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categories: ["bodies", "foundations"]categories: ["bodies", "foundations"]tags: ["simulation", "in silico trials", "personalized medicine", "computational modelling", "medical devices", "cardiology"]tags: ["simulation", "in silico trials", "personalized medicine", "computational modelling", "medical devices", "cardiology"]summary: "Computational models of an individual person's physiology, built from that person's own data and run to predict what an intervention would do before it is attempted."summary: "Computational models of an individual person's physiology, built from that person's own data and run to predict what an intervention would do before it is attempted."updated: "2026-08-22"updated: "2026-10-29"humanEvidence: "In patients, simulation is routine only for radiotherapy dose and CT-derived coronary physiology; virtual-heart ablation planning rests on small studies. No whole-body twin exists for any person."humanEvidence: "In patients, simulation is routine only for radiotherapy dose and CT-derived coronary physiology; virtual-heart ablation planning rests on small studies. No whole-body twin exists for any person."access: "Organ-specific models are sold as regulated software and reimbursed in some health systems, notably CT-derived coronary flow analysis. Products marketed as whole-person twins are usually dashboards."access: "Organ-specific models are sold as regulated software and reimbursed in some health systems, notably CT-derived coronary flow analysis. Products marketed as whole-person twins are usually dashboards."reversibility: "reversible"reversibility: "reversible"lines 31–37 → 31–3716 unchanged lines not shown
}}`````` **Human digital twins** are computational models of an individual person's physiology, built from that person's own imaging, measurements and sensor data and run to predict what an intervention would do before it is attempted. The idea is imported from aerospace and manufacturing, where a simulation of one specific engine is kept synchronised with the engine itself and used to decide when to service it. Medicine has no whole-body equivalent and is not near one. What exists is a set of organ- and device-specific models, each narrow, each personalised in a few respects and generic in the rest.**Human digital twins** are computational models of an individual person's physiology, built from that person's own imaging, measurements and sensor data and run to predict what an intervention would do before it is attempted. The idea comes from aerospace and manufacturing, where a simulation of one specific engine is kept synchronised with the engine itself and used to decide when to service it. Medicine has no whole-body equivalent and is not near one. What exists is a set of organ- and device-specific models, each narrow, and each personalised in a few respects and generic in the rest. ```keyfacts```keyfacts[[lines 43–132 → 43–1325 unchanged lines not shown
## What the term covers## What the term covers Three different objects travel under the name, and the difference between them is most of the subject.Three different objects travel under the name, and most of the subject lies in telling them apart. The first is a mechanistic simulation whose geometry is taken from a patient's scan and whose equations come from physics and physiology: blood flow through that person's coronary tree, electrical propagation around the scar in that person's ventricle, the field a stimulation lead produces in that person's brain. The second is a statistical or machine-learned predictor fitted to population data and evaluated on one person's record, closer in kind to [[biological-age|a biological-age estimate]] than to a simulation. The third is a longitudinal data display: a record assembled from [[wearable-health-sensors]], [[continuous-glucose-monitoring]] and the laboratory, rendered next to a picture of a body.The first is a mechanistic simulation whose geometry is taken from a patient's scan and whose equations come from physics and physiology: blood flow through that person's coronary tree, electrical propagation around the scar in that person's ventricle, the field a stimulation lead produces in that person's brain. The second is a statistical or machine-learned predictor fitted to population data and evaluated on one person's record, closer in kind to [[biological-age|a biological-age estimate]] than to a simulation. The third is a longitudinal data display, in which a record assembled from [[wearable-health-sensors]], [[continuous-glucose-monitoring]] and the laboratory is rendered next to a picture of a body. Only the first can be intervened on, which is what the word twin promises. Vendors apply it to all three, and hospital software sold as a digital twin platform is usually the third. [[quantified-self|Self-tracking]] products have inherited the vocabulary as well. The distinction worth holding onto is between a model personalised with an individual's own measurements and a model carrying population parameters that merely displays that individual's numbers.Only the first can be intervened on, which is what the word twin promises. Vendors apply it to all three, and hospital software sold as a digital twin