Paper Citation Record · LEDGER

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification

As of 10 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2505.18695.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.18695 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:29:58.964383Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T09:39:26.587719Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T09:43:49.296326Z

Reference resolution

65 of 65 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8135aaae-2985-4c94-85d1-ee886c7e0a06 · outbound

This paper cites Fda announces completion of first ai- assisted scientific review pilot and aggressive agency-wide ai rollout,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Fda announces completion of first ai- assisted scientific review pilot and aggressive agency-wide ai rollout,

Reference 1

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Observation 06c82c28-4005-465c-af33-651c43f5bba8 · outbound

This paper cites Openai and the fda are talking about ai for drug evaluation,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Openai and the fda are talking about ai for drug evaluation,

Reference 2

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Observation 62530501-71af-4989-9911-45d3df4a458c · outbound

This paper cites Mantus and D.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Mantus and D

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:07.339348Z

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Observation 62ec28f3-1ee4-4c3b-907d-e5f3bc61d391 · outbound

This paper cites Current regulatory requirements for registration of medicines, compilation and submission of dossier in australian therapeutic goods administration,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Current regulatory requirements for registration of medicines, compilation and submission of dossier in australian therapeutic goods administration,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:07.097791Z

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Observation a08e6f2a-4a48-4c8f-96f3-2238199db4a7 · outbound

This paper cites Regulatory frameworks for ai-enabled medical device software in china: Com- parative analysis and review of implications for global manufacturer,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Regulatory frameworks for ai-enabled medical device software in china: Com- parative analysis and review of implications for global manufacturer,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:06.856980Z

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Observation 0aac9a39-0903-470a-8cbb-e694ef730bea · outbound

This paper cites Uncovering Regulatory Affairs Complexity in Medical Products: A Qualitative Assessment Utilizing Open Coding and Natural Language Processing (NLP).

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Uncovering Regulatory Affairs Complexity in Medical Products: A Qualitative Assessment Utilizing Open Coding and Natural Language Processing (NLP)

Reference 6

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Observation 9782a0ba-9090-438b-ab2c-0f609c1a7128 · outbound

This paper cites Fda-cleared artificial intelligence and machine learning-based medical devices and their 510 (k) predicate networks,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Fda-cleared artificial intelligence and machine learning-based medical devices and their 510 (k) predicate networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:06.633094Z

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Observation ac0da6cb-f6c8-4dda-9d2d-04622449cc08 · outbound

This paper cites Medical devices: definition, classification, and regulatory implications,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Medical devices: definition, classification, and regulatory implications,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:06.410903Z

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Observation bda9194c-21c3-4d95-b97b-261a00839201 · outbound

This paper cites A comprehensive strategy to overhaul fda authority for misleading food labels,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification A comprehensive strategy to overhaul fda authority for misleading food labels,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:06.119760Z

Source-reported events for the cited work

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Observation 514bfcd8-7a08-4370-91cc-9aaaacff1f59 · outbound

This paper cites Position statement: the need for eu legislation to require disclosure and labelling of the composition of medical devices,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Position statement: the need for eu legislation to require disclosure and labelling of the composition of medical devices,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:05.925634Z

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Observation 7f5f246a-b5e5-4d72-86e4-59bb1b762281 · outbound

This paper cites The complexity of medical device regulations has increased, as assessed through data-driven techniques,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification The complexity of medical device regulations has increased, as assessed through data-driven techniques,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:05.659660Z

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Observation 0287fbf7-436b-47ef-af8e-1536ffa1c204 · outbound

This paper cites More than red tape: exploring complexity in medical device regulatory affairs,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification More than red tape: exploring complexity in medical device regulatory affairs,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:05.429106Z

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Observation 26504224-dcd2-4b6e-b792-6804d2b86631 · outbound

This paper cites Role of artificial intelligence applications in real-life clinical practice: systematic review,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Role of artificial intelligence applications in real-life clinical practice: systematic review,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:05.274595Z

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Observation 9c4adac6-12ab-48ea-8c06-1ec71301e69a · outbound

This paper cites Ai applications to medical images: From machine learning to deep learning,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Ai applications to medical images: From machine learning to deep learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:05.230112Z

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Observation 98cb32ea-e277-4cde-ab4d-efefbf788009 · outbound

This paper cites A comparison of rule-based and machine learning methods for medical information extraction,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification A comparison of rule-based and machine learning methods for medical information extraction,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:05.087962Z

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Observation 2914ebf1-6ed6-4a2f-a030-fb59e4e3e963 · outbound

