Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization Article Swipe
Dave Van Veen
,
Cara Van Uden
,
Louis Blankemeier
,
Jean-Benoit Delbrouck
,
Asad Aali
,
Christian Bluethgen
,
Anuj Pareek
,
Malgorzata Polacin
,
Eduardo Pontes Reis
,
Anna Seehofnerová
,
Nidhi Rohatgi
,
Poonam Hosamani
,
William Collins
,
Neera Ahuja
,
Curtis P. Langlotz
,
Jason Hom
,
Sergios Gatidis
,
John M. Pauly
,
Akshay Chaudhari
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.07430
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.07430
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP), their effectiveness on a diverse range of clinical summarization tasks remains unproven. In this study, we apply adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Quantitative assessments with syntactic, semantic, and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with ten physicians evaluates summary completeness, correctness, and conciseness; in a majority of cases, summaries from our best adapted LLMs are either equivalent (45%) or superior (36%) compared to summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.07430
- https://arxiv.org/pdf/2309.07430
- OA Status
- green
- Cited By
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386794639
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386794639Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2309.07430Digital Object Identifier
- Title
-
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text SummarizationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-14Full publication date if available
- Authors
-
Dave Van Veen, Cara Van Uden, Louis Blankemeier, Jean-Benoit Delbrouck, Asad Aali, Christian Bluethgen, Anuj Pareek, Malgorzata Polacin, Eduardo Pontes Reis, Anna Seehofnerová, Nidhi Rohatgi, Poonam Hosamani, William Collins, Neera Ahuja, Curtis P. Langlotz, Jason Hom, Sergios Gatidis, John M. Pauly, Akshay ChaudhariList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.07430Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.07430Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2309.07430Direct OA link when available
- Concepts
-
Automatic summarization, Computer science, Categorization, Documentation, Adaptation (eye), Health care, Workflow, Correctness, Artificial intelligence, Medical record, Natural language processing, Data science, Medicine, Psychology, Radiology, Political science, Database, Law, Programming language, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
35Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 22, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4386794639 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2309.07430 |
| ids.doi | https://doi.org/10.48550/arxiv.2309.07430 |
| ids.openalex | https://openalex.org/W4386794639 |
| fwci | 8.94050107 |
| type | preprint |
| title | Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10028 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9986000061035156 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Topic Modeling |
| topics[1].id | https://openalex.org/T13702 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9876000285148621 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Machine Learning in Healthcare |
| topics[2].id | https://openalex.org/T10181 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.986299991607666 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Natural Language Processing Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C170858558 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8072146773338318 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1394144 |
| concepts[0].display_name | Automatic summarization |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5691713094711304 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C94124525 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5422864556312561 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q912550 |
| concepts[2].display_name | Categorization |
| concepts[3].id | https://openalex.org/C56666940 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5069041848182678 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q788790 |
| concepts[3].display_name | Documentation |
| concepts[4].id | https://openalex.org/C139807058 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4925914406776428 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q352374 |
| concepts[4].display_name | Adaptation (eye) |
| concepts[5].id | https://openalex.org/C160735492 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4708383083343506 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q31207 |
