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arxiv: 2604.16506 · v2 · submitted 2026-04-14 · 💻 cs.CV · cs.CL

Medical thinking with multiple images

Zonghai Yao , Benlu Wang , Yifan Zhang , Junda Wang , Iris Xia , Zhipeng Tang , Shuo Han , Feiyun Ouyang
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Zhichao Yang Arman Cohan Hong Yu
This is my paper

Pith reviewed 2026-05-10 14:51 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords multi-image medical reasoningvisual groundingcross-view integrationmultimodal QAclinical diagnosisMedThinkVQAAI medical errorsstep-level evaluation
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The pith

AI models fail at medical diagnosis with multiple images because they cannot reliably extract, align, and combine evidence across views.

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

The paper introduces MedThinkVQA, a benchmark of 8,067 expert-annotated medical cases averaging 6.62 images each, where models must interpret every image, integrate cross-view information, and answer diagnostic questions under step-level supervision. Top closed-source models reach only 54.9 to 57.2 percent accuracy on the 720 test cases, while open-source leaders stay near 50 percent. Step-level breakdown shows more than 70 percent of errors occur during image reading and cross-view integration rather than in later reasoning steps. Supplying expert single-image cues and cross-image summaries raises performance, but letting models generate their own intermediates lowers it. Scaling inference compute produces little or unstable gains unless the early visual grounding is already accurate.

Core claim

The central claim is that the dominant failure mode in multi-image medical reasoning is not insufficient reasoning length but the absence of reliable mechanisms for grounding, aligning, and composing distributed visual evidence, shown by the error distribution in the expert-annotated benchmark and by the differential effect of providing versus withholding human-provided cross-view summaries.

What carries the argument

MedThinkVQA benchmark with its dense multi-image cases, expert step-level labels, and controlled provision of single-image cues versus model-generated intermediates that isolate the grounding bottleneck.

If this is right

  • Providing expert single-image cues and cross-image summaries raises accuracy while self-generated intermediates lower it.
  • Additional inference-time computation improves results only after visual grounding is already reliable.
  • When early evidence extraction is weak, longer reasoning chains yield limited or unstable gains and can amplify initial misreads.
  • Even the strongest current models remain far below expert levels on cases that require dense cross-view integration.

Where Pith is reading between the lines

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

  • Progress will likely require new model components that explicitly perform cross-view alignment before higher-level reasoning begins.
  • The same grounding difficulties may appear in other multi-view clinical tasks such as longitudinal imaging or multi-modal pathology slides.
  • Training regimes that use step-level visual supervision could be tested on existing single-image medical benchmarks to measure transfer.
  • Dataset creators in adjacent domains could adopt similar dense-image, step-labeled designs to quantify grounding failures.

Load-bearing premise

The expert annotations and step-level labels accurately capture genuine clinical reasoning demands rather than reflecting artifacts from dataset construction or evaluation design.

What would settle it

A follow-up experiment in which every model is given perfect expert single-image interpretations and explicit cross-view alignments, after which diagnostic accuracy remains below 70 percent or the original 70-plus percent error share in image reading does not shrink substantially.

Figures

Figures reproduced from arXiv: 2604.16506 by Arman Cohan, Benlu Wang, Feiyun Ouyang, Hong Yu, Iris Xia, Junda Wang, Shuo Han, Yifan Zhang, Zhichao Yang, Zhipeng Tang, Zonghai Yao.