platform is usually the third. [[quantified-self|Self-tracking]] products have inherited the vocabulary as well. The useful distinction is between a model personalised with an individual's own measurements and a model that carries population parameters and merely displays that individual's numbers. > [!note] Not an emulation> [!note] Not an emulation> A digital twin is a predictive instrument, judged by whether its outputs match measurements taken from the body it models. [[whole-brain-emulation]] proposes something categorically different: a simulation detailed enough that it might itself be a bearer of the original's mental life, which is the premise [[mind-uploading]] rests on. Treating the two as points on one scale imports the whole of [[personal-identity-and-continuity]] into what is otherwise an engineering question about model error.> A digital twin is a predictive instrument, judged by whether its outputs match measurements taken from the body it models. [[whole-brain-emulation]] proposes something of a different kind: a simulation detailed enough that it might itself carry the original's mental life, which is the premise [[mind-uploading]] rests on. Placing the two on one scale drags the whole of [[personal-identity-and-continuity]] into what is otherwise an engineering question about model error. ## How one is built## How one is built A patient-specific model is assembled in four steps, and the third decides whether the object deserves the name.A patient-specific model is assembled in four steps, and the third determines whether the result deserves the name. Geometry comes first. A CT or MRI volume is segmented into a mesh of the relevant anatomy, a step now largely automated and genuinely individual: the mesh is that patient's artery, that patient's infarct. Physics comes second, as constitutive equations selected from the literature — a fluid model for blood, an ionic model for a cardiac cell, a compartmental model for a drug distributing through tissue.Geometry comes first. A CT or MRI volume is segmented into a mesh of the relevant anatomy. This step is now largely automated and genuinely individual: the mesh is that patient's artery, that patient's infarct. Physics comes second, as constitutive equations selected from the literature, such as a fluid model for blood, an ionic model for a cardiac cell, or a compartmental model for a drug distributing through tissue. Third is personalisation, meaning estimation of the model's parameters for this person. A cardiac electromechanics model carries tens to hundreds of parameters, far more than the independent measurements obtainable from a living patient. Most parameters are therefore fixed at population values while a small identifiable subset is tuned until the model reproduces something that was actually measured. The commercially deployed case makes the pattern plain: in CT-derived coronary flow analysis the arterial geometry belongs to the patient, but microvascular resistance and resting flow are assigned from scaling relationships between vessel size, myocardial mass and population physiology, because they cannot be measured non-invasively in that patient.Third is personalisation, meaning estimation of the model's parameters for this person. A cardiac electromechanics model carries tens to hundreds of parameters, far more than the independent measurements obtainable from a living patient. Most parameters are therefore fixed at population values, and a small identifiable subset is tuned until the model reproduces something that was actually measured. The commercially deployed case shows the pattern clearly. In CT-derived coronary flow analysis the arterial geometry belongs to the patient, but microvascular resistance and resting flow cannot be measured non-invasively in that patient, so they are assigned from scaling relationships between vessel size, myocardial mass and population physiology. Fourth is the counterfactual run and its uncertainty. The model is re-solved with a stent in place, a lesion ablated, a dose changed. A prediction without an uncertainty interval cannot support a decision, and quantifying that interval is harder than the simulation itself, because it requires knowing how wrong the fixed population parameters might be.Fourth is the counterfactual run and its uncertainty. The model is solved again with a stent in place, a lesion ablated, or a dose changed. A prediction without an uncertainty interval cannot support a decision, and quantifying that interval is harder than the simulation itself, because it requires knowing how wrong the fixed population parameters might be. A twin in the engineering sense is also supposed to stay synchronised, ingesting new data and re-estimating as the system changes. Almost no medical model does this. Most are built once from one scan, used for one decision, and then discarded.A twin in the engineering sense is also supposed to stay synchronised, taking in new data and re-estimating as the system changes. Almost no medical model does this. Most are built once from one scan, used for one decision, and then discarded. ## Development history## Development history The name arrived from aerospace, where pairing a physical vehicle with a virtual model continuously updated from its sensors was proposed as a way to manage airframe fatigue over a lifetime of flights.