Discovering Topics and Trends in Artificial Intelligence Chatbots in Medicine: Using Latent Dirichlet Allocation Topic Modeling (Preprint) Article Swipe
M. Ni
,
Yun Xia Jiang
,
Ming Li
,
Xin Lin
,
Shao Hua Xu
,
Yunping Zhou
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.69983
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.69983
BACKGROUND With the widespread adoption of the internet and smart devices, chatbots have emerged as significant auxiliary tools for public health activities. Despite the increasing application of chatbots in the medical field, comprehensive assessments of research topics and trends in this area remain relatively scarce. OBJECTIVE This study analyzed the application topics of chatbot technology in the medical field and explored the trends of these topics across different time periods, various journals, and different countries. METHODS In this study, a bibliometric approach was used to systematically search the PubMed, CINAHL, Web of Science and Embase databases for literature on medicine and chatbots between 2004 and 2024. By applying Latent Dirichlet Allocation (LDA) topic modeling, the study identified and analyzed the thematic applications of chatbots in the medical field, and explored the temporal evolution of these topics as well as their distribution characteristics across journals and countries. RESULTS We ultimately identified 3,029 articles for analysis. Utilizing the Latent Dirichlet Allocation (LDA) topic modeling technique, we identified nine core topics from the abstracts: ChatGPT medical quiz accuracy research, digital healthcare support assistants, mental health intervention research, epidemic health conversation application research, cancer patient diagnosis and treatment care, artificial intelligence (AI) healthcare education potential research, natural language processing models, human-computer interaction emotion research, and AI reading assistance systems. This study also found that these topics have shown diverse developmental trajectories over time, reflecting the evolution of research interests. In addition, researchers from different journals and countries have shown significant differences in the topics they focus on. CONCLUSIONS This study analyzed the topic distribution, temporal trends, journal, and country distribution characteristics of chatbots in the medical field. The results revealed popular and less researched topics, as well as emerging directions and trends, providing researchers with a tool for rapid identification. These findings not only provide guidance for researchers in selecting research directions but also offer references for journals and countries in determining research priorities, formulating strategic plans, and promoting international collaborative research.
Related Topics
Concepts
Latent Dirichlet allocation
Topic model
Chatbot
CINAHL
Preprint
Computer science
Data science
Field (mathematics)
Health care
Thematic analysis
Medical education
Artificial intelligence
World Wide Web
Medicine
Social science
Psychological intervention
Political science
Qualitative research
Sociology
Nursing
Mathematics
Law
Pure mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.2196/preprints.69983
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405362205
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405362205Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2196/preprints.69983Digital Object Identifier
- Title
-
Discovering Topics and Trends in Artificial Intelligence Chatbots in Medicine: Using Latent Dirichlet Allocation Topic Modeling (Preprint)Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-12Full publication date if available
- Authors
-
M. Ni, Yun Xia Jiang, Ming Li, Xin Lin, Shao Hua Xu, Yunping ZhouList of authors in order
- Landing page
-
https://doi.org/10.2196/preprints.69983Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.2196/preprints.69983Direct OA link when available
- Concepts
-
Latent Dirichlet allocation, Topic model, Chatbot, CINAHL, Preprint, Computer science, Data science, Field (mathematics), Health care, Thematic analysis, Medical education, Artificial intelligence, World Wide Web, Medicine, Social science, Psychological intervention, Political science, Qualitative research, Sociology, Nursing, Mathematics, Law, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4405362205 |
|---|---|