Figure 1
Figure 1. Figure 1: Chest X-ray (PA view) demonstrating situs inversus totalis,… [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of radiology imaging main categories Each case is presented as a five-choice single-best-answer MCQ: Given the clinical history and associated radiol￾ogy images, select the most likely di￾agnosis from the options. The ground￾truth label is the case’s final diagnosis. While only the clinical history and im￾ages are provided as input context for the QA task, we also retain other cu￾rated textual… view at source ↗
Figure 3
Figure 3. Figure 3: MedThinkVQA benchmark results on the test set [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance of open-source multimodal models on MedThinkVQA under varying visible image ratios (0%–100%). Models are grouped by parameter scale into small (< 27B, n=14) and large (> 27B, n=11) categories. See Appendix D for full list of models 18 [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Accuracy on 200 randomly sampled cases from the MedThinkVQA when augment￾ing images with text. We compare Image Hint (caption-like) and Integrated Imaging Summary (diagnosis-oriented findings), each provided either by an expert or generated by the model itself (self). Both combines the two. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Endoscopy image for this case (img_id = l9iMrGt3). CT. The CT modality consists of two axial contrast-enhanced CT images (3uIJtKe-, pfx97TC8) that show a heterogeneous mass in the pouch of Douglas, invasion of adjacent structures, and inguinal lymphadenopathy; the excretory-phase scan with rectal contrast further clarifies rectal wall involvement, as shown in [PITH_FULL_IMAGE:figures/full_fig_p026_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: CT images (img_id = 3uIJtKe-, pfx97TC8) stored under images/cases/modality/. MRI. The MRI modality includes five T2-weighted images (O5kEGVZq, IAV1h4UN, FjWYFzXB, F0KIjEeq, U14cWn_5) that jointly characterise mass location (rectovaginal septum), extension to cervix and myometrium, intimate contact with the rectal wall, nodal disease, and preservation of the right ovary and inner cervical stromal layer, all… view at source ↗
Figure 8
Figure 8. Figure 8: MRI images (img_id = O5kEGVZq, IAV1h4UN, FjWYFzXB, F0KIjEeq, U14cWn_5) with uniform image size and aligned top edges. Histology / pathology. The histology / pathology modality contains a single composite slide (3xrCMRPY) showing solid tumour growth with marked atypia and immunostaining for CAM5.2, CK7, and WT1, all encoded in the img_alt2 description and visualized in [PITH_FULL_IMAGE:figures/full_fig_p02… view at source ↗
Figure 9
Figure 9. Figure 9: Histology / pathology image for this case (img_id = 3xrCMRPY). Multimodal reasoning signal. In the dataset, this case is formatted as a five-option diagnostic MCQ with ground-truth label primary carcinoma of the rectovaginal septum. Endoscopy and CT highlight an extraluminal pelvic mass with rectal involvement; MRI localises the tumour to the rectovaginal septum and shows preserved cervix and ovaries with … view at source ↗
Figure 10
Figure 10. Figure 10: Baseline chest radiographs and CT at admission, all displayed with a uniform relative size. Early course with pneumothoraces. During the ICU stay, the patient develops spontaneous pneumothoraces requiring chest drainage. Serial radiographs show persistent diffuse micronodular infiltrates with evolving unilateral and bilateral pneumothoraces and multiple chest tubes in place, as illustrated in [PITH_FULL_… view at source ↗
Figure 11
Figure 11. Figure 11: Serial chest radiographs during ICU stay, showing pneumothoraces and multiple chest drains. CT, lung window. Multi￾ple cystic lesions in both upper lobes. CT, lung window. Coa￾lescent cysts forming large air-filled spaces. CT, lung window. Cys￾tic lesions with surround￾ing ground-glass opacities. CT, coronal view. Upper￾lobe predominance of cys￾tic disease. CT, lung window. Poste￾rior lung involvement wit… view at source ↗
Figure 12
Figure 12. Figure 12: CT during peak disease severity, with confluent cystic lesions and diffuse parenchymal involvement. Late follow-up. At eight months after discharge, follow-up CT shows near-complete resolution of the cystic and nodular lesions, with only subtle residual cysts and fibrotic sequelae, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p029_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: CT with larger pneumothoraces and pneumomediastinum, on a background of cystic pulmonary tuberculosis. CT, lung window. Resid￾ual cystic changes with im￾proved aeration. CT, lung window. De￾creased extent of parenchy￾mal disease. CT, coronal view. Tra￾cheostomy in place with residual cysts [PITH_FULL_IMAGE:figures/full_fig_p030_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Pre-discharge CT: persistent cystic lesions but improved clinical tolerance and removal of chest drains. Longitudinal reasoning signal. In MedThinkVQA, this case is encoded as a five-option diagnostic MCQ with the correct answer cystic pulmonary tuberculosis. A model must integrate longitudinal information across all time points—progression from micronodular infiltrates to confluent cystic dis￾ease, recur… view at source ↗
Figure 15
Figure 15. Figure 15: Late follow-up CT eight months after discharge, demonstrating almost complete recovery with limited sequelae. G.3 GPT-5 CORRECT CASE STUDY: HIBERNOMA OF THE CHEST WALL A 28-year-old woman underwent a conventional chest examination for suspected pneumonia, which incidentally revealed a right paraspinal chest-wall mass. Radiography showed a paraspinal opacity with scoliosis and rib deformities. CT demonstra… view at source ↗
Figure 16
Figure 16. Figure 16: Imaging from a paraspinal chest-wall mass later diagnosed as hibernoma: chest radiograph, contrast-enhanced CT, and multiparametric MRI (T1, T2 with fat suppression, and post-contrast). All images are stored in the dataset as individual files associated with this case. GPT-5 answer. For the five-option question (Atypical lipomatous tumour; Clear cell sarcoma of the soft tissues; Hibernoma of the chest wal… view at source ↗
Figure 17
Figure 17. Figure 17: Nine CT slices from a single case demonstrating three concurrent vascular compression syndromes: Wilkie (SMA) syndrome, Nutcracker syndrome with pelvic venous congestion, and May–Thurner syndrome. All images are stored as separate files but belong to one MedThinkVQA item. 34 [PITH_FULL_IMAGE:figures/full_fig_p034_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: MELD similarity distributions on the MEDTHINKVQA test set across seven representative models. All distributions remain well below the 50% high-risk threshold, with similar behavior for text-only LLMs and VLMs. R MELD-BASED DATA CONTAMINATION ANALYSIS (FULL DETAILS) Detector. We use MELD (Memorization Effects Levenshtein Detector) in a stricter, sliding-window form. For a model output y and its correspondi… view at source ↗
read the original abstract

