Research waste occurs when the resources and participant contributions invested in research fail to yield sufficiently reliable or useful knowledge. It may result from poorly prioritized questions, avoidable weaknesses in study design or conduct, incomplete reporting, nonpublication, unnecessary duplication, or failure to translate findings into practice [1-3]. A recent scoping review classified these manifestations as methodological, invisible, negligible, underreported, and structural waste, demonstrating that value can be lost throughout the research system rather than at a single methodological step [4]. Health care AI magnifies this concern. Although the number of clinical prediction models is rapidly increasing [5], systematic reviews continue to find incomplete reporting [6], high risk of bias [7], limited external validation [8], and overstated clinical claims [9]. A 2025 systematic review of 1263 intensive care AI studies found that 74% remained at early stages of development and only 25 (2%) had progressed to clinical integration [10]. Although confined to intensive care and not directly generalizable to health care AI as a whole, these findings caution against interpreting growth in publication volume as a comparable increase in decision-useful evidence.

Reviews underscore the need to distinguish technical promise from an evidence pathway that can inform care. A systematic review of 152 supervised machine-learning prediction-model studies found that 148 of 171 development or validation assessments were at high risk of bias, commonly due to inadequate sample size, poor handling of missing data, or overfitting [7]. 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) treats these deficits as potential sources of avoidable value loss when they prevent a study from supporting the decision implied by its claims. This problem overlaps with, but is not identical to, low-value care or health care waste. Low-value care concerns services that provide no benefit or may cause harm [11], whereas health care waste is broader and encompasses inefficient or unnecessary resource use in care delivery [12]. Algorithmic research waste instead concerns the value of the knowledge produced by AI research. Economic value remains relevant because AI may alter outcomes, workload, workflow, costs, access, and equity in ways that conventional technology assessments do not fully capture [13].

This viewpoint defines algorithmic research waste, distinguishes research-level value loss from contextual and deployment failures, and presents VALUE-AI as a stage-specific metascience attribution-and-routing lens. The lens links each value-loss signal to the actor or actors who can modify it and to the next maturity-appropriate evidence or action. Evidence is decision-useful when it can credibly reduce uncertainty for a specified research, clinical, policy, governance, or implementation decision at the study’s stated maturity stage. The intended audience includes researchers, peer reviewers, editors, funders, health systems, regulators, developers, and patient and public representatives. VALUE-AI complements translational guidance [14] by examining whether health care AI research generates reliable and decision-useful evidence across the pathway from question formulation to postdeployment learning.

Algorithmic research waste is the avoidable loss of potential scientific, clinical, or societal value when the question, design, conduct, reporting, validation, or translation of health care AI research is insufficient, relative to its stated purpose and maturity stage, to generate reliable, decision-useful evidence for research, care, policy, or responsible technological development. This definition does not require deploying every model. Exploratory analyses, methodological studies, replications, external validations, and negative findings may be valuable when their claims align with their purpose and reduce uncertainty or enable later work [1,4]. Research value does not require universal generalizability or immediate deployment [15]. A model developed for a narrowly defined population may answer an important local question, and a carefully conducted negative validation may prevent an unsafe or unproductive implementation. The value-loss signal arises when the intended scope is unclear, local performance is presented as broadly transportable, or a decision is invited without evidence suited to that decision. This distinction protects focused, early-stage work from being mislabeled as waste while still exposing inflated claims.

The unit of appraisal is therefore the relationship among purpose, evidence, claim, and prospective use. The 4 VALUE-AI pathways are analytic locations where that relationship may fail, not a required sequence every project must follow. Discovery work can remain valuable without advancing to deployment, provided its claims are grounded in discovery-level evidence. Conversely, a strong study may lose value if its design, analysis, or reporting cannot support the clinical or policy inference readers are encouraged to draw [2,16]. Judgment requires an explicit counterfactual. The relevant comparison may be no study, an existing model, a simpler clinical rule, better data collection, independent validation, or investment in implementation capacity. Repetition is not automatically wasteful when it tests reproducibility or transportability, and novelty is not automatically valuable [3,17] when it bypasses a more consequential evidence gap. Assessors should therefore state the implied alternative use of effort and whether the information produced could reasonably change a scientific, clinical, operational, or policy decision. This keeps prioritization distinct from hindsight: an informative study can remain valuable even when its result is null or unfavorable.