| concepts[5].display_name | Health care |
| concepts[6].id | https://openalex.org/C177212765 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4650394320487976 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q627335 |
| concepts[6].display_name | Workflow |
| concepts[7].id | https://openalex.org/C55439883 |
| concepts[7].level | 2 |
| concepts[7].score | 0.43669945001602173 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q360812 |
| concepts[7].display_name | Correctness |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4169030487537384 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C195910791 |
| concepts[9].level | 2 |
| concepts[9].score | 0.41027164459228516 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1324077 |
| concepts[9].display_name | Medical record |
| concepts[10].id | https://openalex.org/C204321447 |
| concepts[10].level | 1 |
| concepts[10].score | 0.40163350105285645 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[10].display_name | Natural language processing |
| concepts[11].id | https://openalex.org/C2522767166 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3646218180656433 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[11].display_name | Data science |
| concepts[12].id | https://openalex.org/C71924100 |
| concepts[12].level | 0 |
| concepts[12].score | 0.3051877021789551 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[12].display_name | Medicine |
| concepts[13].id | https://openalex.org/C15744967 |
| concepts[13].level | 0 |
| concepts[13].score | 0.2812270522117615 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[13].display_name | Psychology |
| concepts[14].id | https://openalex.org/C126838900 |
| concepts[14].level | 1 |
| concepts[14].score | 0.11314985156059265 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[14].display_name | Radiology |
| concepts[15].id | https://openalex.org/C17744445 |
| concepts[15].level | 0 |
| concepts[15].score | 0.10321688652038574 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[15].display_name | Political science |
| concepts[16].id | https://openalex.org/C77088390 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[16].display_name | Database |
| concepts[17].id | https://openalex.org/C199539241 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[17].display_name | Law |
| concepts[18].id | https://openalex.org/C199360897 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[18].display_name | Programming language |
| concepts[19].id | https://openalex.org/C169760540 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[19].display_name | Neuroscience |
| keywords[0].id | https://openalex.org/keywords/automatic-summarization |
| keywords[0].score | 0.8072146773338318 |
| keywords[0].display_name | Automatic summarization |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5691713094711304 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/categorization |
| keywords[2].score | 0.5422864556312561 |
| keywords[2].display_name | Categorization |
| keywords[3].id | https://openalex.org/keywords/documentation |
| keywords[3].score | 0.5069041848182678 |
| keywords[3].display_name | Documentation |
| keywords[4].id | https://openalex.org/keywords/adaptation |
| keywords[4].score | 0.4925914406776428 |
| keywords[4].display_name | Adaptation (eye) |
| keywords[5].id | https://openalex.org/keywords/health-care |
| keywords[5].score | 0.4708383083343506 |
| keywords[5].display_name | Health care |
| keywords[6].id | https://openalex.org/keywords/workflow |
| keywords[6].score | 0.4650394320487976 |
| keywords[6].display_name | Workflow |
| keywords[7].id | https://openalex.org/keywords/correctness |
| keywords[7].score | 0.43669945001602173 |
| keywords[7].display_name | Correctness |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.4169030487537384 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/medical-record |
| keywords[9].score | 0.41027164459228516 |
| keywords[9].display_name | Medical record |
| keywords[10].id | https://openalex.org/keywords/natural-language-processing |
| keywords[10].score | 0.40163350105285645 |
| keywords[10].display_name | Natural language processing |
| keywords[11].id | https://openalex.org/keywords/data-science |
| keywords[11].score | 0.3646218180656433 |
| keywords[11].display_name | Data science |
| keywords[12].id | https://openalex.org/keywords/medicine |
| keywords[12].score | 0.3051877021789551 |
| keywords[12].display_name | Medicine |
| keywords[13].id | https://openalex.org/keywords/psychology |
| keywords[13].score | 0.2812270522117615 |