| doi | https://doi.org/10.2196/preprints.69983 |
| ids.doi | https://doi.org/10.2196/preprints.69983 |
| ids.openalex | https://openalex.org/W4405362205 |
| fwci | 0.0 |
| type | preprint |
| title | Discovering Topics and Trends in Artificial Intelligence Chatbots in Medicine: Using Latent Dirichlet Allocation Topic Modeling (Preprint) |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11519 |
| topics[0].field.id | https://openalex.org/fields/32 |
| topics[0].field.display_name | Psychology |
| topics[0].score | 0.8795999884605408 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3202 |
| topics[0].subfield.display_name | Applied Psychology |
| topics[0].display_name | Digital Mental Health Interventions |
| topics[1].id | https://openalex.org/T12488 |
| topics[1].field.id | https://openalex.org/fields/32 |
| topics[1].field.display_name | Psychology |
| topics[1].score | 0.8690999746322632 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3207 |
| topics[1].subfield.display_name | Social Psychology |
| topics[1].display_name | Mental Health via Writing |
| topics[2].id | https://openalex.org/T12027 |
| topics[2].field.id | https://openalex.org/fields/33 |
| topics[2].field.display_name | Social Sciences |
| topics[2].score | 0.7509999871253967 |
| topics[2].domain.id | https://openalex.org/domains/2 |
| topics[2].domain.display_name | Social Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/3306 |
| topics[2].subfield.display_name | Health |
| topics[2].display_name | Social Media in Health Education |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C500882744 |
| concepts[0].level | 3 |
| concepts[0].score | 0.9415231943130493 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q269236 |
| concepts[0].display_name | Latent Dirichlet allocation |
| concepts[1].id | https://openalex.org/C171686336 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7471685409545898 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3532085 |
| concepts[1].display_name | Topic model |
| concepts[2].id | https://openalex.org/C2779041454 |
| concepts[2].level | 2 |
| concepts[2].score | 0.694457471370697 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q870780 |
| concepts[2].display_name | Chatbot |
| concepts[3].id | https://openalex.org/C2781145037 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5835410356521606 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q5011772 |
| concepts[3].display_name | CINAHL |
| concepts[4].id | https://openalex.org/C43169469 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5720937252044678 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q580922 |
| concepts[4].display_name | Preprint |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.5016534328460693 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C2522767166 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4977579414844513 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[6].display_name | Data science |
| concepts[7].id | https://openalex.org/C9652623 |
| concepts[7].level | 2 |
| concepts[7].score | 0.44409361481666565 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q190109 |
| concepts[7].display_name | Field (mathematics) |
| concepts[8].id | https://openalex.org/C160735492 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4257940948009491 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q31207 |
| concepts[8].display_name | Health care |
| concepts[9].id | https://openalex.org/C74196892 |
| concepts[9].level | 3 |
| concepts[9].score | 0.4213748574256897 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7781188 |
| concepts[9].display_name | Thematic analysis |
| concepts[10].id | https://openalex.org/C509550671 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3663933575153351 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q126945 |
| concepts[10].display_name | Medical education |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3469666540622711 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C136764020 |
| concepts[12].level | 1 |
| concepts[12].score | 0.325628399848938 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[12].display_name | World Wide Web |
| concepts[13].id | https://openalex.org/C71924100 |
| concepts[13].level | 0 |
| concepts[13].score | 0.23853805661201477 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[13].display_name | Medicine |
