pith. sign in

arxiv: 2604.04878 · v1 · submitted 2026-04-06 · 💻 cs.AI · cs.PF

Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices

Alexis Burgon , Berkman Sahiner , Nicholas A Petrick , Gene Pennello , Ravi K Samala This is my paper

Pith reviewed 2026-05-10 20:06 UTC · model grok-4.3

classification 💻 cs.AI cs.PF
keywords adaptive AImedical devicesperformance evaluationlearningretentionpopulation shiftsregulatory assessmentmodel updates
0
0 comments X

The pith

Three complementary measurements separate model adaptation effects from dataset shifts in adaptive AI medical devices.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces learning, potential, and retention as three measurements to evaluate how adaptive AI models for medical devices change performance. Learning tracks improvement on current data, potential captures shifts driven by the dataset itself, and retention checks preservation of prior knowledge across updates. This separation clarifies whether gains or losses stem from the model's changes or from evolving patient populations. Simulations of gradual and rapid population shifts illustrate stable learning under slow transitions versus plasticity-stability trade-offs under fast ones. The framework supports regulatory assessment of safety and effectiveness over repeated modifications.

Core claim

Performance changes in adaptive AI-enabled medical devices can be disentangled by computing learning as model improvement on current data, potential as dataset-driven performance shifts, and retention as knowledge preservation across modification steps, with case studies on simulated population shifts showing that gradual transitions support stable learning and retention while rapid shifts expose trade-offs between plasticity and stability.

What carries the argument

The three complementary measurements of learning (model improvement on current data), potential (dataset-driven performance shifts), and retention (knowledge preservation across modification steps).

Load-bearing premise

The three measurements can be computed to reliably separate model adaptation effects from dataset effects, and simulated population shifts are representative of real clinical data dynamics.

What would settle it

If applying the three measurements to real sequential clinical datasets fails to distinguish adaptation-driven changes from dataset-driven ones, the approach would not hold.

Figures

Figures reproduced from arXiv: 2604.04878 by Alexis Burgon, Berkman Sahiner, Gene Pennello, Nicholas A Petrick, Ravi K Samala.

Figure 1
Figure 1. Figure 1: A simple adaptation timeline for an adaptive AI system with an initial implementation (timepoint 0) and five modification steps (timepoints 1-5), showing the transition between timepoints 2 (current) and 3 (new). disease classification from chest X-rays) remains the same across all modification steps, in contrast to some continual learning models that learn sequences of different tasks [10]. This training … view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of learning between Scenario A & Scenario B using a toy example. Despite showing the same performance at each comparable modification step (0 & 1), as indicated by their identical line plots, the two scenarios exhibit different learning. The performance change in Scenario A is due to a shift in dataset difficulty, whereas the performance change in Scenario B is due to an improvement in model kno… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of potential between Scenario A & Scenario B using a toy example. Despite showing the same performance at each comparable modification step (0 & 1), as indicated by their identical line plots, the two scenarios exhibit different po￾tential. Scenario A demonstrates greater potential because the modification step 1 dataset presented a greater challenge to the modification step 0 model than was obs… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison ofretention between Scenario A & Scenario B using a toy example. Despite showing the same performance at comparable modification step (0 & 1), as indicated by their identical line plots, the two scenarios exhibit different retention. Scenario A shows lower retention because the modification step 1 model demonstrates greater performance degradation on the modification step 0 evaluation dataset. 2… view at source ↗
Figure 5
Figure 5. Figure 5: (a) Population distribution of training, validation, and testing data, (b) learning & potential and (c) retention & perfor￾mance for a model trained and evaluated on a dataset gradually transitioning from one population to another. Vertical markers indicate 95% confidence intervals from models across 25 repeti￾tions. of this decrease: the model’s learning never reaches its po￾tential at any of the modifica… view at source ↗
Figure 6
Figure 6. Figure 6: (a) Population distribution of training, validation, and testing data, (b) learning & potential and (c) retention & perfor￾mance for a model with limited plasticity trained and evaluated on a dataset gradually transitioning from one population to an￾other. Vertical markers indicate 95% confidence intervals from models across 25 repetitions. potential (Figure 7b). The model’s potential (and thuslearn￾ing) s… view at source ↗
read the original abstract

This work addresses challenges in evaluating adaptive artificial intelligence (AI) models for medical devices, where iterative updates to both models and evaluation datasets complicate performance assessment. We introduce a novel approach with three complementary measurements: learning (model improvement on current data), potential (dataset-driven performance shifts), and retention (knowledge preservation across modification steps), to disentangle performance changes caused by model adaptations versus dynamic environments. Case studies using simulated population shifts demonstrate the approach's utility: gradual transitions enable stable learning and retention, while rapid shifts reveal trade-offs between plasticity and stability. These measurements provide practical insights for regulatory science, enabling rigorous assessment of the safety and effectiveness of adaptive AI systems over sequential modifications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The paper proposes a framework for evaluating adaptive AI-enabled medical devices that face challenges from iterative model updates and changing evaluation datasets. It introduces three complementary measurements—learning (model improvement on current data), potential (dataset-driven performance shifts), and retention (knowledge preservation across modification steps)—to disentangle effects of model adaptations from those of dynamic environments. These are illustrated via simulated case studies on population shifts, where gradual transitions support stable learning and retention while rapid shifts expose plasticity-stability trade-offs. The measurements are presented as providing practical insights for regulatory science on the safety and effectiveness of such systems over sequential modifications.