[^glaessgen2012]The name came from aerospace, where pairing a physical vehicle with a virtual model continuously updated from its sensors was proposed as a way to manage airframe fatigue over a lifetime of flights.[^glaessgen2012] ```timeline```timeline[[ { "year": "Late 1990s", "title": "The Physiome Project", "text": "The International Union of Physiological Sciences launches a coordinated effort to build quantitative, multiscale models of human physiology and the file standards needed to exchange them." }, { "year": "Late 1990s", "title": "The Physiome Project", "text": "The International Union of Physiological Sciences launches a coordinated effort to build quantitative, multiscale models of human physiology and the file standards needed to exchange them." }, { "year": "2002–2012", "title": "Digital twin named", "text": "Michael Grieves formulates the pairing of a physical artefact with a virtual counterpart updated from its data; NASA and US Air Force researchers give the idea its name and its first serious application." }, { "year": "2002-2012", "title": "Digital twin named", "text": "Michael Grieves formulates the pairing of a physical artefact with a virtual counterpart updated from its data; NASA and US Air Force researchers give the idea its name and its first serious application." }, { "year": "2007–2013", "title": "Virtual Physiological Human", "text": "European framework programmes fund a coordinated push toward integrated models of human physiology, followed by a standing institute to maintain the effort." }, { "year": "2007-2013", "title": "Virtual Physiological Human", "text": "European framework programmes fund a coordinated push toward integrated models of human physiology, followed by a standing institute to maintain the effort." }, { "year": "2008", "title": "A simulator accepted instead of animals", "text": "The FDA accepts a metabolic simulator of type 1 diabetes as a substitute for animal testing in the preclinical evaluation of closed-loop insulin controllers." }, { "year": "2008", "title": "A simulator accepted instead of animals", "text": "The FDA accepts a metabolic simulator of type 1 diabetes as a substitute for animal testing in the preclinical evaluation of closed-loop insulin controllers." }, { "year": "2014", "title": "Living Heart Project", "text": "Dassault Systèmes launches a collaborative high-fidelity model of the heart, with the FDA joining as a research partner for device evaluation." }, { "year": "2014", "title": "Living Heart Project", "text": "Dassault Systèmes launches a collaborative high-fidelity model of the heart, with the FDA joining as a research partner for device evaluation." }, { "year": "2016–2018", "title": "Regulators write the rules", "text": "FDA guidance sets out how computational modelling studies should be reported in device submissions; ASME publishes V&V 40, grading the credibility a model must show against the decision it informs." }, { "year": "2016-2018", "title": "Regulators write the rules", "text": "FDA guidance sets out how computational modelling studies should be reported in device submissions; ASME publishes V&V 40, grading the credibility a model must show against the decision it informs." }, { "year": "2018", "title": "An imaging trial run in software", "text": "FDA researchers replicate a breast-imaging comparison end to end on synthetic anatomy, with simulated detectors and algorithmic readers." }, { "year": "2018", "title": "An imaging trial run in software", "text": "FDA researchers replicate a breast-imaging comparison end to end on synthetic anatomy, with simulated detectors and algorithmic readers." }, { "year": "2022", "title": "Animal testing no longer mandatory", "text": "The FDA Modernization Act 2.0 removes the blanket statutory requirement that new drugs be tested in animals and names computer modelling among acceptable alternatives." }, { "year": "2022", "title": "Animal testing no longer mandatory", "text": "The FDA Modernization Act 2.0 removes the blanket statutory requirement that new drugs be tested in animals and names computer modelling among acceptable alternatives." }, { "year": "2023–2026", "title": "Marketing outruns evidence", "text": "Digital twin becomes standard vocabulary in hospital and device software while validated clinical uses stay confined to a handful of organ-scale models." } { "year": "2023-2026", "title": "Marketing outruns evidence", "text": "Digital twin becomes standard vocabulary in hospital and device software while validated clinical uses stay confined to a handful of organ-scale models." }]]`````` ## Where it works today## Where it works today The most widely deployed patient-specific simulation in medicine is decades old and is rarely described as a twin. Radiotherapy planning computes the dose a beam arrangement will deposit throughout that patient's own CT volume, and treatment is prescribed from the model's output.The most widely deployed patient-specific simulation in medicine is decades old and is rarely described as a twin. Radiotherapy planning computes the dose a beam arrangement will deposit throughout the patient's own CT volume, and treatment is prescribed from the model's output. In cardiology, CT-derived fractional flow reserve estimates how far pressure falls across a coronary narrowing by solving flow equations on the reconstructed artery, sparing some patients a catheter. It is cleared for marketing, reimbursed in several health systems, and was evaluated against invasively measured pressure in patients already scheduled for catheterisation.