This paper cites Artificial intelligence in pharmaceutical regulatory affairs,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Artificial intelligence in pharmaceutical regulatory affairs,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:04.965454Z

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Observation c0659867-7f8f-4764-ba53-6d4b7e870b4d · outbound

This paper cites Post-market surveillance of medical devices using ai,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Post-market surveillance of medical devices using ai,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:04.819326Z

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Observation 1e54ebd8-bd50-42c1-bcc8-9acd76cac965 · outbound

This paper cites Automatic induction of rule based text categorization,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Automatic induction of rule based text categorization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:04.682800Z

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Observation a3741553-69d6-4f17-9a79-fefd3fee6330 · outbound

This paper cites A review of machine learning algorithms for text-documents classification,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification A review of machine learning algorithms for text-documents classification,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:04.560176Z

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Observation 6baa5c6c-efe4-4be0-b262-b966753c150e · outbound

This paper cites Rule-based semantic relation extraction in regulatory documents.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Rule-based semantic relation extraction in regulatory documents

Reference 20

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Observation 8c3556b9-d322-4ae6-9d67-baaf5c76660b · outbound

This paper cites Support vector machines for classification,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Support vector machines for classification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:04.341249Z

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Observation 79301ee0-00d6-4f57-9f20-f2030b06730e · outbound

This paper cites Na ¨ ıve bayes.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Na ¨ ıve bayes

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:04.223928Z

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Observation d6aa5760-0bfb-44a3-9a28-8ade00a3abd4 · outbound

This paper cites Semantic text classification for supporting automated compliance checking in construction,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Semantic text classification for supporting automated compliance checking in construction,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:04.087803Z

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Observation 63e9ba7a-abb0-43c7-8b01-11e106f83766 · outbound

This paper cites Semantic nlp-based information extraction from construction regulatory documents for automated compliance checking,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Semantic nlp-based information extraction from construction regulatory documents for automated compliance checking,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:03.938063Z

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Observation 665f4333-23d5-4342-99b6-f35f4465d2e6 · outbound

This paper cites A machine learning approach for medical device classification,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification A machine learning approach for medical device classification,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:03.787988Z

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Observation 268a1885-7c3d-441f-a1e8-d0a9ccd97fa0 · outbound

This paper cites Large language modeling and classical ai methods for the future of healthcare,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Large language modeling and classical ai methods for the future of healthcare,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:03.644579Z

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Observation e23db86e-3970-45be-b156-406f6b9cdbf5 · outbound

This paper cites Regulating AI Adaptation: An Analysis of AI Medical Device Updates.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Regulating AI Adaptation: An Analysis of AI Medical Device Updates

Reference 27

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Observation ee967404-b0b2-4bab-8917-a2a9384db052 · outbound

This paper cites Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities

Reference 28

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Observation 28e79faa-73c8-42cd-affd-6d898afd346d · outbound

This paper cites Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models

Reference 29

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Observation 5e6c6ba3-2cfa-4097-8c38-ccb6e50b683b · outbound

This paper cites Improving support vector machine classifiers by modifying kernel functions,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Improving support vector machine classifiers by modifying kernel functions,

Reference 30

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Observation 155c0c7f-c1b1-4589-81c8-95d489a18d42 · outbound

This paper cites Xgboost: A scalable tree boosting system,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Xgboost: A scalable tree boosting system,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:03.334179Z

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Observation 2e674a4f-21cc-4807-a910-17f70cd2b966 · outbound

This paper cites A unified approach to interpreting model predictions,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification A unified approach to interpreting model predictions,

Reference 32

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This paper cites Convolutional neural networks for sentence classification,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Convolutional neural networks for sentence classification,

Reference 33

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Observation 23aa3d24-4d19-4b1a-a955-cfa9b792fc3b · outbound

This paper cites Comparative Study of CNN and RNN for Natural Language Processing.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Comparative Study of CNN and RNN for Natural Language Processing

Reference 34

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This paper cites Deep pyramid convolutional neural networks for text categorization,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Deep pyramid convolutional neural networks for text categorization,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:02.927597Z

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AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Recurrent convolutional neural networks for text classification,

Reference 36

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This paper cites Axiomatic attribution for deep networks,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Axiomatic attribution for deep networks,

Reference 37

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Observation 8fc918e3-c49d-4029-883d-259e758af29e · outbound

This paper cites Why should i trust you? explaining the predictions of any classifier,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Why should i trust you? explaining the predictions of any classifier,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:02.446918Z

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Observation 88c26d25-95d7-4057-898d-75c04296d0ce · outbound

This paper cites Attention is all you need,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Attention is all you need,

Reference 39

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AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 40

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AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Pre-Training with Whole Word Masking for Chinese BERT

Reference 41

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification LLaMA: Open and Efficient Foundation Language Models

Reference 42

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AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification TDFNet: An Efficient Audio-Visual Speech Separation Model with Top-down Fusion

Reference 43

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Observation 5601fa0e-e292-4cb1-9507-016d50dce74f · outbound

This paper cites Towards faithfully interpretable nlp systems: How should we define and evaluate faith- fulness?.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Towards faithfully interpretable nlp systems: How should we define and evaluate faith- fulness?