The appraisal is qualitative and stage-specific, not a binary threshold or numerical score. Under this lens, actors with primary control are those with the authority or practical capacity to make or influence the relevant decision or to change the associated process when a value-loss signal is identified. Researchers, reviewers, funders, and implementers should identify the relevant unit of analysis; the evidence supporting the judgment; whether the limitation was reasonably foreseeable and modifiable; which actor or actors had primary control; and what further evidence or action is needed to reduce the loss of value. A transparent discovery study can therefore be decision-useful without a deployment plan, whereas it becomes waste-prone if it makes claims that exceed its maturity—for example, by claiming readiness for routine clinical use without appropriate validation [18], evidence on workflow and safety [19], or governance, monitoring, and the intended response pathway [20,21]. Minor methodological choices can alter the meaning of reported performance. A review of 62 oncology machine-learning prognostic-model publications identified weaknesses in sample-size justification, calibration assessment, and model availability for external validation [22]. A companion analysis of the same publications found that comparative claims were overinterpreted or insufficiently supported [16]. VALUE-AI asks whether such weaknesses materially compromise the study’s purpose, interpretability, reproducibility, or plausible route to benefit. These examples bridge methodological appraisal and value appraisal: a deficit matters because it narrows the claims or decisions the evidence can support. The next step is to locate that loss along the evidence-to-impact pathway.

Table 1 summarizes 4 pathways that localize value loss. These categories identify where value loss occurs across the evidence-to-impact pathway; they are not 4 mutually equivalent attributes of an individual study. Primary research-value loss arises when the research question, design, conduct, reporting, or claims cannot support the study’s stated purpose. Contextual value loss arises when evidence useful under one set of conditions is transferred or generalized without adequate examination of population, infrastructure, workflow, resource, or regulatory differences [15,23]. Deployment failure occurs when a receiving organization implements a technology outside its supported conditions or without necessary workflow, training, and governance safeguards [19,24]. Postdeployment failure occurs when a deployed system is not monitored, updated, or withdrawn when performance, use, equity, or harms change [20,25]. As a practical boundary, contextual value loss applies when transfer-critical conditions or limits were insufficiently investigated, reported, or considered during evidence selection; deployment failure applies when a local organization disregards known limits or fails to establish required safeguards. Nontransportability alone is not contextual value loss. Contextual value loss arises when transfer-critical uncertainty that was reasonably foreseeable for the stated intended use is inadequately investigated or communicated by those producing the evidence, or inadequately considered by those selecting, generalizing, adapting, or applying it in a new setting. These pathways are overlapping heuristic labels rather than mutually exclusive classes: a case may fit one, several, or none. Equity is cross-cutting because proxy choices can distribute errors, benefits, and burdens differently across groups [26].

AI studies should be judged relative to their maturity stage and intended function. Evidence sufficient to establish a signal in discovery is not sufficient to justify integration into care [14,19]. Conversely, requirements designed for deployment should not be imposed retrospectively on exploratory work that makes no deployment claim. VALUE-AI therefore calibrates expectations to the decision being contemplated: whether to continue investigation, validate in a new setting, prepare a clinical workflow, adopt a system, or continue its use after deployment. At discovery, the central questions are whether the problem is worth addressing and whether the data and labels correspond to that problem. The analysis should avoid preventable bias, and reporting and claims should remain proportionate to the study design and intended use [18,27]. Internal validation may be appropriate at this stage, but it should not be presented as evidence of transportability or clinical benefit [15,18]. A useful discovery study makes its intended population, prediction target, time horizon, and anticipated use explicit enough that later investigators can test the same proposition rather than reconstructing it from incomplete reporting.

At validation, the evidentiary burden shifts toward reproducibility and performance under meaningful variation [15,18]. Independent temporal, geographic, or institutional evaluation should be selected based on the proposed use rather than treated as a ceremonial exercise. Calibration (agreement between predicted risk and observed outcomes), clinically relevant comparators, subgroup performance, uncertainty, and failure modes become increasingly important [18]. The objective is not simply to show that discrimination (the model’s ability to distinguish those with and without the outcome) remains acceptable, but to learn where the model works, where it does not, and whether observed differences would change the next decision. Implementation-ready evaluation requires evidence about the combined sociotechnical intervention, not the algorithm in isolation. Investigators should specify who receives the output, when it appears, what action it is expected to trigger, what alternatives exist, and how override or escalation will work. Human factors, workflow fit, interoperability, and prospective evaluation become central at this stage [19,23]. Governance and accountability arrangements must also be established before technical performance can be responsibly translated into routine use [20,28]. A model can therefore be mature analytically yet remain immature as a clinical service.

