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REVIEW 4 major objections 7 minor 106 references

Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers

T0 review · 4 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A review argues that embedding physics knowledge into medical imaging AI algorithms makes them more trustworthy and robust, especially when data are scarce.

desk verdict A useful but uneven pedagogical review of medical imaging physics for AI researchers; the central claim about physics improving trustworthiness is plausible but overgeneralized, and a few real physics errors need fixing before it can serve as the trusted handbook it aims to be. read the letter →

arxiv 2505.02843 v1 pith:JZZNRBMZ submitted 2025-04-28 eess.IV cs.AIcs.CVphysics.med-ph

Miriam Cobo , David Corral Fontecha , Wilson Silva , Lara Lloret Iglesias This is my paper · ORCID
classification eess.IVcs.AIcs.CVphysics.med-ph
keywords physics-informedmachinelearningmedicalimagingtrustworthyAIgenerativemodelsimagereconstructionphysicslimiteddataexplainability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper sets out to establish that AI systems for medical imaging become more trustworthy—more robust, more explainable, and safer for clinical use—when they are built on an accurate understanding of the physics that produces each image. It argues that the gap between AI research and clinical translation is partly due to developers lacking this physical background, and it supplies a modality-by-modality tutorial of that physics, from visible-light and X-ray imaging through MRI, nuclear medicine, and ultrasound. It then connects each modality's acquisition physics to the current AI toolkit, especially generative models and reconstruction algorithms, and reviews physics-informed machine learning, where physical laws are inserted as data constraints, loss terms, or architectural inductive biases. The payoff the paper claims is that these physics-based constraints make AI models behave more reliably precisely in the settings where medical AI struggles: scarce labeled data, low-dose or accelerated acquisitions, and out-of-distribution inputs. A sympathetic reader would take the paper's central thesis as a handbook-level argument that imaging physics is not optional background for medical AI but a load-bearing component of trustworthiness.

What carries the argument

The machinery that carries the argument is the mapping of every clinical imaging modality onto its governing physical process, combined with a taxonomy of physics-informed machine learning. For each modality the paper identifies the physical effect that forms the image—absorption and scattering for X-ray, Hounsfield-unit attenuation for CT, T1/T2 relaxation and k-space sampling for MRI, gamma emission and coincidence detection for PET/SPECT, echo propagation for ultrasound—and then classifies ways of injecting that physics into a learning algorithm: observational bias (the data themselves reflect physics), learning bias (physics-based penalty terms in the loss), and inductive bias (physics hard-wired into the architecture). This two-part structure is what lets the review move from 'physics describes the image' to 'physics can regularize, constrain, and explain the model'.

What would settle it

Run a controlled head-to-head on a public low-dose CT or under-sampled MRI benchmark: train the same architecture with and without a physics-informed loss, forward model, or acquisition-matched noise schedule, and test both on an out-of-distribution set from a different scanner or dose level. If the physics-enhanced model is not consistently more robust or more accurate, the paper's central claim is not supported.

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Extended reading notes

Core claim

The paper's central claim is that the trustworthiness of AI in medical imaging is substantially determined by how faithfully the model respects the physical processes that create the image. Radiographs and CT images are records of X-ray attenuation and scattering; MRI images are reconstructed from spatial-frequency data in k-space whose sampling pattern is governed by gradient physics; PET images are formed from coincidence detection of annihilation photons; ultrasound images are built from reflected acoustic pulses. The review argues that AI developers who ignore these processes can be misled by artifacts, overtrust synthetic images, and produce models that fail on out-of-distribution clinical data. The discovery it offers, as a review synthesis, is that the same physics that constrains image formation can be turned into algorithmic constraints—through synthetic data that mimics acquisition, loss functions that penalize physics violations, or architectures that encode physical invariances—and that this is a concrete route to robustness and explainability in limited-data regimes. In short, the paper claims that medical imaging physics is not a static background fact but an exploitable resource for making AI models more reliable.

Load-bearing premise

The argument rests on the assumption that the physics tutorial the paper provides is accurate enough to serve as a trusted reference for AI developers; if a core physical account is wrong, as with its uncited explanation of X-ray electron production by ionizing nitrogen and oxygen molecules, the handbook misleads its intended readers and the claim that physics knowledge improves AI loses its foundation.