| concepts[14].id | https://openalex.org/C36289849 |
| concepts[14].level | 1 |
| concepts[14].score | 0.20791178941726685 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q34749 |
| concepts[14].display_name | Social science |
| concepts[15].id | https://openalex.org/C27415008 |
| concepts[15].level | 2 |
| concepts[15].score | 0.15915104746818542 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7256382 |
| concepts[15].display_name | Psychological intervention |
| concepts[16].id | https://openalex.org/C17744445 |
| concepts[16].level | 0 |
| concepts[16].score | 0.15409287810325623 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[16].display_name | Political science |
| concepts[17].id | https://openalex.org/C190248442 |
| concepts[17].level | 2 |
| concepts[17].score | 0.1430504024028778 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q839486 |
| concepts[17].display_name | Qualitative research |
| concepts[18].id | https://openalex.org/C144024400 |
| concepts[18].level | 0 |
| concepts[18].score | 0.1146683394908905 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q21201 |
| concepts[18].display_name | Sociology |
| concepts[19].id | https://openalex.org/C159110408 |
| concepts[19].level | 1 |
| concepts[19].score | 0.11121195554733276 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q121176 |
| concepts[19].display_name | Nursing |
| concepts[20].id | https://openalex.org/C33923547 |
| concepts[20].level | 0 |
| concepts[20].score | 0.10940581560134888 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[20].display_name | Mathematics |
| concepts[21].id | https://openalex.org/C199539241 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[21].display_name | Law |
| concepts[22].id | https://openalex.org/C202444582 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[22].display_name | Pure mathematics |
| keywords[0].id | https://openalex.org/keywords/latent-dirichlet-allocation |
| keywords[0].score | 0.9415231943130493 |
| keywords[0].display_name | Latent Dirichlet allocation |
| keywords[1].id | https://openalex.org/keywords/topic-model |
| keywords[1].score | 0.7471685409545898 |
| keywords[1].display_name | Topic model |
| keywords[2].id | https://openalex.org/keywords/chatbot |
| keywords[2].score | 0.694457471370697 |
| keywords[2].display_name | Chatbot |
| keywords[3].id | https://openalex.org/keywords/cinahl |
| keywords[3].score | 0.5835410356521606 |
| keywords[3].display_name | CINAHL |
| keywords[4].id | https://openalex.org/keywords/preprint |
| keywords[4].score | 0.5720937252044678 |
| keywords[4].display_name | Preprint |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.5016534328460693 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/data-science |
| keywords[6].score | 0.4977579414844513 |
| keywords[6].display_name | Data science |
| keywords[7].id | https://openalex.org/keywords/field |
| keywords[7].score | 0.44409361481666565 |
| keywords[7].display_name | Field (mathematics) |
| keywords[8].id | https://openalex.org/keywords/health-care |
| keywords[8].score | 0.4257940948009491 |
| keywords[8].display_name | Health care |
| keywords[9].id | https://openalex.org/keywords/thematic-analysis |
| keywords[9].score | 0.4213748574256897 |
| keywords[9].display_name | Thematic analysis |
| keywords[10].id | https://openalex.org/keywords/medical-education |
| keywords[10].score | 0.3663933575153351 |
| keywords[10].display_name | Medical education |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.3469666540622711 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/world-wide-web |
| keywords[12].score | 0.325628399848938 |
| keywords[12].display_name | World Wide Web |
| keywords[13].id | https://openalex.org/keywords/medicine |
| keywords[13].score | 0.23853805661201477 |
| keywords[13].display_name | Medicine |
| keywords[14].id | https://openalex.org/keywords/social-science |
| keywords[14].score | 0.20791178941726685 |
| keywords[14].display_name | Social science |
| keywords[15].id | https://openalex.org/keywords/psychological-intervention |
| keywords[15].score | 0.15915104746818542 |
| keywords[15].display_name | Psychological intervention |
| keywords[16].id | https://openalex.org/keywords/political-science |
| keywords[16].score | 0.15409287810325623 |
| keywords[16].display_name | Political science |