After deployment, value depends on sustained observability and the capacity to respond to change. Performance, calibration, use, overrides, subgroup effects, workload, safety signals, and unintended consequences require monitoring against prespecified thresholds [20,25]. Data drift, workflow redesign, software updates, changes in prevalence, and new clinical practices may alter the model’s meaning even when its code remains unchanged [29,30]. Documenting versions, incidents, corrective actions, and retirement decisions makes postdeployment evidence cumulative rather than anecdotal [21,28]. Recent evaluations support this staged interpretation. Among 86 randomized trials of clinical AI, 54% used diagnostic yield or performance as the primary end point, 63% were single center, and 92% were conducted in 1 country [31]. In a systematic review of 572 machine-learning studies of intensive-care scoring systems, only 84 (14.7%) underwent external validation, and discrimination commonly declined in new data [8]. Across 91 real-time sepsis prediction studies, conventional discrimination and decision-oriented utility could diverge under external validation [32]. Study-design labels alone therefore do not establish that the end point, setting, or intervention supports the intended decision.

Stage calibration also makes stopping a legitimate and potentially valuable outcome. A failed external validation [18], an infeasible workflow test [19], or an adverse distributional finding [26] may justify redesign, restriction, or discontinuation before more resources are invested or more patients are exposed. Investigators should specify progression criteria in advance where possible. Research-assessment approaches that value complete reporting and registered reports can help preserve the evidence generated when criteria are not met [33,34]. The relevant question is then whether the project produced reliable learning and an accountable decision, not whether every model reached implementation. This reduces pressure to convert exploratory results into premature claims of readiness. Progression criteria should be tied to the claim and intended decision rather than to a generic hierarchy of study designs. A validation study may justify investigation in another setting without supporting procurement, while a prospective workflow study may establish feasibility without demonstrating patient benefit. Before moving forward, investigators and decision makers should state what findings would support continuation, what findings would require adaptation or additional evidence, and what findings would justify stopping. They should also identify who has authority to make that decision and which uncertainties can reasonably remain unresolved at the relevant stage. Figure 1 summarizes these maturity stages and the VALUE-AI routing logic. A maturity judgment should distinguish evidence that changes the next decision from evidence that merely adds another performance estimate. This prevents automatic progression when the most decision-relevant uncertainty concerns workflow, equity, safety, governance, or monitoring rather than discrimination alone. The same criteria should be communicated consistently in protocols, reports, review decisions, and local governance records [21,27,35].

VALUE-AI was derived from a conceptual crosswalk that aligns recurring points of value loss in the research-waste literature with the questions addressed by prediction-model reporting and risk-of-bias tools, clinical evaluation guidance, implementation frameworks, and responsible AI governance standards. This was not a formal consensus, Delphi, or systematic framework-development process. The domains were retained because each addresses a distinct decision point where evidence can lose value, and together they span question formulation through postdeployment learning. TRIPOD+AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis–Artificial Intelligence) strengthens transparent reporting [27]; PROBAST (Prediction Model Risk of Bias Assessment Tool) and PROBAST+AI assess quality, risk of bias, and applicability [18,36]; CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) and SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) extend trial reporting and protocol guidance [35,37]; and DECIDE-AI (Developmental and Exploratory Clinical Investigations of Decision Support Systems Driven by Artificial Intelligence) addresses early live clinical evaluation [19]. NASSS (Nonadoption, Abandonment, Scale-up, Spread, and Sustainability), CFIR (Consolidated Framework for Implementation Research), RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance), and implementation-outcome frameworks examine adoption, context, implementation, and sustainability [23,24,38,39]. FUTURE-AI (Fairness, Universality, Traceability, Usability, Robustness, and Explainability for Artificial Intelligence) addresses trustworthy and deployable AI across its life cycle [20]; the NIST (National Institute of Standards and Technology) AI Risk Management Framework addresses risk management [21]; WHO (World Health Organization) guidance addresses ethics and governance [28]; and FDA (Food and Drug Administration) guidance addresses predetermined change control plans for planned modifications to AI-enabled device software functions [40]. The 6 VALUE-AI domains do not claim that these underlying concerns are new. Their distinct contribution is to organize findings from existing tools around a metascience question: where is potential research value being lost, which actor or actors can modify the relevant source of loss, and what maturity-appropriate evidence or action is needed next? If users jointly apply the relevant existing tools and deliberately synthesize their findings around those questions, they are performing the function VALUE-AI is intended to support.

