---
title: "Medicine — papers — Elamur Research"
description: "Medicine: 75 papers of the Elamur Research library · page 1 of 2 · 1–25, the most cited first — our summary of each, its ideas, references…"
canonical: "https://research.elamur.ai/free/en/shelves/medicine/"
---
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# 🩺 Medicine

75 papers · page 1 of 2 · 1–25 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Clinical medicine, public health, diagnosis and the language of health records.

## Subfields

The sub-shelves of Medicine, the most papers first, with the two OpenAlex topics most of their papers here are on.

1. [**Health Informatics**Artificial Intelligence in Healthcare and Education*53 papers*](https://md.research.elamur.ai/free/en/shelves/medicine/health-informatics/)
2. [**Physiology**Voice and Speech Disorders*11 papers*](https://md.research.elamur.ai/free/en/shelves/medicine/physiology/)
3. [**Public Health, Environmental and Occupational Health**Consumer Attitudes and Food Labeling · Ethics in Clinical Research*5 papers*](https://md.research.elamur.ai/free/en/shelves/medicine/public-health-environmental-and-occupational-health/)
4. [**Epidemiology**Data-Driven Disease Surveillance · Sepsis Diagnosis and Treatment*3 papers*](https://md.research.elamur.ai/free/en/shelves/medicine/epidemiology/)
5. [**Psychiatry and Mental health**Dementia and Cognitive Impairment Research · Attention Deficit Hyperactivity Disorder*3 papers*](https://md.research.elamur.ai/free/en/shelves/medicine/psychiatry-and-mental-health/)
6. [**Infectious Diseases**SARS-CoV-2 and COVID-19 Research · Viral Infections and Outbreaks Research*2 papers*](https://md.research.elamur.ai/free/en/shelves/medicine/infectious-diseases/)
7. [**Radiology, Nuclear Medicine and Imaging**COVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging*2 papers*](https://md.research.elamur.ai/free/en/shelves/medicine/radiology-nuclear-medicine-and-imaging/)
8. [**Pulmonary and Respiratory Medicine**Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis · Lung Cancer Diagnosis and Treatment*1 paper*](https://md.research.elamur.ai/free/en/shelves/medicine/pulmonary-and-respiratory-medicine/)

A survey · about 4 minutes to read · every paper named here has its own page in this library

**In this article**

1. [Scope](#a-scope)
2. [History in brief](#a-history-in-brief)
3. [Papers in this library](#a-papers-in-this-library)
4. [People and places](#a-people-and-places)
5. [Open questions](#a-open-questions)
6. [Neighbouring shelves](#a-neighbouring-shelves)
7. [The library in figures](#a-in-figures)

The Medicine shelf collects the papers of this library that OpenAlex places in medicine, mostly language processing built for biology and health: finding named entities in research abstracts, adapting pretrained models to clinical notes, summarising patient records, simplifying medical texts for lay readers, and structuring radiology reports. It is a small corner of this library, and most of its papers also stand on the [Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) shelf.

What the shelf does show is the arc of biomedical NLP in miniature: a classic shared task from the early 2000s, a careful benchmark of domain-adapted transformers from 2020, and a cluster of 2026 workshop papers that apply large and small language models to clinical text in French, Hebrew, German and English.

## Scope

Biomedical text differs from news or web text in vocabulary, abbreviation habits, naming conventions and, in the clinic, in privacy constraints and terse note-taking. Research in the area has two broad branches: work on the scientific literature (abstracts and full texts in PubMed) and work on clinical documents (patient records, discharge letters, imaging reports). Both branches appear here, alongside one paper on health-related social-media-style classification of research sentences.

The topics on this shelf Semitic languages: 18 of the 75 papers here.

**The numbers**

| Topic | Papers |
| --- | --- |
| [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) | 18 papers |
| [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) | 10 papers |
| [Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) | 6 papers |
| [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) | 5 papers |
| [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) | 4 papers |
| [Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) | 4 papers |
| [Text generation](https://md.research.elamur.ai/free/en/topics/text-generation/) | 3 papers |
| [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) | 2 papers |
| [French](https://md.research.elamur.ai/free/en/topics/french/) | 2 papers |
| [Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) | 2 papers |
| [Modern Hebrew](https://md.research.elamur.ai/free/en/topics/modern-hebrew/) | 2 papers |
| [Speech & spoken language](https://md.research.elamur.ai/free/en/topics/speech/) | 2 papers |

## History in brief

Biomedical [named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) became a community benchmark task in the early 2000s, built on the GENIA corpus of annotated MEDLINE abstracts. The late 2010s brought domain-specific pretrained encoders such as BioBERT, SciBERT and ClinicalBERT, and with them the question of whether domain pretraining beats a stronger general model. The most recent papers move to retrieval-augmented generation, small open models and uncertainty estimation, and to languages other than English.

Papers on this shelf by year 75 dated papers from 2016 to 2026; the busiest year is 2026 with 26, and 95% of them appeared in 2018 or later.

**The numbers**

| Year | Papers |
| --- | --- |
| 2026 | 26 |
| 2025 | 8 |
| 2024 | 8 |
| 2023 | 21 |
| 2022 | 2 |
| 2021 | 1 |
| 2020 | 4 |
| 2018 | 1 |
| 2017 | 1 |
| 2016 | 3 |

Citations of the papers on this shelf, by when they were published Papers published in 2020–2024 gather 72% of the 35,501 citations of the papers on this shelf.

**The numbers**

| Published | Papers and their citations |
| --- | --- |
| 2025–2029 | 34 papers, 449 citations |
| 2020–2024 | 36 papers, 25,730 citations |
| 2015–2019 | 5 papers, 9,322 citations |

## Papers in this library

**A benchmark for biomedical entity recognition.** [Introduction to the Bio-entity Recognition Task at JNLPBA](https://md.research.elamur.ai/free/en/papers/collier-2004-introduction-bio-entity-recognition-task/) (2004) describes the shared task that asked systems to tag proteins, DNA, RNA, cell types and cell lines in MEDLINE abstracts. The organisers collapsed GENIA's fine-grained classes into five, released data in IOB2 format, and split the test abstracts by publication period to probe generalisation. Most participants used support vector machines, often combined with sequence models; even the best total F-score stayed under 73, well behind newswire systems of the time. The data set remained a standard reference for biomedical NER and is still among the tasks used to test later models.

**Pretrained models for biomedical and clinical text.** [Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art](https://md.research.elamur.ai/free/en/papers/lewis-2020-pretrained-language-models-biomedical-clinical/) (2020) put existing domain models and general RoBERTa checkpoints through one fine-tuning protocol on 18 established tasks, JNLPBA among them. Domain models led on literature tasks, while the large general model did better on clinical ones. The authors then pretrained RoBERTa-style models on freely available PubMed, PubMed Central and MIMIC-III text; a large model with an in-domain vocabulary and mixed data set new best results on half the tasks, and the models were released.

**Retrieval for French patient records.** [Evaluating the Retrieval Component in a Retrieval-Augmented Summarization System for Patient Records in French](https://md.research.elamur.ai/free/en/papers/naguib-2026-evaluating-retrieval-component-retrieval-augmented/) (2026) studies the first stage of a summariser for intensive-care clinicians. Queries built from physicians' annotations retrieved relevant chunks better than hand-written prompts, especially with simple TF-IDF; see also [information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) and [summarization](https://md.research.elamur.ai/free/en/topics/summarization/).

**Simplifying mental-health definitions.** [Evaluation of Multilingual Text Simplification for the Mental Health Domain: Exploring Small Language Models](https://md.research.elamur.ai/free/en/papers/pelloni-2026-evaluation-multilingual-text-simplification-mental/) (2026) tried nine small models on ICD-11 articles about psychotic disorders in three languages. Only one model gave usable output, and only in English; changes in lexical diversity and readability proved more telling than similarity scores.

**Classifying sentences about patients.** [PEI at #SMM4H-HeaRD 2026: Enhancing Patient Metadata Detection via Hypothesis-Conditioned Classification and Paraphrase-Based Data Augmentation](https://md.research.elamur.ai/free/en/papers/zeidi-2026-pei-smm4h-heard-2026-enhancing/) (2026) found that a biomedical BERT encoder, recast as a hypothesis-pairing task and trained on paraphrase-augmented data, beat a fine-tuned generative model at spotting patient details in COVID-19 sequencing papers.

**Uncertainty in Hebrew radiology.** [Trustworthy NLP for Low-Resource Languages: Agent-Based Uncertainty Modeling for Hebrew Radiology Report Structuring](https://md.research.elamur.ai/free/en/papers/atya-2026-trustworthy-nlp-low-resource-languages/) (2026) asks a model the same question through several paraphrased prompts and lets a larger model grade its confidence; withholding uncertain cases raised accuracy at the cost of coverage. It links the shelf to [Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) and [low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/).

## People and places

Authors with the most papers on this shelf Ali Hamdi leads with 4 papers; the 12 authors shown wrote 30 of the papers on this shelf between them, counting each paper once per author.

**The numbers**

| Author | Papers here · their citations |
| --- | --- |
| [Ali Hamdi](https://md.research.elamur.ai/free/en/authors/ali-hamdi/) | 4 papers · 10 citations |
| [Tao Tu](https://md.research.elamur.ai/free/en/authors/tao-tu/) | 3 papers · 5,198 citations |
| [Karan Singhal](https://md.research.elamur.ai/free/en/authors/karan-singhal/) | 3 papers · 5,101 citations |
| [Xiang Wan](https://md.research.elamur.ai/free/en/authors/xiang-wan/) | 3 papers · 1,046 citations |
| [Zhihong Chen](https://md.research.elamur.ai/free/en/authors/zhihong-chen/) | 3 papers · 1,046 citations |
| [Mengling Feng](https://md.research.elamur.ai/free/en/authors/mengling-feng/) | 2 papers · 8,630 citations |
| [Heather Cole-Lewis](https://md.research.elamur.ai/free/en/authors/heather-cole-lewis/) | 2 papers · 4,986 citations |
| [Stephen Pfohl](https://md.research.elamur.ai/free/en/authors/stephen-pfohl/) | 2 papers · 4,986 citations |
| [Jason Wei](https://md.research.elamur.ai/free/en/authors/jason-wei/) | 2 papers · 4,592 citations |
| [Anuj Pareek](https://md.research.elamur.ai/free/en/authors/anuj-pareek/) | 2 papers · 1,264 citations |
| [Juraj Gottweis](https://md.research.elamur.ai/free/en/authors/juraj-gottweis/) | 2 papers · 721 citations |
| [Ankit Pal](https://md.research.elamur.ai/free/en/authors/ankit-pal/) | 2 papers · 674 citations |

The JNLPBA task was organised by [Nigel Collier](https://md.research.elamur.ai/free/en/authors/nigel-collier/), now associated with the University of Cambridge and [University College London](https://md.research.elamur.ai/free/en/universities/university-college-london/), together with [Jin-Dong Kim](https://md.research.elamur.ai/free/en/authors/jin-dong-kim/), [Tomoko Ohta](https://md.research.elamur.ai/free/en/authors/tomoko-ohta/) and [Yoshimasa Tsuruoka](https://md.research.elamur.ai/free/en/authors/yoshimasa-tsuruoka/), names closely tied to the GENIA project at [The University of Tokyo](https://md.research.elamur.ai/free/en/universities/the-university-of-tokyo/). The 2020 benchmark came from [Patrick Lewis](https://md.research.elamur.ai/free/en/authors/patrick-lewis/), [Myle Ott](https://md.research.elamur.ai/free/en/authors/myle-ott/), [Jingfei Du](https://md.research.elamur.ai/free/en/authors/jingfei-du/) and [Veselin Stoyanov](https://md.research.elamur.ai/free/en/authors/veselin-stoyanov/); Lewis is now at [Cohere](https://md.research.elamur.ai/free/en/companies/cohere/) and Stoyanov at [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/).

French clinical NLP is represented by [Aurélie Névéol](https://md.research.elamur.ai/free/en/authors/aurelie-neveol/) of the [Centre National de la Recherche Scientifique](https://md.research.elamur.ai/free/en/labs/centre-national-de-la-recherche-scientifique/) and [Xavier Tannier](https://md.research.elamur.ai/free/en/authors/xavier-tannier/), linked with [Inserm](https://md.research.elamur.ai/free/en/labs/inserm/) and [Sorbonne Université](https://md.research.elamur.ai/free/en/universities/sorbonne-universite/). The Hebrew radiology work joins engineers such as [Moti Freiman](https://md.research.elamur.ai/free/en/authors/moti-freiman/) and [Hadas Ben Atya](https://md.research.elamur.ai/free/en/authors/hadas-ben-atya/) of the [Technion](https://md.research.elamur.ai/free/en/universities/technion-israel-institute-of-technology/) with clinicians including [Dan Turner](https://md.research.elamur.ai/free/en/authors/dan-turner/) of the [Hebrew University of Jerusalem](https://md.research.elamur.ai/free/en/universities/hebrew-university-of-jerusalem/).

Countries of the authors on this shelf United States comes first: authors working there now wrote 36 papers on this shelf.

**The numbers**

| Country | Papers |
| --- | --- |
| [United States](https://md.research.elamur.ai/free/en/countries/united-states/) | 36 papers |
| [China](https://md.research.elamur.ai/free/en/countries/china/) | 22 papers |
| [United Kingdom](https://md.research.elamur.ai/free/en/countries/united-kingdom/) | 7 papers |
| [Singapore](https://md.research.elamur.ai/free/en/countries/singapore/) | 6 papers |
| [Germany](https://md.research.elamur.ai/free/en/countries/germany/) | 5 papers |
| [Netherlands](https://md.research.elamur.ai/free/en/countries/netherlands/) | 5 papers |
| [Egypt](https://md.research.elamur.ai/free/en/countries/egypt/) | 4 papers |
| [Japan](https://md.research.elamur.ai/free/en/countries/japan/) | 4 papers |
| [United Arab Emirates](https://md.research.elamur.ai/free/en/countries/united-arab-emirates/) | 4 papers |
| [India](https://md.research.elamur.ai/free/en/countries/india/) | 3 papers |

Where the papers on this shelf were published Language Resources and Evaluation Conference (LREC) carries the most, 20 of 75.

**The numbers**

| Venue | Papers |
| --- | --- |
| [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/) | 20 papers |
| [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) | 5 papers |
| [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) | 3 papers |
| [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/) | 2 papers |
| [Nature](https://md.research.elamur.ai/free/en/venues/nature/) | 2 papers |
| [ACL Workshop on Ethics in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/acl-workshop-on-ethics-in-natural-language-processing/) | 1 paper |
| [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) | 1 paper |
| [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/) | 1 paper |
| [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/) | 1 paper |
| [Bioinformatics](https://md.research.elamur.ai/free/en/venues/bioinformatics/) | 1 paper |

## Open questions

The papers here point to several unsolved issues: how to evaluate simplification and summarisation of medical text without costly expert judgement; whether small, locally run models can be trusted with clinical documents in languages with little training data; and when a system should abstain rather than answer. The gap between English and other languages is visible throughout.

## Neighbouring shelves

All of the shelf's papers also sit on [Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/); some cross into [Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) and [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) through their multilingual work. Related topics include [evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/), [language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and [multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/).

Neighbouring shelves: papers shared Computer Science shares the most: 62 of the 75 papers here (83%) stand on both.

**The numbers**

| Shelf | Papers shared |
| --- | --- |
| [Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) | 62 shared · 83% |
| [Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) | 50 shared · 67% |
| [Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) | 10 shared · 13% |
| [Biochemistry, Genetics and Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) | 5 shared · 7% |
| [Health Professions](https://md.research.elamur.ai/free/en/shelves/health-professions/) | 5 shared · 7% |
| [Agricultural and Biological Sciences](https://md.research.elamur.ai/free/en/shelves/agricultural-and-biological-sciences/) | 2 shared · 3% |
| [Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) | 2 shared · 3% |
| [Decision Sciences](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) | 1 shared · 1% |
| [Environmental Science](https://md.research.elamur.ai/free/en/shelves/environmental-science/) | 1 shared · 1% |
| [Neuroscience](https://md.research.elamur.ai/free/en/shelves/neuroscience/) | 1 shared · 1% |

## The library in figures

Landmark papers on this shelf: the 12 most cited, in time The 12 most cited papers on this shelf span 2016–2023; the dot grows with the citations.

**The numbers**

| Paper | Citations |
| --- | --- |
| [2016 — MIMIC-III, a freely accessible critical care database](https://md.research.elamur.ai/free/en/papers/johnson-2016-mimic-iii-freely-accessible-critical/) | 8,544 |
| [2018 — On the Automatic Generation of Medical Imaging Reports](https://md.research.elamur.ai/free/en/papers/jing-2018-automatic-generation-medical-imaging-reports/) | 480 |
| [2020 — Generating Radiology Reports via Memory-driven Transformer](https://md.research.elamur.ai/free/en/papers/chen-2020-generating-radiology-reports-memory-driven/) | 710 |
| [2020 — CORD-19: The COVID-19 Open Research Dataset](https://md.research.elamur.ai/free/en/papers/wang-2020-cord-19-covid-19-open/) | 689 |
| [2022 — Large Language Models Encode Clinical Knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/) | 4,477 |
| [2023 — ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/) | 7,436 |
| [2023 — Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models](https://md.research.elamur.ai/free/en/papers/kung-2023-performance-chatgpt-usmle-potential-ai/) | 4,168 |
| [2023 — Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://md.research.elamur.ai/free/en/papers/veen-2023-adapted-large-language-models-outperform/) | 928 |
| [2023 — Capabilities of GPT-4 on Medical Challenge Problems](https://md.research.elamur.ai/free/en/papers/nori-2023-capabilities-gpt-4-medical-challenge/) | 711 |
| [2023 — LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://md.research.elamur.ai/free/en/papers/li-2023-llava-med-training-large-language/) | 680 |
| [2023 — ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://md.research.elamur.ai/free/en/papers/li-2023-chatdoctor-medical-chat-model-fine/) | 643 |
| [2023 — Towards Expert-Level Medical Question Answering with Large Language Models](https://md.research.elamur.ai/free/en/papers/singhal-2023-towards-expert-level-medical-question/) | 509 |

Where the authors on this shelf work now University of Science and Technology of China is the current home of authors of 6 papers on this shelf; counted are the authors with a page here, by their latest institution.

**The numbers**

| Institution | Papers |
| --- | --- |
| [University of Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) | 6 papers |
| [University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) | 6 papers |
| [Harvard University](https://md.research.elamur.ai/free/en/universities/harvard-university/) | 5 papers |
| [Google](https://md.research.elamur.ai/free/en/companies/google/) | 4 papers |
| [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) | 4 papers |
| [National University of Defense Technology](https://md.research.elamur.ai/free/en/universities/national-university-of-defense-technology/) | 4 papers |
| [National University of Singapore](https://md.research.elamur.ai/free/en/universities/national-university-of-singapore/) | 4 papers |
| [New York University](https://md.research.elamur.ai/free/en/universities/new-york-university/) | 4 papers |
| [October University of Modern Sciences and Arts](https://md.research.elamur.ai/free/en/universities/october-university-of-modern-sciences-and-arts/) | 4 papers |
| [ByteDance](https://md.research.elamur.ai/free/en/companies/bytedance/) | 3 papers |

## Papers (75)

**1** [2](https://md.research.elamur.ai/free/en/shelves/medicine/page/2/) [next →](https://md.research.elamur.ai/free/en/shelves/medicine/page/2/)

1. [**MIMIC-III, a freely accessible critical care database**Alistair E. W. Johnson, Tom Pollard, Lu Shen, et al.*2016 · 8,544 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2016-mimic-iii-freely-accessible-critical/)
2. [**ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education**Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.*2023 · 7,436 citations*](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
3. [**Large Language Models Encode Clinical Knowledge**Karan Singhal, Shekoofeh Azizi, Tao Tu, et al.*2022 · 4,477 citations*](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/)
4. [**Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models**Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, et al.*2023 · 4,168 citations*](https://md.research.elamur.ai/free/en/papers/kung-2023-performance-chatgpt-usmle-potential-ai/)
5. [**Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization**Dave Van Veen, Cara Van Uden, Louis Blankemeier, et al.*2023 · 928 citations*](https://md.research.elamur.ai/free/en/papers/veen-2023-adapted-large-language-models-outperform/)
6. [**Capabilities of GPT-4 on Medical Challenge Problems**Harsha Nori, Nicholas King, Scott Mayer McKinney, et al.*2023 · 711 citations*](https://md.research.elamur.ai/free/en/papers/nori-2023-capabilities-gpt-4-medical-challenge/)
7. [**Generating Radiology Reports via Memory-driven Transformer**Zhihong Chen, Yan Song, Tsung-Hui Chang, et al.*2020 · 710 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-generating-radiology-reports-memory-driven/)
8. [**CORD-19: The COVID-19 Open Research Dataset**Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, et al.*2020 · 689 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-cord-19-covid-19-open/)
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