| keywords[17].id | https://openalex.org/keywords/qualitative-research |
| keywords[17].score | 0.1430504024028778 |
| keywords[17].display_name | Qualitative research |
| keywords[18].id | https://openalex.org/keywords/sociology |
| keywords[18].score | 0.1146683394908905 |
| keywords[18].display_name | Sociology |
| keywords[19].id | https://openalex.org/keywords/nursing |
| keywords[19].score | 0.11121195554733276 |
| keywords[19].display_name | Nursing |
| keywords[20].id | https://openalex.org/keywords/mathematics |
| keywords[20].score | 0.10940581560134888 |
| keywords[20].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.2196/preprints.69983 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.2196/preprints.69983 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5111291401 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | M. Ni |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ming Yue Ni |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5044931973 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Yun Xia Jiang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yun Xia Jiang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5100351387 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1341-5585 |
| authorships[2].author.display_name | Ming Li |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Ming Run Li |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5087180897 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6230-438X |
| authorships[3].author.display_name | Xin Lin |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Xin Lin |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5070045163 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Shao Hua Xu |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Shao Hua Xu |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5078084167 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-6132-7590 |
| authorships[5].author.display_name | Yunping Zhou |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Yun Ping Zhou |
| authorships[5].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://doi.org/10.2196/preprints.69983 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Discovering Topics and Trends in Artificial Intelligence Chatbots in Medicine: Using Latent Dirichlet Allocation Topic Modeling (Preprint) |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11519 |
| primary_topic.field.id | https://openalex.org/fields/32 |
| primary_topic.field.display_name | Psychology |
| primary_topic.score | 0.8795999884605408 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3202 |
| primary_topic.subfield.display_name | Applied Psychology |
| primary_topic.display_name | Digital Mental Health Interventions |
| related_works | https://openalex.org/W2888805565, https://openalex.org/W4312773271, https://openalex.org/W4315588616, https://openalex.org/W2769501189, https://openalex.org/W2962686197, https://openalex.org/W2207653751, https://openalex.org/W4293863151, https://openalex.org/W3159709618, https://openalex.org/W2611137333, https://openalex.org/W3005513013 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.2196/preprints.69983 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.2196/preprints.69983 |
| primary_location.id | doi:10.2196/preprints.69983 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.2196/preprints.69983 |
| publication_date | 2024-12-12 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 84, 300 |
| abstract_inverted_index.AI | 218 |
| abstract_inverted_index.By | 111 |
| abstract_inverted_index.In | 81, 242 |
| abstract_inverted_index.We | 154 |
| abstract_inverted_index.as | 15, 141, 143, 290, 292 |
| abstract_inverted_index.in | 29, 40, 58, 129, 254, 278, 313, 325 |
| abstract_inverted_index.of | 6, 27, 35, 55, 66, 96, 127, 138, 239, 276 |
| abstract_inverted_index.on | 103 |
| abstract_inverted_index.to | 89 |
| abstract_inverted_index.we | 170 |
| abstract_inverted_index.The | 282 |
| abstract_inverted_index.Web | 95 |
| abstract_inverted_index.and | 9, 38, 62, 75, 98, 105, 109, 122, 133, 149, 199, 217, 248, 272, 286, 295, 323, 332 |
| abstract_inverted_index.but | 317 |
| abstract_inverted_index.for | 19, 101, 159, 302, 311, 321 |
| abstract_inverted_index.not | 307 |
| abstract_inverted_index.on. | 259 |
| abstract_inverted_index.the | 3, 7, 24, 30, 52, 59, 64, 92, 119, 124, 130, 135, 162, 176, 237, 255, 266, 279 |