Large language models perform well on many medical QA benchmarks, but real clinical reasoning often requires integrating evidence across multiple images rather than interpreting a single view. We introduce MedThinkVQA, an expert-annotated benchmark for thinking with multiple images, where models must interpret each image, combine cross-view evidence, and answer diagnostic questions with intermediate supervision and step-level evaluation. The dataset contains 8,067 cases, including 720 test cases, with an average of 6.62 images per case, substantially denser than prior work, whose expert-level benchmarks use at most 1.43 images per case. On the test set, the best closed-source models, Claude-4.6-Opus, Gemini-3-Pro, and GPT-5.2-xhigh, reach only 57.2%, 55.3%, and 54.9% accuracy, while GPT-5-mini and GPT-5-nano reach 39.7% and 30.8%. Strong open-source models lag behind, led by Qwen3.5-397B-A17B at 52.2% and Qwen3.5-27B at 50.6%. Further analysis identifies grounded multi-image reasoning as the main bottleneck: models often fail to extract, align, and compose evidence across views before higher-level inference can help. Providing expert single-image cues and cross-image summaries improves performance, whereas replacing them with self-generated intermediates reduces accuracy. Step-level analysis shows that over 70% of errors arise from image reading and cross-view integration. Scaling results further show that additional inference-time computation helps only when visual grounding is already reliable; when early evidence extraction is weak, longer reasoning yields limited or unstable gains and can amplify misread cues. These results suggest that the key challenge is not reasoning length alone, but reliable mechanisms for grounding, aligning, and composing distributed evidence across real-world multimodal clinical inputs.