| keywords[13].display_name | Psychology |
| keywords[14].id | https://openalex.org/keywords/radiology |
| keywords[14].score | 0.11314985156059265 |
| keywords[14].display_name | Radiology |
| keywords[15].id | https://openalex.org/keywords/political-science |
| keywords[15].score | 0.10321688652038574 |
| keywords[15].display_name | Political science |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2309.07430 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2309.07430 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2309.07430 |
| locations[1].id | doi:10.48550/arxiv.2309.07430 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article-journal |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2309.07430 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5079666746 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9312-1773 |
| authorships[0].author.display_name | Dave Van Veen |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Van Veen, Dave |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5072177559 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5409-2279 |
| authorships[1].author.display_name | Cara Van Uden |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Van Uden, Cara |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5025304724 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6365-9767 |
| authorships[2].author.display_name | Louis Blankemeier |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Blankemeier, Louis |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5016013231 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Jean-Benoit Delbrouck |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Delbrouck, Jean-Benoit |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5046938499 |
| authorships[4].author.orcid | https://orcid.org/0009-0008-2120-5722 |
| authorships[4].author.display_name | Asad Aali |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Aali, Asad |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5111185084 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Christian Bluethgen |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Bluethgen, Christian |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5060928641 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-1526-3685 |
| authorships[6].author.display_name | Anuj Pareek |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Pareek, Anuj |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5003995646 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-0425-5479 |
| authorships[7].author.display_name | Malgorzata Polacin |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Polacin, Malgorzata |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5037419111 |
| authorships[8].author.orcid | https://orcid.org/0000-0001-5110-457X |
| authorships[8].author.display_name | Eduardo Pontes Reis |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Reis, Eduardo Pontes |
| authorships[8].is_corresponding | False |
| authorships[9].author.id | https://openalex.org/A5006006903 |
| authorships[9].author.orcid | |
| authorships[9].author.display_name | Anna Seehofnerová |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Seehofnerova, Anna |
| authorships[9].is_corresponding | False |
| authorships[10].author.id | https://openalex.org/A5022702389 |
| authorships[10].author.orcid | https://orcid.org/0000-0003-4574-0283 |
| authorships[10].author.display_name | Nidhi Rohatgi |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Rohatgi, Nidhi |
| authorships[10].is_corresponding | False |
| authorships[11].author.id | https://openalex.org/A5029998947 |
| authorships[11].author.orcid | |
| authorships[11].author.display_name | Poonam Hosamani |
| authorships[11].author_position | middle |
| authorships[11].raw_author_name | Hosamani, Poonam |
| authorships[11].is_corresponding | False |
| authorships[12].author.id | https://openalex.org/A5015508892 |
| authorships[12].author.orcid | https://orcid.org/0000-0003-0974-2599 |
| authorships[12].author.display_name | William Collins |
| authorships[12].author_position | middle |
| authorships[12].raw_author_name | Collins, William |
| authorships[12].is_corresponding | False |
| authorships[13].author.id | https://openalex.org/A5041327310 |
| authorships[13].author.orcid | https://orcid.org/0000-0002-2979-7530 |
| authorships[13].author.display_name | Neera Ahuja |
| authorships[13].author_position | middle |
| authorships[13].raw_author_name | Ahuja, Neera |
| authorships[13].is_corresponding | False |
| authorships[14].author.id | https://openalex.org/A5087710258 |