[^norgaard2014] Separately, models of arrhythmia built from contrast-enhanced MRI of a patient's scar have been used to propose ablation targets, the simulation pacing the virtual heart to find where reentrant circuits can be induced.[^prakosa2018] That work has been tested in small studies, not in a large randomised trial.In cardiology, CT-derived fractional flow reserve estimates how far pressure falls across a coronary narrowing by solving flow equations on the reconstructed artery, which spares some patients a catheter. It is cleared for marketing and reimbursed in several health systems, and it was evaluated against invasively measured pressure in patients already scheduled for catheterisation.[^norgaard2014] Separately, models of arrhythmia built from contrast-enhanced MRI of a patient's scar have been used to propose ablation targets: the simulation paces the virtual heart to find where reentrant circuits can be induced.[^prakosa2018] That work has been tested in small studies, not in a large randomised trial. In-silico testing of devices and drugs is further along than in-silico treatment of individuals. A metabolic simulator of type 1 diabetes was accepted as a substitute for animal testing in the preclinical evaluation of closed-loop insulin controllers, and it was used in developing control algorithms that went on to reach clinical use.[^kovatchev2009] FDA researchers later ran an entire imaging comparison in software, generating a population of synthetic breasts, simulating both modalities and reading the images with model observers; the result agreed with the direction of the patient trials.[^badano2018] Physiologically based pharmacokinetic models are routinely submitted to regulators to support dosing in groups never studied directly and to justify not running certain drug-interaction studies; related compartmental models are used to predict where a [[targeted-drug-delivery]] carrier ends up.In-silico testing of devices and drugs is further along than in-silico treatment of individuals. A metabolic simulator of type 1 diabetes was accepted as a substitute for animal testing in the preclinical evaluation of closed-loop insulin controllers, and it was used in developing control algorithms that went on to reach clinical use.[^kovatchev2009] FDA researchers later ran an entire imaging comparison in software. They generated a population of synthetic breasts, simulated both modalities and read the images with model observers, and the result agreed with the direction of the patient trials.[^badano2018] Physiologically based pharmacokinetic models are routinely submitted to regulators to support dosing in groups never studied directly and to justify not running certain drug-interaction studies. Related compartmental models are used to predict where a [[targeted-drug-delivery]] carrier ends up. Elsewhere, field models estimate the tissue volume a [[deep-brain-stimulation]] lead activates in a specific patient's anatomy and are used to guide programming; musculoskeletal simulation scaled to a subject's dimensions and driven by their recorded motion informs orthopaedic surgery and [[exoskeleton]] design;[^delp2007] flow models support the evaluation of circulatory devices including the [[artificial-heart]]. Patient-derived [[organoids]] fill the same niche in wet biology, with the advantage of containing the person's actual cells rather than an estimate of their parameters.Field models estimate the tissue volume a [[deep-brain-stimulation]] lead activates in a specific patient's anatomy and are used to guide programming. Musculoskeletal simulation, scaled to a subject's dimensions and driven by their recorded motion, informs orthopaedic surgery and [[exoskeleton]] design.[^delp2007] Flow models support the evaluation of circulatory devices, including the [[artificial-heart]]. In wet biology, patient-derived [[organoids]] fill the same niche, with the advantage of containing the person's actual cells rather than an estimate of their parameters. ## Regulatory standing## Regulatory standing Regulators have moved further than clinicians. The FDA published guidance in 2016 on how computational modelling studies should be reported in device submissions,[^fda2016] and recognises a consensus standard, ASME V&V 40, that scales the credibility a model must demonstrate to the risk of the decision it informs.[^asme2018] The organising idea is context of use: a model is never validated in general, only for a stated question at a stated level of influence. A simulation used to screen candidate designs and a simulation used to replace a clinical trial are, under that framework, different objects with different burdens.Regulators have moved further than clinicians. In 2016 the FDA published guidance on how computational modelling studies should be reported in device submissions,[^fda2016] and it recognises a consensus standard, ASME V&V 40, that scales the credibility a model must demonstrate to the risk of the decision it informs.