TY  - JOUR
AU  - Ghose, Sankalpa
AU  - Hyun, Insoo
PY  - 2026
TI  - Toward computational assessment of scientific justification across biomedical research lifecycles
JF  - Journal of Medical Artificial Intelligence; Vol 9 (June 30, 2026): Journal of Medical Artificial Intelligence
Y2  - 2026
KW  - 
N2  - Background: Scientific justification is the foundation of institutional science, ensuring that research and experimentation are pursued on the basis of knowledge contribution, methodological rigor, ethical responsibility, and public benefit. In biomedicine, research investigators, academic departments, funders, and oversight bodies including Institutional Review Boards (IRBs), Institutional Animal Care and Use Committees (IACUCs), Scientific Review Boards (SRBs), and Stem Cell Research Oversight (SCRO) committees each take part in this process—yet evidence shows wide variability in how justification standards are applied across biomedical research lifecycles, with significant consequences for the practice of the scientific method and public commitments to it. Accordingly, this study aims to develop a more precise and practically applicable account of scientific justification.Methods: This study explores whether computational tools can assist in structuring and evaluating aspects of scientific justification in biomedical research. We propose a categorized and quantified rubric for evaluating justification across research lifecycles, broken down into seven stages of experimental and institutional operation, each assessed against seven core components of justificatory relevance. We develop a data architecture and processing pipeline for computing this in a parameterized schema with prompt-based workflows.Results: This computable framework is implemented in SciJust.AI, an artificial intelligence (AI)-enabled prototype that uses large language models (LLMs) to score research-related inputs, generate structured feedback, and visualize justification profiles. Constructed to support rather than replace human judgment, SciJust.AI advances a model in which investigators and reviewers can be equipped with an experimental assessment tool that could be used at key stages, or on an ad hoc basis, throughout the design, execution, and evaluation of biomedical research—with the aim of promoting greater clarity, consistency, and comparability from protocol to practice. Both manual and AI-assisted evaluation are made publicly available for early-stage testing, and we present illustrative example evaluations with the goal of generating discussion and gathering feedback for future improvements and validation efforts.Conclusions: By reframing scientific justification as a measurable, improvable dimension of the scientific method, this approach puts forward a practical path for better aligning experimental rigor with confidence in results.
UR  - https://jmai.amegroups.org/article/view/11399