| authorships[14].author.orcid | https://orcid.org/0000-0002-8972-8051 |
| authorships[14].author.display_name | Curtis P. Langlotz |
| authorships[14].author_position | middle |
| authorships[14].raw_author_name | Langlotz, Curtis P. |
| authorships[14].is_corresponding | False |
| authorships[15].author.id | https://openalex.org/A5061093158 |
| authorships[15].author.orcid | https://orcid.org/0000-0002-8404-5796 |
| authorships[15].author.display_name | Jason Hom |
| authorships[15].author_position | middle |
| authorships[15].raw_author_name | Hom, Jason |
| authorships[15].is_corresponding | False |
| authorships[16].author.id | https://openalex.org/A5080097591 |
| authorships[16].author.orcid | https://orcid.org/0000-0002-6928-4967 |
| authorships[16].author.display_name | Sergios Gatidis |
| authorships[16].author_position | middle |
| authorships[16].raw_author_name | Gatidis, Sergios |
| authorships[16].is_corresponding | False |
| authorships[17].author.id | https://openalex.org/A5008348052 |
| authorships[17].author.orcid | https://orcid.org/0000-0001-5918-4172 |
| authorships[17].author.display_name | John M. Pauly |
| authorships[17].author_position | middle |
| authorships[17].raw_author_name | Pauly, John |
| authorships[17].is_corresponding | False |
| authorships[18].author.id | https://openalex.org/A5064829377 |
| authorships[18].author.orcid | https://orcid.org/0000-0002-3667-6796 |
| authorships[18].author.display_name | Akshay Chaudhari |
| authorships[18].author_position | last |
| authorships[18].raw_author_name | Chaudhari, Akshay S. |
| authorships[18].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2309.07430 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-09-16T00:00:00 |
| display_name | Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10028 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9986000061035156 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Topic Modeling |
| related_works | https://openalex.org/W2366403280, https://openalex.org/W1495108544, https://openalex.org/W2091301346, https://openalex.org/W3148229873, https://openalex.org/W4389760904, https://openalex.org/W2150160875, https://openalex.org/W4242223894, https://openalex.org/W4306886878, https://openalex.org/W2973759123, https://openalex.org/W1517524280 |
| cited_by_count | 35 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 12 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 22 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2309.07430 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2309.07430 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2309.07430 |
| primary_location.id | pmh:oai:arXiv.org:2309.07430 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2309.07430 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2309.07430 |
| publication_date | 2023-09-14 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 88 |
| abstract_inverted_index.a | 13, 38, 102 |
| abstract_inverted_index.In | 47 |
| abstract_inverted_index.as | 138 |
| abstract_inverted_index.by | 132 |
| abstract_inverted_index.in | 30, 101, 161 |
| abstract_inverted_index.of | 41, 104, 149, 156 |
| abstract_inverted_index.on | 16, 37, 185 |
| abstract_inverted_index.or | 116 |
| abstract_inverted_index.to | 54, 120, 142, 182 |
| abstract_inverted_index.we | 50, 139 |
| abstract_inverted_index.NLP | 79 |
| abstract_inverted_index.Our | 152 |
| abstract_inverted_index.The | 125 |
| abstract_inverted_index.and | 4, 69, 77, 85, 99, 135, 146 |
| abstract_inverted_index.are | 112 |
| abstract_inverted_index.how | 17 |
| abstract_inverted_index.key | 6 |
| abstract_inverted_index.our | 108 |
| abstract_inverted_index.ten | 93 |
| abstract_inverted_index.LLMs | 111, 134, 157, 172 |
| abstract_inverted_index.This | 168 |
| abstract_inverted_index.best | 109 |
| abstract_inverted_index.both | 133 |
| abstract_inverted_index.data | 3 |
| abstract_inverted_index.four | 58 |
| abstract_inverted_index.from | 8, 107, 122 |
| abstract_inverted_index.harm | 145 |
| abstract_inverted_index.have | 27 |
| abstract_inverted_index.into | 173 |
| abstract_inverted_index.more | 184 |
| abstract_inverted_index.text | 163 |
| abstract_inverted_index.that | 170 |
| abstract_inverted_index.this | 48 |
| abstract_inverted_index.vast | 1 |
| abstract_inverted_index.with | 74, 92 |
| abstract_inverted_index.(36%) | 118 |
| abstract_inverted_index.(45%) | 115 |
| abstract_inverted_index.LLMs, | 56 |
| abstract_inverted_index.apply | 51 |
| abstract_inverted_index.care. | 187 |
| abstract_inverted_index.could | 176 |
| abstract_inverted_index.eight | 55 |
| abstract_inverted_index.faced | 131 |