Editorial extensions

If this is right

  • Generative models for medical images can be made physically plausible by embedding acquisition physics, such as a noise schedule that mimics ultrasound echo attenuation, so synthetic images are less likely to mislead clinicians.
  • Image reconstruction algorithms that include a physical forward model can recover high-quality images from lower-dose or under-sampled data, supporting reductions in radiation exposure and scan time.
  • Physics-based constraints act as a regularizer, which should reduce overfitting and improve generalization when labeled medical data are scarce.
  • Models with explicit physical structure are more explainable, because failures and predictions can be traced back to a physical quantity such as attenuation or relaxation time.
  • Physics-informed methods offer a route to robustness against out-of-distribution acquisitions, since the model already knows how scanner settings and patient anatomy affect the image.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves implicit that the same logic implies physics should become a standard for evaluating synthetic medical images: a generative model that violates known acquisition physics could be rejected before clinician review.
  • A testable extension is to use modality-specific physics as a zero-shot or few-shot prior, so a model trained on one scanner could be adapted to another scanner with almost no labeled data.
  • If the physics tutorial is meant to be a handbook, it invites a companion set of worked examples that convert each modality's equations into code-level constraints, turning the review's thesis into directly actionable recipes.
  • The argument also suggests an educational consequence: medical imaging AI curricula should treat imaging physics not as a prerequisite nicety but as a core component of trustworthy-model design.
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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. This manuscript is a narrative review intended as a pedagogical handbook for AI researchers entering medical imaging. It surveys the physical principles behind each clinical imaging modality (visible-light, X-ray, CT, mammography, fluoroscopy, MRI, SPECT, PET, ultrasound, and combined systems), discusses image-quality challenges and artifacts, and then introduces physics-informed machine learning (PIML), grouping methods into observational, learning, and inductive biases. The paper's central claim, stated in the abstract and repeated in Section 4, is that integrating physics knowledge into AI algorithms enhances their trustworthiness and robustness, particularly when training data are scarce.

Significance. If the central claim were established, this review would be a useful orientation for AI researchers and could serve as a bridge between the medical imaging physics community and the machine learning community. The manuscript has genuine strengths: a modality-by-modality organization that is easy to navigate; concrete examples of physics-informed reconstruction and generation across modalities; an explicit taxonomy of PIML approaches; and a clearly written list of challenges and limitations in Section 3.4. However, the abstract's causal claim is presented as a general result while the supporting evidence is a curated set of examples rather than a systematic comparison; moreover, at least one substantive physics error appears in the tutorial portion. Both issues are fixable in revision, but they need to be addressed before the review can serve as a reliable reference.

major comments (4)
  1. [Abstract and Section 4] The manuscript asserts that integrating physics knowledge into AI algorithms 'enhances their trustworthiness and robustness in medical imaging, especially in scenarios with limited data availability.' This claim is stated as a general result, but the review does not supply controlled comparisons or a quantitative synthesis: the cited examples, such as [29, 30, 70, 73, 86, 101], demonstrate feasibility on selected tasks, not superiority over purely data-driven baselines on out-of-distribution or limited-data metrics. Section 3.4 itself concedes that excessive constraints can cause over-regularization and that explainability and uncertainty remain limitations. Please soften the abstract and conclusion to 'can enhance' or add a systematic evidence table with baseline comparisons, so that the message matches the evidence presented.
  2. [Section 2.2] The description of X-ray production is physically incorrect and uncited: the text states that 'electrons are produced due to the ionization of nitrogen and oxygen atoms, which attract positive ions to the cathode, and therefore inject electrons that are accelerated to the anode.' The standard account is that diagnostic X-ray tubes generate electrons via thermionic emission from a heated filament (cathode), and these electrons are then accelerated toward the anode. Because the paper's stated purpose is to provide authoritative physical foundations for AI researchers, this error is load-bearing and must be corrected and referenced, ideally to the manuscript's own primary source [18].
  3. [Section 2.2.2] The statement that CT voxel values 'ranging from -1000 to 1000' represent the Hounsfield Unit scale is an oversimplification that could mislead AI researchers who normalize or interpret CT data. Air is approximately -1000 HU and water 0 HU, but dense cortical bone and metal can exceed +1000 HU, commonly reaching values around +3000 HU depending on the scanner, reconstruction kernel, and object composition. Please replace this with a more precise statement about the conventional calibration points and the practical range of CT numbers.
  4. [Section 3.4] The challenges paragraph explicitly states that 'incorporating excessive constraints during training can lead to over-fitting and over-regularization' and that explainability, uncertainty, and incomplete physics knowledge remain limitations. These caveats are not carried into the abstract or Section 4, where the benefit of physics integration is stated without qualification. Please connect Section 3.4 explicitly to the central claim so that the review's overall message is internally consistent.
minor comments (7)
  1. [Section 2.5] The paragraph beginning 'Optimizing US image quality involves selecting appropriate settings for the specific anatomical area being examined' appears twice with nearly identical wording later in the same section; please remove the duplicate.
  2. [Figures 1 and 2] 'Frecuency' should be 'Frequency' in the axis labels.
  3. [Section 2.3] 'the higher spacial frequencies are in the periphery' should read 'the higher spatial frequencies are in the periphery.'
  4. [Section 2.2.4] 'prosprocedural imaging evaluation' should be 'postprocedural imaging evaluation.'
  5. [Section 3.2] 'Sef-adaptive PINNs' should be 'Self-adaptive PINNs.'
  6. [Section 2.2.3] 'AI has holds significant potential' should be 'AI holds significant potential.'
  7. [Section 2.2.2] When naming the Hounsfield Unit, consider crediting Sir Godfrey Hounsfield explicitly, as the current phrasing 'after one of the main developers of this technology' is unnecessarily vague.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a narrative review whose claims are external-evidence syntheses, and its only self-citation is not load-bearing.

