and real-world application.

Purpose

The purpose of JADIH is to improve health by publishing rigorous research that transforms data into evidence and uses data intelligence, artificial intelligence and quantitative methods responsibly.

The journal prioritises interpretable, useful and methodologically sound knowledge over technological novelty for its own sake. The research question and potential health value should guide the choice of methods.

Core objectives

01 · Applied relevanceConnect analytical innovation to a clear health, biomedical, clinical, population or healthcare-system problem.
02 · Scientific rigourPromote valid design, appropriate statistical/computational methods, transparent reporting and conclusions supported by evidence.
03 · Responsible innovationAddress generalisability, interpretability, bias, fairness, privacy, safety and real-world usefulness when evaluating data-intensive methods.
04 · Interdisciplinary exchangeBridge clinical science, epidemiology, biostatistics, data science, engineering, informatics, public health and health economics.
05 · Equitable accessPublish under a Diamond Open Access model with no APCs for authors and no subscription fees for readers.
06 · International reachDevelop a geographically diverse community while accepting complete manuscripts in both English and Spanish.

Scientific scope

Health Data Science

Health-data management and analysis, Real-World Data, Real-World Evidence, clinical registries, longitudinal data, data quality, linkage and large-scale health datasets.

Artificial Intelligence & Data Intelligence

Machine learning, deep learning, computer vision, NLP, large language models, generative AI, foundation models, federated learning and privacy-preserving methods.

Predictive & Causal Analytics

Prognostic models, risk stratification, survival analysis, time series, forecasting, causal inference, target-trial emulation and impact evaluation.

Digital Health

Telemedicine, remote monitoring, connected devices, wearables, digital biomarkers, digital therapeutics and decision-support tools.

Biomedical & Clinical Informatics

Clinical informatics, bioinformatics, interoperability, data standards, ontologies, electronic health records and clinical decision support.

Health Systems & Population Analytics

Computational epidemiology, public health, resource utilisation, health-system organisation, outcomes research, health economics and pharmacoeconomics.

Methodological Innovation

Biostatistics, model validation, reproducibility, synthetic data, benchmarking, software tools and methodological frameworks relevant to health research.

Especially aligned work

  • External validation or evaluation in independent contexts.
  • Research addressing equity, bias, safety or generalisability of models.
  • Studies with reproducible code, protocols or materials when feasible.
  • Work combining clear health relevance with well-justified methodological innovation.
  • Real-world data studies with careful attention to data quality, confounding and traceability.

Generally outside scope

JADIH will generally not prioritise purely technical demonstrations without a clear health question, weak internal-only model evaluations, descriptive analyses without sufficient methodological or scientific contribution, or manuscripts whose conclusions clearly exceed the available evidence.