| abstract_inverted_index.focus | 183 |
| abstract_inverted_index.large | 23 |
| abstract_inverted_index.range | 40 |
| abstract_inverted_index.shown | 28 |
| abstract_inverted_index.study | 91 |
| abstract_inverted_index.tasks | 44 |
| abstract_inverted_index.their | 20, 35 |
| abstract_inverted_index.time. | 21 |
| abstract_inverted_index.types | 148 |
| abstract_inverted_index.(LLMs) | 26 |
| abstract_inverted_index.(NLP), | 34 |
| abstract_inverted_index.across | 165 |
| abstract_inverted_index.burden | 15 |
| abstract_inverted_index.cases, | 105 |
| abstract_inverted_index.either | 113 |
| abstract_inverted_index.errors | 141 |
| abstract_inverted_index.health | 10 |
| abstract_inverted_index.models | 25, 84 |
| abstract_inverted_index.notes, | 68 |
| abstract_inverted_index.reader | 90 |
| abstract_inverted_index.reveal | 81 |
| abstract_inverted_index.safety | 127 |
| abstract_inverted_index.study, | 49 |
| abstract_inverted_index.tasks. | 167 |
| abstract_inverted_index.tasks: | 62 |
| abstract_inverted_index.adapted | 110 |
| abstract_inverted_index.between | 83 |
| abstract_inverted_index.burden, | 179 |
| abstract_inverted_index.connect | 140 |
| abstract_inverted_index.diverse | 39 |
| abstract_inverted_index.ensuing | 126 |
| abstract_inverted_index.experts | 160 |
| abstract_inverted_index.imposes | 12 |
| abstract_inverted_index.medical | 123, 136, 144, 159 |
| abstract_inverted_index.methods | 53 |
| abstract_inverted_index.metrics | 80 |
| abstract_inverted_index.natural | 31 |
| abstract_inverted_index.patient | 65, 186 |
| abstract_inverted_index.promise | 29 |
| abstract_inverted_index.records | 11 |
| abstract_inverted_index.remains | 45 |
| abstract_inverted_index.summary | 96 |
| abstract_inverted_index.textual | 2 |
| abstract_inverted_index.Although | 22 |
| abstract_inverted_index.allocate | 19 |
| abstract_inverted_index.allowing | 180 |
| abstract_inverted_index.analysis | 128 |
| abstract_inverted_index.clinical | 42, 60, 89, 162, 174 |
| abstract_inverted_index.compared | 119 |
| abstract_inverted_index.distinct | 59 |
| abstract_inverted_index.evidence | 155 |
| abstract_inverted_index.experts, | 137 |
| abstract_inverted_index.experts. | 124 |
| abstract_inverted_index.language | 24, 32 |
| abstract_inverted_index.majority | 103 |
| abstract_inverted_index.methods. | 87 |
| abstract_inverted_index.multiple | 166 |
| abstract_inverted_index.progress | 67 |
| abstract_inverted_index.provides | 154 |
| abstract_inverted_index.reports, | 64 |
| abstract_inverted_index.research | 153 |
| abstract_inverted_index.spanning | 57 |
| abstract_inverted_index.suggests | 169 |
| abstract_inverted_index.superior | 117 |
| abstract_inverted_index.Analyzing | 0 |
| abstract_inverted_index.alleviate | 177 |
| abstract_inverted_index.dialogue. | 71 |
| abstract_inverted_index.evaluates | 95 |
| abstract_inverted_index.potential | 143 |
| abstract_inverted_index.radiology | 63 |
| abstract_inverted_index.semantic, | 76 |
| abstract_inverted_index.summaries | 106, 121 |
| abstract_inverted_index.unproven. | 46 |
| abstract_inverted_index.workflows | 175 |
| abstract_inverted_index.adaptation | 52, 86 |
| abstract_inverted_index.categorize | 147 |
| abstract_inverted_index.challenges | 130 |
| abstract_inverted_index.clinicians | 18, 181 |
| abstract_inverted_index.conceptual | 78 |
| abstract_inverted_index.electronic | 9 |
| abstract_inverted_index.equivalent | 114 |
| abstract_inverted_index.fabricated | 150 |
| abstract_inverted_index.highlights | 129 |
| abstract_inverted_index.physicians | 94 |
| abstract_inverted_index.processing | 33 |
| abstract_inverted_index.questions, | 66 |
| abstract_inverted_index.syntactic, | 75 |
| abstract_inverted_index.trade-offs | 82 |
| abstract_inverted_index.assessments | 73 |
| abstract_inverted_index.information | 7 |
| abstract_inverted_index.integrating | 171 |
| abstract_inverted_index.substantial | 14 |
| abstract_inverted_index.summarizing | 5 |
| abstract_inverted_index.Quantitative | 72 |
| abstract_inverted_index.conciseness; | 100 |
| abstract_inverted_index.correctness, | 98 |
| abstract_inverted_index.information. | 151 |
| abstract_inverted_index.completeness, | 97 |
| abstract_inverted_index.documentation | 178 |
| abstract_inverted_index.effectiveness | 36 |
| abstract_inverted_index.outperforming | 158 |
| abstract_inverted_index.summarization | 43, 61, 164 |
| abstract_inverted_index.doctor-patient | 70 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 19 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.97839899 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |