The Latest

The Promise and the ProblemArtificial intelligence was meant to transform medicine from intuition to inference — replacing the bias of experience with the precision of pattern recognition. Instead, it has delivered a paradox: astonishing predictive power and diminishing evidentiary value.Regulators hesitate, clinicians distrust, and researchers debate endlessly not whether models “work,” but whether their results can be trusted. The problem is not computation; it is verification. Prediction is mathematics; proof is governance.The Unverifiable MachineTraditional clinical research earns its authority through reproducibility. Trials are documented, data locked, protocols registered. AI models, by contrast, are dynamic — retrained continuously, tuned privately, and often developed on data that cannot legally or technically be shared.The result is epistemic opacity: outputs that cannot be audited, methods that cannot be replicated, and performance claims that evaporate under scrutiny. This is prediction without proof — intelligence unanchored from accountability.Without verifiable provenance, even a correct result is epistemically worthless.What Proof RequiresFor AI to generate clinical evidence, it must satisfy the same principles that govern experimental science:Traceability. Every data point must have a known origin and chain of custody.Reproducibility. Methods must be executable by an independent party.Auditability. Every decision — human or algorithmic — must leave a record.Integrity. The system must ensure that no one can alter inputs or outputs post hoc.Federated Circle Datasets meet these criteria by embedding governance into the data layer itself. The model is not trusted; its process is.Federation as the Proof EngineFederation transforms AI from an act of blind aggregation into a continuous audit. Each institution retains its own data, applies standardized Observational Protocols (OPs), and contributes derivative insights rather than raw information.Because each node’s contribution is independently validated, the resulting global model carries a verifiable lineage. Every prediction becomes not just an output, but an accountable statement backed by a transparent epistemic trail.Circle Datasets thus replace “black box” predictions with chain-of-custody analytics — data and model co-validating one another.The Reproducibility DividendThis design yields what centralized systems never achieved: reproducibility without centralization. An investigator in Boston can re-run a federated analysis using identical protocols applied to distinct patient populations in Berlin or Tokyo — without any data ever leaving its jurisdiction.Results that converge become credible; results that diverge reveal context, not contradiction. The proof lies not in uniformity, but in traceable variation.Medicine regains what it lost in the era of digital opacity: falsifiability.From “Working” to “Valid”An AI model that works is one that predicts correctly. An AI model that is valid is one that predicts correctly for the right reasons, under reproducible conditions, and in a manner that can be independently confirmed. Proof is what separates functionality from reliability. Federated provenance makes that distinction measurable — transforming claims of performance into evidence of integrity. Regulatory ConvergenceGlobal regulators increasingly align around this philosophy. The FDA’s Good Machine Learning Practice framework, the EU AI Act, and the OECD’s AI governance guidelines all converge on one principle: trustworthy AI must be explainable, traceable, and verifiable throughout its lifecycle. Circle Datasets operationalize that principle by making proof a byproduct of process — not an afterthought. The system itself generates the audit trail regulators require.The burden of proof moves from paperwork to architecture. The Moral of VerificationVerification is not bureaucracy; it is ethics made executable. To prove something is to take responsibility for it — to make truth a shared obligation rather than a personal claim. Prediction without proof is speculation; proof without transparency is dogma. Medicine deserves neither. The future of AI will belong to systems that transform computation into conscience — where every prediction carries the weight of evidence, and every insight can stand as testimony.