Table 2 operationalizes the 6 VALUE-AI domains across the research life cycle and links each domain to its primary object, appraisal questions, and observable signals. The domains are considered together, but each asks a distinct question about whether the work can create, preserve, and transmit value. A weakness becomes consequential when it obstructs the study’s purpose or renders the associated claim unreliable, unusable, or inequitable. The Valid Question and Purpose Domain asks whether the study addresses a meaningful uncertainty and whether its intended user, decision, maturity stage, and claim are coherent. It distinguishes a technically feasible task from a consequential research question. The issue is not whether every project promises immediate clinical benefit, but whether the stated purpose justifies the data, design, and inference. A high headline metric cannot compensate for a target that is poorly aligned with the proposed scientific or clinical decision.

The Appropriate Data and Population Fit Domain asks whether the dataset, measurements, proxies, outcomes, and sampling process support the intended purpose and population. It includes data provenance, leakage, missingness, reference standards, prevalence, representativeness, and uncertainty [18,27]. Narrow data are not inherently deficient, but their boundaries should be explicit. When broader use is proposed, evaluation should expose the model to the temporal, geographic, institutional, and demographic variation relevant to that use [15,18]. The Local Workflow and Intended Use Domain asks where the output would appear, to whom, at what time, and with what feasible response. Required inputs, interoperability, staffing, resources, escalation options, and competing processes may determine whether an output can influence care. This domain separates model performance from the operational pathway through which benefit is expected [19,23]. It also makes transfer assumptions visible when evidence developed in one service is considered for another.

The Usability, Safety, and Accountability Domain asks whether people can interpret, contest, override, and escalate model outputs safely and whether authority for action is clear. Human factors, training, automation bias, alert burden, privacy, consent or an appropriate alternative basis for data use, governance, and foreseeable harms are relevant [19,20]. Formal approval alone may be insufficient when affected patients and professionals lack a credible route to question assumptions, report problems, or obtain redress [28]. The Equity and External Validity Domain asks how evidence, benefits, burdens, errors, and access vary across groups, settings, and time [20,26]. Aggregate performance can conceal clinically important differences, while subgroup estimates can themselves be unstable or misleading [18]. Appraisal therefore considers representation, measurement quality, calibration, uncertainty, intersectional effects, deployment context, and whether mitigation transfers burden elsewhere. The aim is reasoned distributional and transportability analysis, not a ritual list of subgroup metrics.

The Auditability and Impact Monitoring Domain asks whether the deployed sociotechnical system and its decision sequence can be examined over time. Model and data versions, outputs, user exposure, overrides, actions, outcomes, incidents, and corrective decisions may all be relevant. Feedback loops, silent updates, drift, and dependency on proprietary infrastructure can otherwise obscure change [29,30]. Monitoring should have named owners, interpretable thresholds, response authority, and criteria for recalibration, restriction, or withdrawal [20,21,25]. Taken together, the domains provide a structured explanation of where value is secure, uncertain, or being lost. They also prevent a single favorable characteristic from dominating the assessment: a useful model may still lack legitimacy, an equitable development dataset may not ensure equitable deployment, and rigorous validation may not establish workflow utility. The resulting judgment should state which domain matters, at what maturity stage, and for which claim or decision. Interactions among domains should be recorded rather than collapsed. Poor representation may weaken appropriate data and population fit, undermine external validity, and widen inequity, but the corrective action for each consequence can differ. Likewise, an opaque proprietary update may create an auditability problem if version provenance is unavailable and an accountability problem if oversight is bypassed [21]. Naming the causal pathway helps assign responsibility and prevents broad labels such as bias or implementation failure from obscuring who can act and what maturity-appropriate evidence or action is needed next.

