mlsanityData to 7 Oct 2026

Automated Clinical Coding: What, Why, and Where We Are?

arXiv 2203.11092 ↗First posted 21 Mar 2022cs.CL cs.AIcited by 33

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Abstract (arXiv)

Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019 - early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 26 of its 109 references that are in the map. First posted on arXiv 21 Mar 2022.

Builds on

26 of 109 references.

References · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2018Explainable Prediction of Medical Codes from Clinical Textcited by 231
  3. 2020Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkitcited by 66
  4. 2022Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Codingcited by 52
  5. 2020An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labelscited by 44
  6. 2021Knowledge-aware Zero-Shot Learning: Survey and Perspectivecited by 41
  7. 2018Multimodal Machine Learning for Automated ICD Codingcited by 40
  8. 2021Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Studycited by 39
  9. 2020Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisationcited by 38
  10. 2017Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignmentcited by 37
  11. 2013Large-scale Multi-label Text Classification - Revisiting Neural Networkscited by 35
  12. 2022A Unified Review of Deep Learning for Automated Medical Codingcited by 26
14 more
  1. 2021Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machinescited by 23
  2. 2020Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Datasetcited by 15
  3. 2021Description-based Label Attention Classifier for Explainable ICD-9 Classificationcited by 12
  4. 2021Estimating Redundancy in Clinical Textcited by 10
  5. 2021A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summariescited by 10
  6. 2021Multitask Balanced and Recalibrated Network for Medical Code Predictioncited by 9
  7. 2021CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classificationcited by 9
  8. 2019Clinical Concept Extraction for Document-Level Codingcited by 7
  9. 2019MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisationcited by 7
  10. 2021Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervisioncited by 5
  11. 2020Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attentioncited by 4
  12. 2021Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitalscited by 3
  13. 2021Active learning for medical code assignmentcited by 2
  14. 2021Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documentscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 10; 2025: 9; 2026: 6.

33 papers.

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  1. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  2. 2023Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Studycited by 48
  3. 2024Exploring LLM Multi-Agents for ICD Codingcited by 14
  4. 2023A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promisescited by 13
  5. 2024Can GPT-3.5 Generate and Code Discharge Summaries?cited by 12
  6. 2023Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunitiescited by 10
  7. 2025Code Like Humans: A Multi-Agent Solution for Medical Codingcited by 10
  8. 2025MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathwayscited by 9
  9. 2022Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?cited by 7
  10. 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationshipscited by 7
  11. 2025Reinforcement Learning for Out-of-Distribution Reasoning in LLMs: An Empirical Study on Diagnosis-Related Group Codingcited by 7
  12. 2024e-Health CSIRO at "Discharge Me!" 2024: Generating Discharge Summary Sections with Fine-tuned Language Modelscited by 4
21 more
  1. 2024Shimo Lab at "Discharge Me!": Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sectionscited by 3
  2. 2024Fine-tuning foundational models to code diagnoses from veterinary health recordscited by 3
  3. 2025MedDCR: Learning to Design Agentic Workflows for Medical Codingcited by 3
  4. 2023Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letterscited by 2
  5. 2024Infusing clinical knowledge into tokenisers for language modelscited by 2
  6. 2024A Comparative Study on Automatic Coding of Medical Letters with Explainabilitycited by 2
  7. 2025OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Modelscited by 2
  8. 2023Improving Text-based Early Prediction by Distillation from Privileged Time-Series Textcited by 1
  9. 2024Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processingcited by 1
  10. 2025Explainable ICD Coding via Entity Linkingcited by 1
  11. 2026A medical coding language model trained on clinical narratives from a population-wide cohort of 1.8 million patientscited by 1
  12. 2023Automatic Coding at Scale: Design and Deployment of a Nationwide System for Normalizing Referrals in the Chilean Public Healthcare Systemcited by 0
  13. 2024Improving ICD coding using Chapter based Named Entities and Attentional Modelscited by 0
  14. 2025Can Modern NLP Systems Reliably Annotate Chest Radiography Exams? A Pre-Purchase Evaluation and Comparative Study of Solutions from AWS, Google, Azure, John Snow Labs, and Open-Source Models on an Independent Pediatric Datasetcited by 0
  15. 2025A Hierarchical Error Framework for Reliable Automated Coding in Communication Research: Applications to Health and Political Communicationcited by 0
  16. 2025TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integrationcited by 0
  17. 2026Symphony for Medical Coding: A Next-Generation Agentic System for Scalable and Explainable Medical Codingcited by 0
  18. 2026Ontology-Aware Design Patterns for Clinical AI Systems: Translating Reification Theory into Software Architecturecited by 0
  19. 2026Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codescited by 0
  20. 2026Enhancing LLM Medical Coding with Structured External Knowledgecited by 0
  21. 2026SNOMED CT Concept Recommendation from Masked Clinical Contextcited by 0