The Seduction of the Score AI in healthcare has learned to sell itself through numbers:“AUC of 0.92.”“F1-score of 0.95.”“Outperformed radiologists on test set X.” These benchmarks, while valuable for early validation, have become a substitute for proof. They convey the illusion of certainty without demonstrating reproducibility. And in medicine, a system that performs well once but not again isn’t intelligent — it’s unreliable. When Validation Fails Translation Most healthcare AI models are tested under tightly controlled conditions: curated datasets, limited variability, and well-defined endpoints. In deployment, those conditions collapse. Noise reappears, coding differs, documentation gaps widen — and benchmark success evaporates.A 2024 BMJ meta-analysis found that less than 8% of published clinical AI models maintained equivalent accuracy when re-evaluated in independent health systems.The problem isn’t statistical — it’s environmental. Benchmarks measure what’s convenient, not what’s representative.The False Proxy of Performance Benchmark-driven AI rewards optimization, not understanding. Models learn to exploit quirks in the dataset rather than underlying clinical truth — a phenomenon known as shortcut learning.A skin-cancer classifier learns lighting patterns instead of lesions. A sepsis predictor learns timestamp habits instead of physiology.These systems pass validation but fail verification. They excel at the exam, not the practice. Ground Truth Over Ground Metrics True evaluation requires ground truth — data with traceable origin, context, and longitudinal follow-up. Only then can AI performance be tied to verified patient outcomes rather than static test sets.Circle datasets provide that foundation. Because every observation in the Circle network is captured through standardized protocols and linked to verified outcomes, models can be tested against real-world, reproducible evidence.This enables continuous validation, not one-time scoring. Benchmarks evolve as care evolves, ensuring alignment between algorithmic performance and clinical reality. Economic and Regulatory Implications Benchmark blindness isn’t just a scientific flaw — it’s a financial risk. AI vendors built on inflated performance metrics face sharp valuation corrections when independent audits reveal instability.Regulators are already adapting: the FDA’s proposed framework for Adaptive AI/ML Software as a Medical Device (SaMD) emphasizes ongoing data monitoring over static validation. In the coming regulatory landscape, the benchmark will be replaced by continuous proof of performance.‍For investors, that means long-term value will accrue to platforms whose claims are verifiable in production, not just impressive in publication.Strategic Outcome Healthcare AI does not need higher scores — it needs better evidence. The next generation of evaluation will measure how well a system sustains accuracy, not how high it peaks. Circle’s architecture makes this possible by embedding reproducibility into the data itself. Benchmarks will still matter — but they will describe performance on living, verifiable data rather than static experiments. The industry must move beyond the comfort of closed validation to the discipline of continuous verification. In that shift lies the end of benchmark blindness — and the beginning of measurable trust.

We invite you to this first Circle Hour for the MOTIV™ TKA Observational Protocol. Leading the discussion will be co-investigators Doctors Andrew Wickline and John Mercuri, as well as OREF leadership. As of today, orthopedic surgeons representing approximately 1600 TKAs per year, have already joined this Circle, or indicated their intention to do so. By February 12, we expect many more will have joined. More information about this initiative can be found here.The summary agenda is:An overview of the major regulatory, reimbursement and commercial trends driving the value of real-world data inherent in the everyday practice of orthopedic medicine. (3 minutes.)OREF’s objectives in the context of the MOTIV™ initiative. (3 minutes)The clinical hypotheses underlying the Observational Protocol. (5 minutes)Sample aggregated data reports available to Circle Members. (3 minutes)Live demonstration of physician and patient user experiences. (5 minutes)Q & A. (30 minutes)‍This live-streamed event is open only to orthopedic surgeons who have pre-registered. Registration information is here:We believe that all orthopedic surgeons will find this to be a stimulating discussion, and the first step of an ongoing valuable collaboration with peers around the world.