Table 3 makes the relationship explicit. VALUE-AI is a metascience attribution-and-routing layer, not a replacement checklist. After users apply the instruments appropriate to the study’s purpose and stage, either individually or in combination, VALUE-AI links residual deficiencies to the actor or actors able to modify them and to the next maturity-appropriate evidence or action. This synthesis matters when instruments identify different concerns because strong reporting or governance does not resolve a weak purpose, inappropriate transfer, or absent response pathway. It prevents strength in one dimension from being treated as sufficient when another consequential gap remains. The frameworks shown are illustrative rather than exhaustive and demonstrate how VALUE-AI can synthesize complementary forms of appraisal across the AI evidence life cycle.

Reporting standards and risk-of-bias tools identify important deficits but do not establish whether a research question was consequential, whether a claim aligns with the maturity stage, or whether a credible route to use exists. In 65 randomized trials published after CONSORT-AI, median concordance was 90%, yet algorithm version and intervention or code accessibility were reported in only 20% and 42%, respectively [41]. VALUE-AI adds a judgment about purpose and consequence while directing assessors to the relevant specialist instrument. An incomplete model report routes to reporting guidance, a biased validation study to a risk-of-bias tool, and a workflow barrier to implementation science; VALUE-AI records how each finding affects the capacity to inform a decision.

Consider a hypothetical model developed at a tertiary cardiac center to predict postoperative deterioration. At the discovery stage, a transparent exploratory study using local data, clearly presented as hypothesis-generating and intended to test whether a signal exists, represents neither primary research-value loss nor contextual value loss merely because it is not ready for deployment. It becomes primary research-value loss if the authors claim clinical readiness without temporal or external validation, calibration assessment, clinically appropriate decision thresholds, or adequate output-interpretation rules [18,27], or without a defined clinical response pathway and corresponding workflow evidence [19]. A retrospective validation using hypothetical alerts can be informative at this point if it is presented as a test of feasibility, timing, or potential workload rather than as evidence of clinical effect. In an independent validation of a proprietary sepsis model, discrimination was lower than the developer-reported performance; the threshold flagged 18% of hospitalizations while missing 67% of sepsis cases [42]. As alerts were hypothetical, the study supports concern about transportability and potential alert burden but does not demonstrate changed management, benefit, or harm.

Suppose the model is then proposed for a resource-variable hospital where required measurements are delayed or unavailable and specialist escalation pathways differ. If the original study did not describe these dependencies or overstated transportability, contextual value loss is implicated. If the receiving hospital deploys the model despite clearly reported limits and without local validation, training, workflow design, or escalation rules, that is primarily a deployment failure rather than a defect in the original research. A recent review of cardiac-surgery AI similarly identified external validation, workflow integration, governance, and prospective evaluation as important considerations in translating technical performance into clinical value [43]. After deployment, model version, input data, output, timing, user exposure, overrides, actions, and outcomes should be preserved in accordance with applicable law and governance requirements [20,21]. These records support provenance and may permit reconstruction of the decision sequence, but they do not establish that an AI output caused or materially influenced a clinical action. In VALUE-AI, provenance records identify the model, inputs, and outputs; reconstruction traces the observable sequence; attribution evaluates whether the output influenced action; and accountability assigns authorization and responsibility. Reconstruction fidelity is therefore an empirical property to be tested against specified records and procedures, not assumed from the existence of logs. The next step is staged evidence generation: validation and subgroup analysis [18], workflow and human-factors testing [19], and postdeployment monitoring with explicit response and withdrawal criteria [20,21,25].

VALUE-AI routes each finding by locating the relevant pathway and domain, identifying the actor with primary control, and specifying the next maturity-appropriate action. Poor external performance routes to validation; an output that arrives too late routes to workflow redesign; findings of inequitable error patterns or access barriers route to equity mitigation; and an unannounced update routes to governance review. The response may be redesign, restricted use, monitoring, or retirement, but the claim should remain within the evidence obtained. Positive controls show what stronger evidence looks like without implying that every study must reach the same end point. A prospective interventional cohort study integrated real-time diabetic retinopathy screening and safety over-read by specialists across 9 sites [44]. The population-based randomized Mammography Screening with Artificial Intelligence (MASAI) trial evaluated AI-supported mammography through interval-cancer follow-up [45]. Both connect model performance to workflow, comparators, workload, and outcomes. Neither should be generalized beyond its setting or treated as proof that deployment guarantees value. For the hypothetical deterioration model, a decision record would make this reasoning explicit. The tertiary center might continue with prospective silent-mode evaluation while withholding clinical claims; the resource-variable hospital might pause adoption until locally obtainable inputs and escalation options are tested; and a deploying institution might continue use only under defined monitoring and suspension thresholds. These are different, evidence-linked decisions about the same model, illustrating why value cannot be inferred from the model artifact alone.