[^asme2018] The organising idea is context of use. A model is never validated in general, only for a stated question at a stated level of influence, so a simulation used to screen candidate designs and one used to replace a clinical trial carry different burdens under the framework. Law has moved the same way. The FDA Modernization Act 2.0, enacted at the end of 2022, removed the blanket requirement for animal testing of new drugs; in 2025 the agency announced a plan to reduce animal testing for certain classes of biologics, again naming computational methods among the alternatives. Neither change validates any particular model. Both raise the value of building one. Judged on [[technology-readiness-level]], the regulatory pathway is more mature than the science travelling down it.Law has moved the same way. The FDA Modernization Act 2.0, enacted at the end of 2022, removed the blanket requirement for animal testing of new drugs. In 2025 the agency announced a plan to reduce animal testing for certain classes of biologics, again naming computational methods among the alternatives. Neither change validates any particular model, though both make building one more worthwhile. Judged on [[technology-readiness-level]], the regulatory pathway is more mature than the science travelling down it. ## What is actually hard## What is actually hard The binding constraint is not compute. It is that a body is not a mechanism with known parameters, and the parameters that would individualise a model are mostly not measurable in a living person.Computing power is not the binding constraint. A body is not a mechanism with known parameters, and the parameters that would individualise a model are mostly not measurable in a living person. **Identifiability.** A model with more free parameters than independent measurements has many parameter sets that fit the data equally well and disagree about the intervention. Fixing the surplus at population values does not remove the problem; it hides it, and turns a personalised model into a population model wearing one patient's anatomy.**Identifiability.** A model with more free parameters than independent measurements has many parameter sets that fit the data equally well and disagree about the intervention. Fixing the surplus at population values hides the problem without removing it, and the result is a population model wearing one patient's anatomy. **No counterfactual to check against.** If the model recommends ablating one site and the clinician ablates it, the outcome of the alternative is never observed. Validating a predictive model of an individual therefore requires randomising against ordinary care, which is expensive and rarely done. A related shortage of checkable ground truth slows the validation of [[aging-biomarkers]].**No counterfactual to check against.** If the model recommends ablating one site and the clinician ablates it, the outcome of the alternative is never observed. Validating a predictive model of an individual therefore requires randomising against ordinary care, which is expensive and rarely done. A similar shortage of checkable ground truth slows the validation of [[aging-biomarkers]]. **Scope.** A model represents what its builders chose to represent. Cardiac mechanics and drug distribution are tractable because the physics is well characterised and the geometry does much of the work. Immune response, metabolism, wound healing and the brain are not in that position, and a whole-body twin needs all of them at once, coupled. The same parameter shortage recurs one level down, where [[virtual-cell-models]] confront it at the scale of a single cell.**Scope.** A model represents what its builders chose to represent. Cardiac mechanics and drug distribution are tractable because the physics is well characterised and the geometry does much of the work. Immune response, metabolism, wound healing and the brain are not in that position, and a whole-body twin needs all of them at once, coupled. The same parameter shortage recurs one level down, where [[virtual-cell-models]] meet it at the scale of a single cell. What its partners call the largest coordinated effort to supply the missing measurements is aimed at that level rather than at people. In October 2026 [[biohub]], the US Department of Energy and the National Institutes of Health expanded the Virtual Biology Initiative, a programme to generate open, AI-ready data on how cells respond to perturbation, into a package the partners value at $1.8 billion, part of it existing federal datasets rather than new money.[^biohub2026] **Drift.** A person is not a fixed system. Anatomy remodels, drugs change physiology, disease progresses. A model validated at one moment silently expires, and there is usually no signal telling anyone when.**Drift.