The PremiseMetrics were born as tools of accountability. They promised to render the invisible work of science visible — to measure contribution, efficiency, and impact in a fair and standardized way. But as in all systems where measurement becomes identity, the instrument has consumed the intent. What began as an aid to judgment has become its replacement. In modern research, to be good is to be measurable, and what cannot be counted ceases to count. This transformation is not merely administrative; it is moral. It converts the pursuit of truth into the pursuit of indicators — citation counts, h-indexes, impact factors, altmetrics. Each of these, originally designed to illuminate influence, now distorts it. Metrics have become moral proxies, absolving institutions of the need for discernment. The Distortion Once metrics become ends rather than means, corruption is no longer an aberration — it is compliance. Scientists learn to optimize the visible signals of productivity: fragmented publications (“salami slicing”), reciprocal citations, and strategic authorship. The logic of the market invades the logic of inquiry. In such an environment, genuine discovery is penalized if it cannot be quickly or widely measured. The long, uncertain pursuit of fundamental understanding — the kind of work that often changes the world decades later — is devalued. Systems designed for fairness end up rewarding conformity, creating a moral inversion: the metrics of virtue replace the virtues themselves. The Consequence The ethical consequence of this metricization is alienation. Researchers begin to experience their own work not as calling, but as calibration. The joy of discovery yields to the anxiety of performance. Academic communities fracture into citation cartels and metric-driven tribes, each vying for algorithmic visibility. The result is epistemic inflation: a glut of measurable output with diminishing intellectual value. Metrics, which once promised to democratize recognition, now amplify inequality. Elite institutions, already advantaged, dominate the statistical landscape, their prestige self-perpetuating through the very indicators meant to neutralize it. The Way ForwardMetrics must return to their moral context — as instruments of stewardship, not judgment. Institutions can reclaim ethical balance by reintroducing qualitative assessment: peer evaluation that values originality and contribution over volume and reach. Funding agencies can weight integrity disclosures and replication efforts equally with publication counts. Above all, the research community must remember that measurement without meaning is not accountability; it is abdication. To measure science ethically is to measure its service to understanding, not its reflection in the mirror of itself. Selected References RegenMed (2025). Genuine Medical Research Has Lost Its Way. White Paper, 2025.Hicks, D., et al. (2015). The Leiden Manifesto for Research Metrics. Nature, 520(7548), 429–431. Biagioli, M., & Lippman, A. (Eds.). (2020). Gaming the Metrics: Misconduct and Manipulation in Academic Research. MIT Press. Muller, J. Z. (2018). The Tyranny of Metrics. Princeton University Press. Moher, D., et al. (2018). Assessing Scientists for Hiring, Promotion, and Tenure. eLife, 7:e33638. Wilsdon, J. (2015). The Metric Tide: Report of the Independent Review of the Role of Metrics in Research Assessment and Management. HEFCE. Get involved or learn more — contact us today!If you are interested in contributing to this important initiative or learning more about how you can be involved, please contact us.‍‍

The Myth of Ownership Modern data law pretends that information can be owned like property. Yet ownership implies exclusion — the right to deny access — while medical truth gains power only through sharing. This paradox defines medicine’s moral tension: knowledge demands openness, but ethics demands control.Circle resolves this not by redefining ownership, but by redefining consent as the true source of value. Every tokenized record within Circle exists only by virtue of an unbroken chain of permission.Possession may confer control, but consent confers legitimacy.Consent as the First Currency In primitive societies, trade required mutual recognition — a handshake, an oath, a gesture of trust. In digital societies, that gesture must be cryptographic: consent recorded, validated, and traceable. Circle’s architecture encodes each act of permission as metadata inseparable from the data it authorizes. This design transforms consent from formality to currency — a transactional unit of moral energy that powers the entire system. Without consent, data cannot circulate; with it, it accrues integrity. The Fragility of Forgetting Healthcare systems today treat consent as disposable: a checkbox, a signature, a line on a clipboard. Once recorded, it vanishes — lost in institutional memory, untraceable to the patient. Circle makes consent immortal. Each act of permission lives as an immutable record, visible to all authorized participants. Its persistence gives moral gravity to every data transaction — the knowledge that the individual remains present in the system that uses their truth. Consent becomes the patient’s enduring voice. The Economics of Permission In conventional data markets, value accrues to collectors, not contributors. But if consent itself is a measurable input, then the individual regains a share of the market they sustain. Each verified act of permission becomes a form of participatory equity. The more one’s data is ethically reused, the greater the return — financial, reputational, or clinical. Circle rebalances the moral economy: those who enable truth finally share in its yield. Consent as Continuous Relationship Permission is not a one-time transaction; it is an ongoing dialogue between person and system. Circle’s federated consent model honors that dialogue: patients may adjust, revoke, or refine their permissions without breaking the data’s lineage. This transforms consent from contract to relationship — dynamic, living, and mutual. Ethics evolves at the same pace as knowledge. The Moral Outcome Consent is the moral weight that prevents truth from drifting into exploitation. It ensures that transparency does not become exposure, and that participation remains dignity, not data extraction. In the Circle economy, consent is both gravity and guarantee — the invisible force that holds value in moral orbit. Without consent, data is weightless. With it, it becomes human truth with mass.