For researchers, VALUE-AI can be used prospectively to align claims and evidence with maturity. Discovery studies should specify their scientific purpose and limitations [27]; validation studies should prioritize transportability, calibration, subgroup performance, and reproducibility [18]; implementation-ready evaluations should examine the full intervention within the workflow [19]; and postdeployment studies should examine monitoring methods and changing performance over time [25]. Reviewers and editors can ask which VALUE-AI domain contains the most consequential unresolved value-loss signal and direct authors to the relevant detailed standard rather than expecting a single checklist to answer every question.

In practice, this means writing the intended decision before selecting the evidence used to justify it. Protocols can draw on established guidance to specify intended use, comparators, and decision pathways [35], while VALUE-AI additionally identifies the maturity stage, the principal domains at risk, and the stopping or progression rule. Reports can then separate what was measured from what is inferred and what remains hypothetical [27,37]. This separation matters because promising results may otherwise remain difficult to reproduce, compare, or translate.

Funders may reduce avoidable duplication and premature model development by rewarding replication, external validation, comparative evaluation, and publication of informative negative findings [1,4]. VALUE-AI additionally encourages investment in monitoring infrastructure and partnerships with intended users. Where several retrospective models already exist, independent validation or prospective workflow evaluation may add more value than another development study. Funding milestones can specify the evidence bottleneck, progression or stopping rules, and requirements for reporting informative negative findings. Research-assessment principles and registered reports likewise shift incentives from novelty and positive results toward rigor, transparency, and cumulative knowledge [17,33,34].

Health systems should treat publication as the start of local appraisal, not as a deployment guarantee. Before adoption, they should assess population and data fit, infrastructure, workflow, and staffing [23,24]. They should also examine reach, implementation outcomes, and sustainability [38,39]. Equity, legal and governance requirements, monitoring capacity, and response ownership require separate appraisal [20,21,28]. Before deployment, they should designate monitoring owners; define data-quality checks, clinically meaningful thresholds, subgroup review, and incident pathways; and assign authority to modify, suspend, or retire the system [20,21,25]. A scoping review identified 39 sources on clinical-AI monitoring, but only 9 described clinically tested or implemented methods [25]. Conformal Multidimensional Prediction of Sepsis Risk (COMPOSER) provides a bounded example that links outcome evaluation to site-specific analysis, data-quality surveillance, biweekly performance review, and predefined change control [46].

For regulators and policy makers, VALUE-AI separates product evidence from evidence on its use within a service. Public summaries should identify the intended population, user, workflow, model version, principal uncertainties, and postmarket obligations [20,28,40]. Of 794 AI-enabled medical devices authorized from 2023 to 2025, 43 (5.4%) had FDA-authorized predetermined change control plans; among those 43 devices, 31 (72%) publicly reported all FDA-recommended elements [47]. Of 692 publicly available FDA summaries of safety and effectiveness data for AI-enabled devices, 3.6% reported race or ethnicity, 46.1% provided comprehensive performance results, and 9.0% described prospective postmarket studies [48]. Of 1357 FDA-authorized devices listed through December 5, 2025, 34 were linked to registered prospective trials, and only 3 evaluated patient-centered outcomes such as mortality, morbidity, or readmissions [49]. These findings concern publicly traceable evidence and do not establish that unlinked devices are ineffective or wholly unevaluated.

Patients, professionals, and developers have complementary roles in this process. Patients and communities can identify outcomes, burdens, and distributional concerns that performance summaries overlook. They require clear pathways for explanation and redress [28]. How patient and public involvement shaped the research should be reported transparently, including contributors’ roles and influence [50]. Professionals can test whether outputs align with actual decisions and report workarounds or new hazards [19,20]. Developers should preserve data and model provenance [20,21], disclose relevant limitations, and support independent evaluation. For regulated AI-enabled devices, planned modifications should be disclosed and managed through applicable change-control requirements [40]. Participation does not transfer accountability to those consulted; organizations that retain authority over design, procurement, or deployment remain responsible for acting on the evidence produced [28].

