JMIR Metascience and Research Integrity

Health care AI research is expanding rapidly, but publication volume alone does not generate evidence that reliably informs research, clinical care, policy, governance, or responsible technological development. Studies may report promising predictive performance while leaving uncertain whether the question is decision-relevant, whether the data fit the intended population, whether performance transfers across settings, whether the output can be integrated into a feasible workflow, or whether a deployed system can be monitored over time. These gaps can create avoidable losses of scientific, clinical, and societal value. This viewpoint argues that algorithmic research waste occurs when health care AI research is insufficient, relative to its stated purpose and maturity stage, to generate reliable and decision-useful evidence. The concept does not require every model to reach deployment. Exploratory studies, methodological research, replication, external validation, and negative findings can all be valuable when their aims, methods, claims, and intended decisions are aligned. Concern arises when research is presented as supporting a broader conclusion than its design and evidence can justify. This viewpoint aims to define algorithmic research waste, distinguish its principal pathways, and introduce VALUE-AI (Valid Question and Purpose, Appropriate Data and Population Fit, Local Workflow and Intended Use, Usability, Safety, and Accountability, Equity and External Validity, and Auditability and Impact Monitoring) as a conceptual metascience lens for identifying value-loss signals and routing them toward appropriate action. Four overlapping pathways are described: primary research-value loss, in which the question, design, conduct, reporting, or claims cannot support the stated purpose; contextual value loss, in which evidence is transferred without adequate attention to population, data, workflow, resource, or regulatory differences; deployment failure, in which an organization implements a technology without necessary local integration and safeguards; and postdeployment failure, in which changes in performance, use, equity, or safety are not adequately monitored or addressed. VALUE-AI is qualitative and stage-specific. The lens asks where potential research value is being lost, which actor or actors have primary control over the source of that loss, and what maturity-appropriate evidence or action is needed next. An illustrative example shows how the same model may represent useful discovery research, primary research-value loss, contextual value loss, deployment failure, or postdeployment failure, depending on its claims, evidence, setting, and intended use. VALUE-AI is not a validated reporting guideline, risk-of-bias tool, scoring system, implementation instrument, or regulatory standard. It is intended to complement established reporting, appraisal, implementation, and governance frameworks by organizing their implications around research value, attribution, and the next maturity-appropriate evidence or action. Its domains, feasibility, interrater interpretability, and effects on decisions require empirical refinement and evaluation across specialties, health systems, resource settings, and AI modalities.