Significance. If the three measurements can be shown to reliably separate model adaptation effects from dataset effects without post-hoc tuning, the framework would address a genuine gap in assessing adaptive AI for medical devices, where standard static evaluation falls short. The simulations serve as an appropriate proof-of-concept for illustrating the concepts and the plasticity-stability tension, and the paper earns credit for framing a structured, multi-faceted evaluative approach rather than a single scalar metric. Its utility for regulatory contexts would increase with explicit computation rules and tests on real clinical data streams.

major comments (1)
  1. [Case studies / simulated population shifts] The central claim that the three measurements disentangle model adaptation from environmental effects rests on the case-study simulations, yet the manuscript provides no explicit formulas, pseudocode, or quantitative separation metrics (e.g., correlation or ablation results) showing that learning, potential, and retention are independent of each other or of arbitrary simulation parameters. This is load-bearing for the utility demonstration.
minor comments (3)
  1. Clarify the exact operational definitions and any hyperparameters used to compute each of the three measurements; without these, reproducibility of the reported trends in gradual vs. rapid shifts is limited.
  2. The abstract and demonstration sections would benefit from a brief comparison table or figure contrasting the new measurements against conventional accuracy or AUC tracking across update steps.
  3. [Introduction] Add citations to related work on continual learning evaluation and adaptive clinical AI to better situate the novelty of the learning-potential-retention triad.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and the recommendation for minor revision. We address the major comment below and will incorporate clarifications and additions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Case studies / simulated population shifts] The central claim that the three measurements disentangle model adaptation from environmental effects rests on the case-study simulations, yet the manuscript provides no explicit formulas, pseudocode, or quantitative separation metrics (e.g., correlation or ablation results) showing that learning, potential, and retention are independent of each other or of arbitrary simulation parameters. This is load-bearing for the utility demonstration.

    Authors: We agree that the current presentation relies primarily on illustrative simulations to demonstrate the disentangling capability. The three measurements are formally defined in Section 3 of the manuscript, with learning quantified as the change in model performance on the contemporaneous dataset post-adaptation, potential as the performance shift attributable solely to dataset evolution (holding the model fixed), and retention as the change in performance on prior datasets after subsequent adaptations. The case studies in Section 4 then apply these to simulated population shifts, showing stable learning and retention under gradual transitions versus plasticity-stability trade-offs under rapid shifts. To address the concern directly, we will add explicit mathematical formulas for each metric and pseudocode for the simulation procedure in the revised manuscript. We will also include a new quantitative analysis (e.g., pairwise correlations and sensitivity to simulation parameters such as shift rate and magnitude) to provide evidence of relative independence. These additions will be placed in Section 4 and an expanded appendix. revision: yes

Circularity Check

0 steps flagged

No significant circularity; conceptual framework with simulation demonstration

full rationale

The paper introduces a measurement framework (learning, potential, retention) as a conceptual proposal for evaluating adaptive AI medical devices. These quantities are defined directly and illustrated via simulated population shifts as proof-of-concept, without any mathematical derivations, fitted parameters, equations, or predictions that reduce to inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems, and no ansatzes or renamings of known results occur. The central claim remains independent of its demonstration data, satisfying the criteria for a self-contained non-circular proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that simulated population shifts can stand in for real clinical data dynamics and that the three metrics can be operationalized without hidden dependencies on model-specific details.

axioms (1)
  • domain assumption Simulated population shifts accurately represent real-world data dynamics in medical AI devices
    Case studies rely on this to demonstrate the approach's utility for gradual and rapid transitions.

pith-pipeline@v0.9.0 · 5421 in / 1166 out tokens · 65716 ms · 2026-05-10T20:06:03.053362+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

  1. [1]

    Food and Drug Administration (FDA)

    U.S. Food and Drug Administration (FDA). Pro- posed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)- based software as a medical device (SaMD) - dis- cussion paper and request for feedback, 2021. www.regulations.gov/docket/FDA-2019-N-1185

    work page 2021

  2. [2]

    Food and Drug Administration (FDA)

    U.S. Food and Drug Administration (FDA). Dig- ital health and artificial intelligence glossary – educational resource, 2024. www.fda.gov/science- research/artificial-intelligence-and-medical- products/fda-digital-health-and-artificial- intelligence-glossary-educational-resource

    work page 2024

  3. [3]

    Calibration driftinregressionandmachinelearningmodelsfor acutekidneyinjury.JournaloftheAmericanMedical InformaticsAssociation,24(6):1052–1061,2017