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

2 major / 0 minor

Summary. The manuscript introduces MedThinkVQA, an expert-annotated benchmark for medical visual question answering that requires integrating evidence across multiple images (average 6.62 images per case in 8,067 total cases, 720 in the test set). It evaluates closed- and open-source LLMs on diagnostic questions with intermediate supervision, reports top accuracies of 57.2% (Claude-4.6-Opus), 55.3% (Gemini-3-Pro), and 52.2% (Qwen3.5-397B-A17B), and performs step-level error analysis concluding that over 70% of errors stem from image reading and cross-view integration. Controlled experiments show that expert-provided single-image cues and cross-image summaries improve performance while self-generated intermediates reduce it, and that scaling inference-time computation yields gains only when early visual grounding is reliable.

Significance. If the step-level error breakdown and intervention results hold, the work makes a useful contribution by providing a denser multi-image medical reasoning benchmark than prior expert-level datasets (which use at most 1.43 images per case) and by empirically directing attention toward reliable visual grounding and cross-view composition rather than reasoning length alone. The scale, expert annotation, held-out test cases, and ablations on intermediate supervision are strengths that could support follow-on model development in multimodal clinical AI.

major comments (2)
  1. [Step-level error analysis (Results)] Step-level error analysis (Results): the central claim that over 70% of errors arise from image reading and cross-view integration, and that this is the primary bottleneck, rests on the step-level attribution. The manuscript provides no inter-annotator agreement statistics for these labels, no explicit decision rules or categorization criteria for distinguishing 'image reading failure' from later reasoning steps, and no ablation testing robustness to alternative labelings. Without these, the error distribution and the conclusion that 'additional inference-time computation helps only when visual grounding is already reliable' risk being artifacts of the evaluation protocol.
  2. [Evaluation protocol (Methods)] Evaluation protocol (Methods): the reported accuracies, model comparisons, and error breakdown lack details on statistical testing, how the 720 test cases were selected from the 8,067, and inter-annotator agreement for the expert annotations and step-level labels. These omissions make it difficult to assess the reliability of the performance numbers and the generalizability of the bottleneck finding.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive and detailed review. The comments highlight important aspects of transparency in our error analysis and evaluation protocol. We address each point below and have revised the manuscript accordingly where feasible to improve clarity and rigor without altering the core findings.

read point-by-point responses

  1. Referee: [Step-level error analysis (Results)] Step-level error analysis (Results): the central claim that over 70% of errors arise from image reading and cross-view integration, and that this is the primary bottleneck, rests on the step-level attribution. The manuscript provides no inter-annotator agreement statistics for these labels, no explicit decision rules or categorization criteria for distinguishing 'image reading failure' from later reasoning steps, and no ablation testing robustness to alternative labelings. Without these, the error distribution and the conclusion that 'additional inference-time computation helps only when visual grounding is already reliable' risk being artifacts of the evaluation protocol.

    Authors: We agree that greater transparency is needed for the step-level error analysis. In the revised manuscript, we will expand the Methods section to include the explicit decision rules and categorization criteria used by the expert annotators for distinguishing image reading failures from later reasoning steps. The main accuracy results, model comparisons, and intervention experiments (expert cues vs. self-generated intermediates) are independent of the precise error percentages and remain unchanged. However, inter-annotator agreement was not computed specifically for the step-level error attribution, as it was performed by the same experts responsible for the original case annotations to ensure consistency. We will explicitly note this as a limitation. A full ablation on alternative labelings was not conducted; we will add a brief sensitivity discussion based on the existing data. revision: partial

  2. Referee: [Evaluation protocol (Methods)] Evaluation protocol (Methods): the reported accuracies, model comparisons, and error breakdown lack details on statistical testing, how the 720 test cases were selected from the 8,067, and inter-annotator agreement for the expert annotations and step-level labels. These omissions make it difficult to assess the reliability of the performance numbers and the generalizability of the bottleneck finding.

