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arxiv: 2604.15378 · v1 · submitted 2026-04-15 · 📡 eess.IV

Portable Medical Imaging in Modern Healthcare: Fundamentals, AI-Based Taxonomy, Image Quality, and Open Challenges

Yassine Habchi , Hamza Kheddar , Muhammad Ali Qureshi , Mohamed Seghier , Azeddine Beghdadi This is my paper

Pith reviewed 2026-05-10 11:27 UTC · model grok-4.3

classification 📡 eess.IV
keywords portable medical imagingimage qualityAI taxonomypoint-of-care diagnosismedical image enhancementclinical usabilityreview
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The pith

Portable medical imaging reaches reliable clinical use when AI methods target quality degradation from motion and hardware limits.

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

This review establishes a quality-centered synthesis of portable medical imaging advances, showing how modalities like portable CT, MRI, ultrasound, and capsule endoscopy can extend point-of-care diagnosis to emergency, rural, and resource-limited settings. It organizes AI approaches into a taxonomy spanning machine learning, deep learning, transfer learning, and transformers, linking them to tasks such as enhancement, reconstruction, quality assessment, detection, and classification. The work contrasts this with prior modality-driven surveys by stressing the ties between image quality, AI robustness, and clinical usability. A sympathetic reader would care because addressing these portable-specific distortions could make timely diagnosis feasible where fixed infrastructure is unavailable.

Core claim

The central claim is that a systematic, quality-centered review of portable medical imaging reveals the direct relationship between image-quality degradation caused by motion artifacts, environmental interference, and hardware limitations and the resulting AI robustness and clinical usability, while providing a taxonomy of AI methods, analyzing devices and datasets, and outlining gaps toward reliable and interpretable systems.

What carries the argument

The AI-based taxonomy of PMI methods, which classifies machine learning, deep learning, transfer learning, and Transformer approaches by their roles in countering modality-specific distortions to support enhancement, reconstruction, quality assessment, and diagnostic tasks.

If this is right

  • AI models for PMI must explicitly preserve image quality under unstable conditions to maintain performance in detection and classification.
  • Evaluation metrics and public datasets should incorporate portable-specific distortions rather than relying on standard fixed-scanner benchmarks.
  • Future PMI systems require greater interpretability to support clinical deployment alongside robustness gains.
  • Research should prioritize handling of motion artifacts and hardware limits to close identified gaps in real-world usability.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This taxonomy could guide development of cross-modality AI tools that adapt to varying portable hardware without retraining from scratch.
  • Field trials measuring diagnostic accuracy before and after quality-aware AI interventions would test the review's practical value.
  • Linking PMI quality metrics to telemedicine platforms might extend reliable remote diagnosis in underserved regions.

Load-bearing premise

The literature chosen for the taxonomy and gap analysis is comprehensive and representative of all relevant PMI research.

What would settle it

A new survey of PMI literature that finds no measurable link between explicit quality-focused AI design and improved clinical usability in portable conditions would falsify the central emphasis.

Figures

Figures reproduced from arXiv: 2604.15378 by Azeddine Beghdadi, Hamza Kheddar, Mohamed Seghier, Muhammad Ali Qureshi, Yassine Habchi.

Figure 1
Figure 1. Figure 1: An example block diagram of PMI fundamentals. extracting insight from low image-quality. For example, intelligent ML algorithms enhance image resolution, correct noise and lighting artifacts, and automatically identify anatomical structures or abnormalities [15]. artificial intelligence (AI)-assisted image reconstruction can also restore diagnostic details lost due to environmental or hardware constraints,… view at source ↗
Figure 2
Figure 2. Figure 2: An overview of how the review is structured and the criteria used for selecting studies. (a) Roadmap presenting the main sections covered in this review. (b) PRISMA flow diagram for the selection of articles. This review is designed for a diverse audience, including researchers, clinicians, and technologists in the fields of medical imaging, AI, and healthcare. It specifically targets those focused on impr… view at source ↗
Figure 3
Figure 3. Figure 3: Bibliographic statistics illustrating the distribution of research papers across PMRI, PCT, and PUS. point-of-care use [37, 38]. As indicated in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Block diagram of PMI working principle for PMRI, PCT, PUS, and WCE. they served as key enablers for portable, cost-effective, and energy-efficient imaging systems, particularly in resource-limited environments [51, 52]. For example, [53] introduced a 1024-channel single FPGA-based beamformer for portable 3D US systems, addressing the computational constraints of conventional 3D US platforms. Traditional sy… view at source ↗
Figure 5
Figure 5. Figure 5: Example of mobile low-dose CT and DL for lung cancer screening [80]. Figure illustrates the process of lung cancer screening using a mobile low-dose CT (LDCT) device combined with a DL model. Participants are first recruited and undergo mobile CT scanning, along with completing demographic questionnaires. The CT images are then processed by the DL model, which operates in two stages: the detection model id… view at source ↗
Figure 6
Figure 6. Figure 6: Portable ultralow-field MRI with DL reconstructs 3D brain images [81]. Illustrates the data acquisition and DL reconstruction pipeline for accelerating ultra-low-field (ULF) isotropic 3D brain MRI at 0.055 T. (a) Shows the acquisition process, where a single excitation (NEX) is used along with 2D partial Fourier (PF) sampling at a fraction of 0.7 in two phase-encoding directions. This reduces the scan time… view at source ↗
Figure 7
Figure 7. Figure 7: Example of 3D US imaging pipeline for carotid atherosclerosis using U-Net segmentation and regularized Fast-Dot Projection algorithm [82]. Figure illustrates the pipeline of the proposed technique for diagnosing carotid atherosclerosis using a portable freehand 3D US imaging system. The top row showcases the data acquisition process, which involves collecting 2D transverse images along with positional info… view at source ↗
Figure 8
Figure 8. Figure 8: Example of classification of mobile-based oral cancer images using the ViT and the swin transformer [92]. (a) Shows the processing image patches through Swin Transformer blocks, reducing token numbers progressively. (b) Illustrates the Swin Transformer block, highlighting the multi-head self-attention mechanism, which processes patch tokens through transformations and passes them through a Multi-layer Perc… view at source ↗
Figure 9
Figure 9. Figure 9: Illustration of different factors affecting image quality in medical imaging. (a) Noisy brain MR image showing the degradation of fine structural details due to random noise, which can obscure subtle pathological changes [108]; (b) Metal artifact caused by dental fillings or implants, producing bright streaks that interfere with adjacent tissue visualization [113]; (c) Respiration blur resulting from patie… view at source ↗
read the original abstract