full rationale

This manuscript is a review, not a derivation: it contains no fitted parameters, no equations whose outputs are constructed from inputs, and no predictive claim computed from the paper's own prior work. The central assertion, that physics knowledge integrated into AI algorithms enhances trustworthiness and robustness in medical imaging, is a synthesis of external literature (e.g., the PIML taxonomy attributed to [9,48,10], and modality-specific examples [29,30,70,73,86,101]); it is not defined into existence by the paper. The only author self-citations are uses of the authors' prior standardization paper [25] in the Introduction and in Section 2.7.2 to support statements about clinical translation gaps and file-format interoperability; these citations are not used to justify the physics-informed-AI central claim, so they are not load-bearing. No uniqueness theorem or physics ansatz is imported from the authors' own previous work. The physics tutorial inaccuracies noted by the reviewer (Section 2.2's electron-production mechanism and the CT Hounsfield range statement) are factual/correctness concerns for a handbook, but they do not make the argument circular. Section 3.4 even acknowledges conditions under which physics constraints hurt performance, which shows the conclusion is not assumed by construction; it may be under-supported, but that is an evidence-quality question, not a circularity. Accordingly, no circular step is found and the score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities: the paper is a survey. It relies on domain assumptions: physics-informed methods improve trustworthiness (supported only by selected examples), the physics tutorial is accurate (contradicted by at least one claim), and AI developers lack physics background (asserted, not measured).

assumptions (3)
  • domain assumption Physics-based constraints improve the trustworthiness and robustness of AI in medical imaging.
    Stated in the abstract and throughout; supported only by selected examples, not a systematic meta-analysis.
  • domain assumption The physics descriptions in the review are accurate representations of standard medical imaging physics.
    The paper relies on Bushberg et al. [18] and other references, but contains at least one nonstandard claim about X-ray tube electron production.
  • domain assumption AI researchers entering medical imaging lack sufficient physics background, making this handbook necessary.
    Motivational premise in the introduction; not empirically demonstrated.

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Cite this review

Pith. "Pith review of Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers." pith.science (2026). https://pith.science/paper/JZZNRBMZ

@misc{pith2026250502843,
  author       = {Pith},
  title        = {Pith review of: Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JZZNRBMZ}},
  note         = {Machine review of arXiv:2505.02843}
}
read the original abstract

Artificial intelligence in medical imaging has seen unprecedented growth in the last years, due to rapid advances in deep learning and computing resources. Applications cover the full range of existing medical imaging modalities, with unique characteristics driven by the physics of each technique. Yet, artificial intelligence professionals entering the field, and even experienced developers, often lack a comprehensive understanding of the physical principles underlying medical image acquisition, which hinders their ability to fully leverage its potential. The integration of physics knowledge into artificial intelligence algorithms enhances their trustworthiness and robustness in medical imaging, especially in scenarios with limited data availability. In this work, we review the fundamentals of physics in medical images and their impact on the latest advances in artificial intelligence, particularly, in generative models and reconstruction algorithms. Finally, we explore the integration of physics knowledge into physics-inspired machine learning models, which leverage physics-based constraints to enhance the learning of medical imaging features.

Figures

Figures reproduced from arXiv: 2505.02843 by the authors.

Figure 1
Figure 1. Medical imaging modalities in the electromagnetic radiation spectrum. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Ultrasound imaging in the acoustic spectrum. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.