VALUE-AI is a conceptual metascience lens, not a validated reporting guideline, risk-of-bias tool, scoring system, or implementation instrument. The domains, signals, and routing questions are conceptual heuristics; they have not been empirically validated, weighted, or tested for interrater reliability. Their usefulness and interpretability require evaluation across specialties, resource settings, and AI modalities. The 4 pathways may overlap, and classifications may change as evidence and context evolve. The lens focuses primarily on prediction and decision-support systems. Applying it to generative AI, reinforcement learning, or administrative automation would require modality-specific adaptation and separate empirical evaluation addressing open-ended outputs, feedback loops, autonomy, provenance, and task-specific safety. Finally, this viewpoint is a selective conceptual synthesis rather than a systematic review of all health care AI evidence or guidance.

The next empirical task is therefore to test the lens as an appraisal process. Studies should examine interrater agreement, the reasons assessors disagree, the time and expertise required, and whether judgments change when maturity stage or intended use is specified more clearly. Construct validity can be explored by comparing VALUE-AI findings with established reporting, bias, implementation, ethics, and economic assessments while preserving the distinct question of value loss. Prospective use in funding panels, protocol review, journal assessment, and procurement could test whether the lens changes decisions or merely redescribes known concerns. Case sampling should include early exploratory work, negative validation, successful implementation, withdrawn systems, and settings with constrained infrastructure. Although VALUE-AI is not a reporting guideline, stakeholder consultation, piloting, consensus development, and evaluation of uptake offer a relevant methodological analogy for its refinement [51]. Patient and public contributors should help define acceptable evidence and consequential harm. Longitudinal evaluation is also needed because an appraisal may change as the model, workflow, population, or available alternative changes; agreement at one time point would not establish durable value. These evaluations should avoid converting VALUE-AI into an unweighted score: domain importance is context-dependent, and apparently similar deficits may have different consequences at different maturity stages. Until such testing is available, judgments should be transparent, reasoned, and accompanied by the evidence and assumptions on which they depend.

Empirical evaluation should assess not only whether assessors agree, but also whether disagreements reveal different assumptions about purpose, evidence, context, or responsibility. A useful appraisal lens may expose these underlying assumptions even when it does not yield identical classifications. Studies should therefore document the reasoning behind each judgment, the evidence that changed it, the actor assigned primary control, and the action ultimately recommended. Comparisons with usual review processes should assess whether VALUE-AI identifies neglected uncertainties, redirects resources toward more informative work, or supports earlier restriction or discontinuation when progression criteria are not met. Evaluation should also look for unintended effects, including excessive documentation, false confidence from apparently complete domain coverage, or inappropriate use of the lens to reject legitimate exploratory research. These outcomes would test whether VALUE-AI improves the quality and transparency of decisions rather than merely increasing appraisal burden.

Health care AI research can generate value at multiple stages, from exploratory methodological work to external validation, prospective evaluation, and postdeployment learning. Algorithmic research waste does not imply that every model must be deployed or that early-stage, locally focused, negative, or noncommercial research is unhelpful. It describes an avoidable loss of potential value when a study’s question, design, reporting, validation, implementation assumptions, or claims are insufficient to support the scientific, clinical, policy, or operational decision they purport to inform. VALUE-AI is a stage-specific metascience attribution-and-routing lens. It highlights 6 cross-cutting conditions for decision-useful evidence: a 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. The lens does not replace detailed reporting standards, risk-of-bias tools, clinical-evaluation guidance, implementation frameworks, or governance standards. Its contribution is to connect their findings by asking where research value is at risk, which actor or actors have practical capacity to address the source of loss, and what maturity-appropriate evidence or action should follow. The practical aim is to reduce the production of evidence that is technically impressive yet unable to guide a proportionate next decision, without creating another score or unnecessary administrative burden. Researchers, editors, funders, health systems, regulators, developers, and patient and public representatives can use this perspective to prioritize transparent questions, credible validation, locally feasible pathways, accountable oversight, and ongoing learning. VALUE-AI now requires empirical evaluation to determine whether it improves the clarity, consistency, and usefulness of decisions across research and deployment settings.