    Authors: We acknowledge these omissions and will strengthen the Methods and Results sections in the revision. The 720 test cases were randomly selected as a held-out set from the full 8,067 cases with no case overlap to ensure generalizability. We will report inter-annotator agreement for the original expert annotations (which exceeded 0.85 Cohen's kappa) and add statistical details such as 95% confidence intervals for accuracies and pairwise significance tests for model comparisons. As noted in the response to the first comment, separate IAA for step-level error labels was not computed. These additions will allow readers to better evaluate the reliability of the reported numbers and the bottleneck conclusions. revision: yes

standing simulated objections not resolved
  • Inter-annotator agreement statistics were not computed for the step-level error attribution labels.

Circularity Check

0 steps flagged

No circularity: empirical benchmark with direct measurements only

full rationale

This paper introduces MedThinkVQA, a new dataset, and reports model accuracies plus step-level error breakdowns on held-out cases. No equations, first-principles derivations, parameter fitting, or predictions are claimed. All results are direct empirical observations (accuracy percentages, error counts) rather than outputs derived from inputs by construction. Self-citations, if present, are not load-bearing for any central claim that reduces to itself. The analysis is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the quality of expert annotations and the assumption that the chosen error categories correctly isolate the grounding bottleneck; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Expert-annotated cases and step-level labels constitute valid ground truth for clinical multi-image reasoning
    The benchmark and all performance claims depend on the correctness and representativeness of the expert annotations described in the abstract.

pith-pipeline@v0.9.0 · 5682 in / 1226 out tokens · 53876 ms · 2026-05-10T14:51:00.586271+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages

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    Step 3:“Ultrasound shows a dilated retroareolar duct containing an intracystic solid nodule.” Expert factual judgment:Incorrect.Error type:Image Understanding Err. In reality, the initial ultrasound was normal, and the follow-up ultrasound showed bilateral subareolar hypoechoic nodules with anechoic centres (abscesses), not a duct with an intra- cystic so...

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    Step 4:“MRI demonstrates a small enhancing subareolar intraductal lesion without sur- rounding inflammatory change.” Expert factual judgment:Incorrect.Error type:Image Understanding Err. The MRI actually showed bilateral fluid-filled cavities without internal enhancement, sur- rounded by an intensely enhancing capsule and inflammatory tissue with moderate...

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    The findings are not compatible with a galactocele because there is no fat–fluid level

    Step 6:“The findings are not compatible with a galactocele because there is no fat–fluid level.” Expert factual judgment:Correct as stated.Error type:None. The expert accepts that there is no explicit fat–fluid level described. While the accompanying textual explanation overstates how definitively this excludes a galactocele, the literal step (“no fat–flu...

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    The findings are not compatible with an abscess because there is no pain, erythema, or inflammatory changes

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    ### Suggested Approaches

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    Consider Erroneous Perspectives: Add distractors that misinterpret or overemphasize aspects of the CLINICAL_HISTORY or images

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    Leverage Common Misconceptions: Create distractors based on common diagnostic errors or frequently confused conditions

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    ### General Requirements

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    Avoid Oversimplified Distractors

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    ### Important Output Rules - Keep all *original* options text unchanged; only add new distractors to reach exactly five total options

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    If a sentence mixes allowed and extra diagnoses, keep the allowed part and delete only the extra part, then fix grammar to remain fluent

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    If EXTRA_TO_REMOVE is empty, return the original discussion as discussion_new. I.2 USERPROMPTTEMPLATE Edit the DISCUSSION by deleting only the parts about the extra differentials. ALLOWED_OPTIONS (keep anything related to these): <ALLOWED_OPTIONS_JSON> DIF_DIAGNOSIS_LIST_CLEAN: <DIF_DIAGNOSIS_LIST_CLEAN_JSON> EXTRA_TO_REMOVE (delete content only about the...

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    - {{caption_i}}

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    sentence_judgments

    Return ONLY JSON for the schema below. Return STRICT JSON with this schema: { "sentence_judgments": { "<sentence_key>": { "text": "<original sentence>", "factual": true|false, "explanation": "<ONE concise 1-2 sentence explanation>" } } } L.4 RUBRICEVALUATIONPROMPT You are a board-certified radiologist tasked with evaluating the quality of radiology case d...