** A person is not a fixed system. Anatomy remodels, drugs change physiology and disease progresses, so a model validated at one moment silently expires, usually with no signal telling anyone when. > [!caution] The twin nobody has built> [!caution] The twin nobody has built> No published work has produced a model of a whole human body, personalised to an individual, that predicts responses across organ systems. Roadmaps that show one arriving within a decade or two extrapolate from organ-scale successes, and those successes are concentrated exactly where the physics is simple and the anatomy carries the prediction. Cardiology and pharmacokinetics are not a representative sample of physiology.> No published work has produced a model of a whole human body, personalised to an individual, that predicts responses across organ systems. Roadmaps showing one within a decade or two extrapolate from organ-scale successes, and those successes are concentrated where the physics is simple and the anatomy carries the prediction. Cardiology and pharmacokinetics are not a representative sample of physiology. > [!debate] Mechanism or learning> [!debate] Mechanism or learning> One camp argues that only mechanistic models can answer interventional questions, because a model fitted to observed care cannot say what would happen under care that was never given. The other argues that mechanistic models will always be underdetermined in an individual, and that large learned models trained across millions of records will predict better in practice even if they explain nothing. Hybrids are common in the literature and are not yet common in anything a regulator has cleared.> One camp argues that only mechanistic models can answer interventional questions, because a model fitted to observed care cannot say what would happen under care that was never given. The other argues that mechanistic models will always be underdetermined in an individual, and that large learned models trained across millions of records will predict better in practice even if they explain nothing. Hybrids are common in the literature but not yet in anything a regulator has cleared. ## Risks and governance## Risks and governance A model dense enough to be useful is also a compact encoding of a person's health state, and one that can be queried for conditions the subject has not been told about. Existing protections were written for records and samples, not for parameterised models derived from them; the discrimination problem is the one already mapped by [[genetic-discrimination]], with the added wrinkle that a twin can be re-run against future questions. Models of neural tissue raise the concerns collected under [[mental-privacy]].A model detailed enough to be useful is also a compact encoding of a person's health state, and it can be queried for conditions the subject has not been told about. Existing protections were written for records and samples rather than for parameterised models derived from them. The discrimination problem is the one already mapped by [[genetic-discrimination]], with the added wrinkle that a twin can be run again against future questions. Models of neural tissue raise the concerns collected under [[mental-privacy]]. Overtrust is the nearer hazard. A simulation produces a specific number with a rendered picture attached, which is a persuasive combination regardless of the model's credibility grade, and clinicians are not generally equipped to audit the assumptions behind it. Liability is unsettled: a wrong prediction from software the clinician cannot inspect sits awkwardly between device failure and clinical judgment. Cost and data availability point the same way as everything else in [[access-and-inequality]], since the models are built from imaging and continuous monitoring that many health systems do not provide.Overtrust is the nearer hazard. A simulation produces a specific number with a rendered picture attached, a persuasive combination whatever the model's credibility grade, and clinicians are not generally equipped to audit the assumptions behind it. Liability is unsettled, because a wrong prediction from software the clinician cannot inspect sits awkwardly between device failure and clinical judgment. Cost and data availability point the same way as everything else in [[access-and-inequality]], since the models are built from imaging and continuous monitoring that many health systems do not provide. ## Outlook## Outlook The direction with the most support behind it is not personal twins at all but virtual patient populations: cohorts of synthetic individuals, sampled to span real anatomical and physiological variation, used to test devices and dosing regimens across conditions no trial could enrol. That work has regulatory precedent, a credibility standard, and results. Proponents of the personal version argue it follows once enough individual data is routinely collected, and roadmaps published by research consortia describe a staged path from organ models to coupled ones. Sceptics point out that the missing ingredient is not integration but individual measurement, and that no amount of coupling fixes parameters nobody can observe.The direction with the most support behind it is virtual patient populations rather than personal twins: cohorts of synthetic individuals, sampled to span real anatomical and physiological variation, used to test devices and dosing regimens across conditions no trial could enrol. That work has regulatory precedent, a credibility standard, and results. Proponents of the personal version