    Sharon E Davis, Thomas A Lasko, Guanhua Chen, EdwardDSiew,andMichaelEMatheny. Calibration driftinregressionandmachinelearningmodelsfor acutekidneyinjury.JournaloftheAmericanMedical InformaticsAssociation,24(6):1052–1061,2017

    work page 2017

  4. [4]

    De- cayingrelevanceofclinicaldatatowardsfuturedeci- sionsindata-driveninpatientclinicalordersets.In- ternationalJournalofMedicalInformatics,102:71–79, 2017

    JonathanHChen,MuthuramanAlagappan,MaryK Goldstein,StevenMAsch,andRussBAltman. De- cayingrelevanceofclinicaldatatowardsfuturedeci- sionsindata-driveninpatientclinicalordersets.In- ternationalJournalofMedicalInformatics,102:71–79, 2017

    work page 2017

  5. [5]

    Featurerobustnessinnon-stationaryhealthrecords: caveatstodeployablemodelperformanceincommon clinical machine learning tasks

    Bret Nestor, Matthew BA McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C Hughes,AnnaGoldenberg,andMarzyehGhassemi. Featurerobustnessinnon-stationaryhealthrecords: caveatstodeployablemodelperformanceincommon clinical machine learning tasks. InMachineLearn- ingforHealthcareConference,pages381–405.PMLR, 2019

    work page 2019

  6. [6]

    Datadriftinmedicalmachinelearn- ing: implicationsandpotentialremedies.TheBritish JournalofRadiology,96(1150):20220878,2023

    BerkmanSahiner,WeijieChen,RaviKSamala,and NicholasPetrick. Datadriftinmedicalmachinelearn- ing: implicationsandpotentialremedies.TheBritish JournalofRadiology,96(1150):20220878,2023

    work page 2023

  7. [7]

    Acontinuallearn- ingsurvey: Defyingforgettinginclassificationtasks

    Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh,andTinneTuytelaars. Acontinuallearn- ingsurvey: Defyingforgettinginclassificationtasks. IEEE transactions on pattern analysis and machine intelligence,44(7):3366–3385,2021

    work page 2021

  8. [8]

    Algorithmsonregulatorylockdown inmedicine.Science,366(6470):1202–1204,2019

    Boris Babic, Sara Gerke, Theodoros Evgeniou, and IGlennCohen. Algorithmsonregulatorylockdown inmedicine.Science,366(6470):1202–1204,2019

    work page 2019

  9. [9]

    Machine learning-enabledmedicaldevices: Keytermsanddef- initions,2022

    InternationalMedicalDeviceRegulatorsForum(IM- DRF),IMDRF/AIMDWG/N67(Edition1). Machine learning-enabledmedicaldevices: Keytermsanddef- initions,2022. www.imdrf.org/documents/machine- learning-enabled-medical-devices-key-terms-and- definitions

    work page 2022

  10. [10]

    Gradient episodicmemoryforcontinuallearning.Advancesin NeuralInformationProcessingSystems,30,2017

    DavidLopez-PazandMarc’AurelioRanzato. Gradient episodicmemoryforcontinuallearning.Advancesin NeuralInformationProcessingSystems,30,2017

    work page 2017

  11. [11]

    Thestability-plasticitydilemma: Investigating the continuum from catastrophic forgetting to age- limitedlearningeffects.FrontiersinPsychology,4:504, 2013

    Martial Mermillod, Aurélia Bugaiska, and Patrick Bonin. Thestability-plasticitydilemma: Investigating the continuum from catastrophic forgetting to age- limitedlearningeffects.FrontiersinPsychology,4:504, 2013

    work page 2013

  12. [12]

    Methodsforim- provedunderstandingofevolvingaimodellearning and knowledge retention across sequential modifi- cationsteps

    AlexisBurgon,BerkmanSahiner,NicholasAPetrick, GenePennello,andRaviKSamala. Methodsforim- provedunderstandingofevolvingaimodellearning and knowledge retention across sequential modifi- cationsteps. InRSNAProgramBook,T6-SSPH08-6,

    work page

  13. [13]

    www.rsna.org/-/media/files/rsna/annual- meeting/future-and-past-meetings/rsna-2023- meeting-program.pdf

    work page 2023

  14. [14]

    Alexis Burgon, Berkman Sahiner, Nicholas Petrick, Gene Pennello, Kenny H Cha, and Ravi K Samala. Decision region analysis for generalizability of arti- ficial intelligence models: estimating model gener- alizabilityinthecaseofcross-reactivityandpopula- tionshift.JournalofMedicalImaging,11(1):014501– 014501,2024

    work page 2024

  15. [15]

    Bias amplificationtofacilitatethesystematicevaluationof biasmitigationmethods.IEEEJournalofBiomedical andHealthInformatics,29(2):1444–1454,2024

    AlexisBurgon,YuhangZhang,NicholasPetrick,Berk- manSahiner,KennyHCha,andRaviKSamala. Bias amplificationtofacilitatethesystematicevaluationof biasmitigationmethods.IEEEJournalofBiomedical andHealthInformatics,29(2):1444–1454,2024. 7–7

    work page 2024