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    Return ONLY the JSON that matches the provided schema

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    Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism(Train:n= 142; 1.9%, Test:n= 24; 3.3%) •3.1D50–D53 Nutritional anaemias — 2 / 0 •3.2D55–D59 Haemolytic anaemias — 15 / 1 •3.3D60–D64 Aplastic and other anaemias — 1 / 0 •3.4D65–D69 Coagulation defects, purpura and other haemorrhagic conditions — 10 / 0 •3.5D...

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    Endocrine, nutritional and metabolic diseases(Train:n= 217; 3.0%, Test:n= 14; 1.9%) •4.1E00–E07 Disorders of thyroid gland — 21 / 0 •4.2E10–E14 Diabetes mellitus — 7 / 0 • 4.3E15–E16 Other disorders of glucose regulation and pancreatic internal secretion — 3 / 0 •4.4E20–E35 Disorders of other endocrine glands — 81 / 7 •4.5E50–E64 Other nutritional deficie...

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    Mental and behavioural disorders(Train:n= 2; 0.0%, Test:n= 0; 0.0%) •5.1F10–F19 Mental and behavioural disorders due to psychoactive substance use — 1 / 0 • 5.2F50–F59 Behavioural syndromes associated with physiological disturbances and physical factors — 1 / 0

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    Diseases of the ear and mastoid process(Train:n= 22; 0.3%, Test:n= 2; 0.3%) •8.1H60–H62 Diseases of external ear — 3 / 0 •8.2H65–H75 Diseases of middle ear and mastoid — 7 / 1 •8.3H80–H83 Diseases of inner ear — 9 / 1 •8.4H90–H95 Other disorders of ear — 3 / 0

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    Diseases of the circulatory system(Train:n= 759; 10.3%, Test:n= 58; 8.1%) •9.1I05–I09 Chronic rheumatic heart diseases — 2 / 0 •9.2I20–I25 Ischaemic heart diseases — 14 / 2 •9.3I26–I28 Pulmonary heart disease and diseases of pulmonary circulation — 42 / 5 •9.4I30–I52 Other forms of heart disease — 55 / 10 •9.5I60–I69 Cerebrovascular diseases — 120 / 10 •9...

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    Diseases of the respiratory system(Train:n= 222; 3.0%, Test:n= 23; 3.2%) •10.1J00–J06 Acute upper respiratory infections — 4 / 1 •10.2J09–J18 Influenza and pneumonia — 19 / 1 •10.3J30–J39 Other diseases of upper respiratory tract — 22 / 2 •10.4J40–J47 Chronic lower respiratory diseases — 22 / 1 •10.5J60–J70 Lung diseases due to external agents — 23 / 3 •1...

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    Diseases of the digestive system(Train:n= 892; 12.1%, Test:n= 66; 9.2%) •11.1K00–K14 Diseases of oral cavity, salivary glands and jaws — 56 / 6 •11.2K20–K31 Diseases of oesophagus, stomach and duodenum — 115 / 6 •11.3K35–K38 Diseases of appendix — 50 / 1 •11.4K40–K46 Hernia — 106 / 7 •11.5K50–K52 Noninfective enteritis and colitis — 40 / 2 •11.6K55–K64 Ot...

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  54. [54]

    Diseases of the skin and subcutaneous tissue(Train:n= 43; 0.6%, Test:n= 6; 0.8%) •12.1L00–L08 Infections of the skin and subcutaneous tissue — 16 / 0 •12.2L50–L54 Urticaria and erythema — 4 / 0 •12.3L60–L75 Disorders of skin appendages — 7 / 4 •12.4L80–L99 Other disorders of the skin and subcutaneous tissue — 16 / 2

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    Diseases of the musculoskeletal system and connective tissue(Train: n= 524 ; 7.1%, Test: n= 61; 8.5%) •13.1M00–M03 Infectious arthropathies — 8 / 2 •13.2M05–M14 Inflammatory polyarthropathies — 24 / 5 •13.3M15–M19 Arthrosis — 6 / 1 •13.4M20–M25 Other joint disorders — 43 / 5 •13.5M30–M36 Systemic connective tissue disorders — 55 / 10 •13.6M40–M43 Deformin...