argue that it follows once enough individual data is routinely collected, and roadmaps published by research consortia describe a staged path from organ models to coupled ones. Sceptics reply that the missing ingredient is individual measurement rather than integration, and that no amount of coupling fixes parameters nobody can observe. Specific applications keep the idea alive. Long-duration missions are one, where a crew without a physician might carry a model of each member; [[space-medicine]] research treats this as attractive and unproven, and an astronaut's physiology changes in ways that are still being characterised, which is precisely the regime where personalisation fails. Learned components are another, with methods from [[ai-drug-discovery]] and structure prediction of the kind used in [[ai-protein-design]] proposed as a source of the parameters mechanistic models cannot measure; the proposal runs ahead of the evidence, since structure prediction returns coordinates rather than the binding affinities and conformational changes a physiological model would need. The benchmark that would settle the field's claims is a randomised comparison in which a personalised model predicts an individual's response to a therapy better than a clinician working from standard information. Trials of that design are rare, and none has been reported for a model spanning more than one organ system. Until that changes, the phrase describes an ambition and a set of organ models, not a technology anyone possesses.Specific applications keep the idea alive. One is long-duration missions, where a crew without a physician might carry a model of each member. [[space-medicine]] research treats this as attractive and unproven, and an astronaut's physiology changes in ways that are still being characterised, which is exactly the regime where personalisation fails. Another is learned components: methods from [[ai-drug-discovery]], and structure prediction of the kind used in [[ai-protein-design]], have been proposed as a source of the parameters mechanistic models cannot measure. That proposal runs ahead of the evidence, since structure prediction returns coordinates rather than the binding affinities and conformational changes a physiological model would need. The benchmark that would settle the field's claims is a randomised comparison in which a personalised model predicts an individual's response to a therapy better than a clinician working from standard information. Trials of that design are rare, and none has been reported for a model spanning more than one organ system. Until one is, the phrase names an ambition and a set of organ models rather than a technology anyone possesses. ## See also## See also lines 148–152 → 148–15315 unchanged lines not shown
[^badano2018]: `paper` Badano, A. et al. "Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial." *JAMA Network Open*, 2018. {Synthetic anatomy sampled from population statistics, not models of individual patients, and a comparison of imaging methods rather than of treatments.}[^badano2018]: `paper` Badano, A. et al. "Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial." *JAMA Network Open*, 2018. {Synthetic anatomy sampled from population statistics, not models of individual patients, and a comparison of imaging methods rather than of treatments.}[^fda2016]: `regulator` U.S. Food and Drug Administration. *Reporting of Computational Modeling Studies in Medical Device Submissions: Guidance for Industry and Food and Drug Administration Staff*, 2016.[^fda2016]: `regulator` U.S. Food and Drug Administration. *Reporting of Computational Modeling Studies in Medical Device Submissions: Guidance for Industry and Food and Drug Administration Staff*, 2016.[^asme2018]: `report` American Society of Mechanical Engineers. *V&V 40-2018: Assessing Credibility of Computational Modeling Through Verification and Validation: Application to Medical Devices*, 2018. {Credibility is graded against the decision a model informs, so the same model can be adequate for one question and inadequate for another.}[^asme2018]: `report` American Society of Mechanical Engineers. *V&V 40-2018: Assessing Credibility of Computational Modeling Through Verification and Validation: Application to Medical Devices*, 2018. {Credibility is graded against the decision a model informs, so the same model can be adequate for one question and inadequate for another.}[^biohub2026]: `statement` Biohub. "International, cross-sector collaboration commits nearly $2 billion to build foundational data for AI models to predict and treat disease." Press release, 7 October 2026. {The partners' own valuation; it combines new funding, planned agency spending, datasets from earlier federal investment and in-kind computing.}[^delp2007]: `paper` Delp, S.L. et al. "OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement." *IEEE Transactions on Biomedical Engineering*, 2007.[^delp2007]: `paper` Delp, S.L. et al. "OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement." *IEEE Transactions on Biomedical Engineering*, 2007. removed, struck through added, underlinedLine numbers count the serialised markdown of each revision, frontmatter included.
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