Portable medical imaging (PMI) has emerged as an important solution for point-of-care diagnosis in emergency, rural, and resource-limited settings where conventional imaging infrastructure is not readily available. Modalities such as portable computed tomography, portable magnetic resonance imaging, portable ultrasound, and wireless capsule endoscopy improve access to timely diagnosis, but they remain highly vulnerable to image-quality degradation caused by motion artifacts, environmental interference, hardware limitations, and unstable acquisition conditions. This review provides a systematic and quality-centered synthesis of recent advances in PMI. It introduces a taxonomy of AI-based PMI methods spanning machine learning, deep learning, transfer learning, and Transformer-based approaches, and examines their roles in image enhancement, reconstruction, quality assessment, detection, and classification. The review also analyzes PMI devices, sensing pipelines, modality-specific distortions, evaluation metrics, and publicly available datasets. In contrast to existing surveys that are mainly modality-driven or application-focused, this work emphasizes the relationship between image quality, AI robustness, and clinical usability in portable settings. Finally, it identifies current research gaps and outlines future directions toward reliable, interpretable, and clinically deployable PMI systems.

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 / 2 minor

Summary. The manuscript offers a systematic review of portable medical imaging (PMI) technologies, including modalities like portable CT, MRI, ultrasound, and capsule endoscopy. It proposes an AI-based taxonomy covering machine learning, deep learning, transfer learning, and Transformer approaches for tasks such as image enhancement, reconstruction, quality assessment, detection, and classification. The paper analyzes device fundamentals, sensing pipelines, modality-specific distortions, evaluation metrics, and public datasets, while highlighting the interplay between image quality, AI robustness, and clinical usability, and outlining research gaps and future directions.

Significance. If the synthesis is representative, this work could significantly contribute to the field by offering a quality-centered perspective that bridges technical AI advancements with clinical deployment challenges in resource-limited settings. It highlights actionable gaps that could guide the development of more robust and interpretable portable imaging solutions.

major comments (1)
  1. [Methods or Literature Search Section] The abstract describes the work as a 'systematic' synthesis, but the manuscript lacks a detailed description of the literature selection process, including databases searched, search terms, time period covered, and inclusion/exclusion criteria. Without this, the taxonomy and gap analysis cannot be verified as comprehensive or free from selection bias, which is central to supporting the claims about unique emphasis and open challenges.
minor comments (2)
  1. [Taxonomy Section] The taxonomy of AI-based PMI methods is presented, but it is unclear how the categorization avoids overlap or ensures coverage of all relevant approaches; for example, the distinction between deep learning and Transformer-based methods may require more explicit justification given that Transformers are a subset of deep learning.
  2. [Figures] Ensure that all figures, such as the taxonomy diagram, have high resolution and clear legends for readability in the final publication.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The feedback highlights an important aspect of systematic reviews, and we have revised the manuscript accordingly to enhance transparency.

read point-by-point responses

  1. Referee: The abstract describes the work as a 'systematic' synthesis, but the manuscript lacks a detailed description of the literature selection process, including databases searched, search terms, time period covered, and inclusion/exclusion criteria. Without this, the taxonomy and gap analysis cannot be verified as comprehensive or free from selection bias, which is central to supporting the claims about unique emphasis and open challenges.

    Authors: We agree that a systematic review requires explicit documentation of the literature search methodology to support claims of comprehensiveness and to allow assessment of potential bias. In the revised manuscript, we have added a dedicated 'Literature Search Strategy' subsection (now Section 2) that details: (1) the databases queried (PubMed, IEEE Xplore, Scopus, Web of Science, and arXiv), (2) the Boolean search strings and keywords employed, (3) the covered time period (2015–2024), and (4) the inclusion/exclusion criteria (peer-reviewed English-language studies on AI methods for portable imaging modalities, excluding non-empirical works and duplicate records). This addition directly addresses the concern and enables verification of the taxonomy and gap analysis. revision: yes

Circularity Check

0 steps flagged

No circularity: standard literature synthesis without self-referential derivations

full rationale

This is a survey paper whose core output is a taxonomy and gap analysis drawn from reviewed external literature. No equations, fitted parameters, or predictions are generated from the paper's own inputs; the taxonomy classifies existing AI methods rather than deriving them from self-defined quantities. Claims about image-quality/AI-robustness links are interpretive summaries, not reductions by construction. Literature selection methodology, while open to critique on completeness, does not create circularity because the paper does not treat its own selection criteria as a derived result. No self-citation load-bearing, ansatz smuggling, or renaming of known results occurs in the derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a review paper the central contribution is synthesis and gap identification rather than new derivations. No free parameters, mathematical axioms, or invented entities are introduced or required by the abstract.

pith-pipeline@v0.9.0 · 5520 in / 1191 out tokens · 50350 ms · 2026-05-10T11:27:55.507169+00:00 · methodology

discussion (0)

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Reference graph

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