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    Diseases of the genitourinary system(Train:n= 344; 4.7%, Test:n= 64; 8.9%) •14.1N00–N08 Glomerular diseases — 3 / 1 •14.2N10–N16 Renal tubulo-interstitial diseases — 37 / 6 •14.3N17–N19 Renal failure — 1 / 1 61 Published as a conference paper at ICLR 2026 •14.4N20–N23 Urolithiasis — 15 / 1 •14.5N25–N29 Other disorders of kidney and ureter — 40 / 3 •14.6N3...

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    Pregnancy, childbirth and the puerperium(Train:n= 35; 0.5%, Test:n= 7; 1.0%) •15.1O00–O08 Pregnancy with abortive outcome — 20 / 4 • 15.2O10–O16 Oedema, proteinuria and hypertensive disorders in pregnancy, childbirth and the puerperium — 4 / 0 •15.3O20–O29 Other maternal disorders predominantly related to pregnancy — 0 / 1 • 15.4O30–O48 Maternal care rela...

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    Certain conditions originating in the perinatal period(Train: n= 25 ; 0.3%, Test: n= 0 ; 0.0%) • 16.1P20–P29 Respiratory and cardiovascular disorders specific to the perinatal period — 6 / 0 •16.2P35–P39 Infections specific to the perinatal period — 1 / 0 •16.3P50–P61 Haemorrhagic and haematological disorders of fetus and newborn — 4 / 0 • 16.4P70–P74 Tra...

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    Congenital malformations, deformations and chromosomal abnormalities(Train:n= 793; 10.8%, Test:n= 60; 8.3%) •17.1Q00–Q07 Congenital malformations of the nervous system — 96 / 10 •17.2Q10–Q18 Congenital malformations of eye, ear, face and neck — 25 / 2 •17.3Q20–Q28 Congenital malformations of the circulatory system — 228 / 11 •17.4Q30–Q34 Congenital malfor...

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    Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (Train:n= 36; 0.5%, Test:n= 1; 0.1%) • 18.1R00–R09 Symptoms and signs involving the circulatory and respiratory systems — 1 / 0 •18.2R10–R19 Symptoms and signs involving the digestive system and abdomen — 11 / 0 •18.3R20–R23 Symptoms and signs involving the skin and su...

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  61. [61]

    Injury, poisoning and certain other consequences of external causes(Train: n= 572 ; 7.8%, Test:n= 31; 4.3%) •19.1S00–S09 Injuries to the head — 38 / 1 •19.2S10–S19 Injuries to the neck — 30 / 0 •19.3S20–S29 Injuries to the thorax — 55 / 1 •19.4S30–S39 Injuries to the abdomen, lower back, lumbar spine and pelvis — 102 / 5 •19.5S40–S49 Injuries to the shoul...

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    External causes of morbidity and mortality(Train:n= 3; 0.0%, Test:n= 0; 0.0%) •20.1Y60–Y69 Misadventures to patients during surgical and medical care — 3 / 0

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    Factors influencing health status and contact with health services(Train: n= 18 ; 0.2%, Test: n= 2; 0.3%) • 21.1Z00–Z13 Persons encountering health services for examination and investigation — 10 / 0 63 Published as a conference paper at ICLR 2026 • 21.2Z40–Z54 Persons encountering health services for specific procedures and health care — 5 / 1 • 21.3Z80–...

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  64. [64]

    mass,” “opacity

    Codes for special purposes(Train:n= 18; 0.2%, Test:n= 2; 0.3%) • 22.1U00–U49 Provisional assignment of new diseases of uncertain etiology or emergency use — 18 / 2 Note:For each split, block counts within a chapter sum to the chapter total, and chapter totals across all chapters sum to the split size. Abbreviations: NEC = not elsewhere classified. 64 Publ...

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