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:02.347926Z

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Observation 372d5a29-e491-46c2-86b9-82826d931912 · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:02.197535Z

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AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Anchors: High-precision model-agnostic explanations,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:02.015345Z

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Observation 4448605c-20e3-43b4-9459-3033add35000 · outbound

This paper cites Explainable machine-learning predictions for the prevention of hypoxaemia during surgery,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Explainable machine-learning predictions for the prevention of hypoxaemia during surgery,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:01.847484Z

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Observation b2509084-d06d-4231-be97-1d062e4a71a9 · outbound

This paper cites Local explanation methods for tree-based models: A unified approach,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Local explanation methods for tree-based models: A unified approach,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:01.675933Z

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Observation 7fad6d37-e386-443e-9d1b-15eb469d6294 · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Energy and Policy Considerations for Deep Learning in NLP

Reference 49

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Observation ca5c15e3-a5e5-4c6d-9c2f-368c1c5ca4b7 · outbound

This paper cites Tensor Moments of Gaussian Mixture Models: Theory and Applications.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Tensor Moments of Gaussian Mixture Models: Theory and Applications

Reference 50

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This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T14:29:57.444055Z

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This paper cites Towards practical trade-offs between interpretability and performance: A composite interpretability metric for trustworthy ai,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Towards practical trade-offs between interpretability and performance: A composite interpretability metric for trustworthy ai,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:01.475907Z

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Observation be442b33-eec1-46c8-9cd1-bf7f2159105e · outbound

This paper cites Trust and transparency in human-ai interaction: A survey of trust calibration, user understanding, and interpretability,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Trust and transparency in human-ai interaction: A survey of trust calibration, user understanding, and interpretability,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:01.258535Z

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This paper cites A taxonomy of interpretability in human-centered ai: From explanations to user experience,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification A taxonomy of interpretability in human-centered ai: From explanations to user experience,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:01.088486Z

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This paper cites Udi database,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Udi database,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:00.971858Z

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This paper cites Split the data between the training data and test data using sklearn,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Split the data between the training data and test data using sklearn,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:00.857219Z

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This paper cites Scikit-learn: Machine learning in python,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Scikit-learn: Machine learning in python,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:00.735220Z

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Observation 5acffe1b-6c97-427d-9f48-8008466efcf5 · outbound

This paper cites Focusagent: [article title],.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Focusagent: [article title],

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:00.609239Z

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Observation 5a60370e-82c8-43d0-a196-9fb9ec980721 · outbound

This paper cites Focus agent: Llm-powered virtual focus group,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Focus agent: Llm-powered virtual focus group,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:00.505198Z

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Observation c460cb88-0c42-4c13-af1a-3215c5ab72a7 · outbound

This paper cites A training algorithm for optimal margin classifiers,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification A training algorithm for optimal margin classifiers,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:00.380087Z

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Observation c3ac8bc2-4e0a-4df5-ac81-666ddbeb770f · outbound

This paper cites On the Robustness of Interpretability Methods.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification On the Robustness of Interpretability Methods

Reference 61

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This paper cites Proposal for a regulation on a european approach for artificial intelligence,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Proposal for a regulation on a european approach for artificial intelligence,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:00.252402Z

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Observation b0ad1984-e8c2-44b3-9bc5-49852d27d124 · outbound

This paper cites Good machine learning practice for medical device development: Guid- ing principles,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Good machine learning practice for medical device development: Guid- ing principles,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:30:00.110405Z

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Observation b9a3f7b4-0533-490c-8878-6df8a0c49ec9 · outbound

This paper cites Counterfactual explanations without opening the black box: Automated decisions and the gdpr,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Counterfactual explanations without opening the black box: Automated decisions and the gdpr,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:59.970968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1206093e-7787-4322-aeaa-e22db33c78cf · outbound

This paper cites Mimic-iii, a freely accessible critical care database,.

AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Mimic-iii, a freely accessible critical care database,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:59.820620Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 1339542d-d331-44c1-ba9e-702d54abe5e6 · inbound

A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms cites this paper.

A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:43:49.298586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.