| abstract_inverted_index.was | 87 |
| abstract_inverted_index.(AI) | 204 |
| abstract_inverted_index.2004 | 108 |
| abstract_inverted_index.This | 49, 222, 263 |
| abstract_inverted_index.With | 2 |
| abstract_inverted_index.also | 224, 318 |
| abstract_inverted_index.area | 42 |
| abstract_inverted_index.core | 173 |
| abstract_inverted_index.from | 175, 245 |
| abstract_inverted_index.have | 13, 229, 250 |
| abstract_inverted_index.less | 287 |
| abstract_inverted_index.nine | 172 |
| abstract_inverted_index.only | 308 |
| abstract_inverted_index.over | 234 |
| abstract_inverted_index.quiz | 180 |
| abstract_inverted_index.that | 226 |
| abstract_inverted_index.they | 257 |
| abstract_inverted_index.this | 41, 82 |
| abstract_inverted_index.time | 71 |
| abstract_inverted_index.tool | 301 |
| abstract_inverted_index.used | 88 |
| abstract_inverted_index.well | 142, 291 |
| abstract_inverted_index.with | 299 |
| abstract_inverted_index.(LDA) | 116, 166 |
| abstract_inverted_index.2024. | 110 |
| abstract_inverted_index.3,029 | 157 |
| abstract_inverted_index.<sec> | 0, 47, 79, 152, 261 |
| abstract_inverted_index.These | 305 |
| abstract_inverted_index.care, | 201 |
| abstract_inverted_index.field | 61 |
| abstract_inverted_index.focus | 258 |
| abstract_inverted_index.found | 225 |
| abstract_inverted_index.offer | 319 |
| abstract_inverted_index.rapid | 303 |
| abstract_inverted_index.shown | 230, 251 |
| abstract_inverted_index.smart | 10 |
| abstract_inverted_index.study | 50, 120, 223, 264 |
| abstract_inverted_index.their | 144 |
| abstract_inverted_index.these | 67, 139, 227 |
| abstract_inverted_index.time, | 235 |
| abstract_inverted_index.tools | 18 |
| abstract_inverted_index.topic | 117, 167, 267 |
| abstract_inverted_index.</sec> | 46, 78, 151, 260, 337 |
| abstract_inverted_index.Embase | 99 |
| abstract_inverted_index.Latent | 113, 163 |
| abstract_inverted_index.across | 69, 147 |
| abstract_inverted_index.cancer | 196 |
| abstract_inverted_index.field, | 32, 132 |
| abstract_inverted_index.field. | 281 |
| abstract_inverted_index.health | 21, 188, 192 |
| abstract_inverted_index.mental | 187 |
| abstract_inverted_index.plans, | 331 |
| abstract_inverted_index.public | 20 |
| abstract_inverted_index.remain | 43 |
| abstract_inverted_index.search | 91 |
| abstract_inverted_index.study, | 83 |
| abstract_inverted_index.topics | 37, 54, 68, 140, 174, 228, 256 |
| abstract_inverted_index.trends | 39, 65 |
| abstract_inverted_index.CINAHL, | 94 |
| abstract_inverted_index.ChatGPT | 178 |
| abstract_inverted_index.Despite | 23 |
| abstract_inverted_index.PubMed, | 93 |
| abstract_inverted_index.Science | 97 |
| abstract_inverted_index.between | 107 |
| abstract_inverted_index.chatbot | 56 |
| abstract_inverted_index.country | 273 |
| abstract_inverted_index.digital | 183 |
| abstract_inverted_index.diverse | 231 |
| abstract_inverted_index.emerged | 14 |
| abstract_inverted_index.emotion | 215 |
| abstract_inverted_index.medical | 31, 60, 131, 179, 280 |
| abstract_inverted_index.models, | 212 |
| abstract_inverted_index.natural | 209 |
| abstract_inverted_index.patient | 197 |
| abstract_inverted_index.popular | 285 |
| abstract_inverted_index.provide | 309 |
| abstract_inverted_index.reading | 219 |
| abstract_inverted_index.results | 283 |
| abstract_inverted_index.scarce. | 45 |
| abstract_inverted_index.support | 185 |
| abstract_inverted_index.topics, | 289 |
| abstract_inverted_index.trends, | 270, 296 |
| abstract_inverted_index.various | 73 |
| abstract_inverted_index.accuracy | 181 |
| abstract_inverted_index.adoption | 5 |
| abstract_inverted_index.analyzed | 51, 123, 265 |
| abstract_inverted_index.applying | 112 |
| abstract_inverted_index.approach | 86 |
| abstract_inverted_index.articles | 158 |
| abstract_inverted_index.chatbots | 12, 28, 106, 128, 277 |
| abstract_inverted_index.devices, | 11 |
| abstract_inverted_index.emerging | 293 |
| abstract_inverted_index.epidemic | 191 |
| abstract_inverted_index.explored | 63, 134 |
| abstract_inverted_index.findings | 306 |
| abstract_inverted_index.guidance | 310 |
| abstract_inverted_index.internet | 8 |
| abstract_inverted_index.journal, | 271 |
| abstract_inverted_index.journals | 148, 247, 322 |
| abstract_inverted_index.language | 210 |
| abstract_inverted_index.medicine | 104 |
| abstract_inverted_index.modeling | 168 |
| abstract_inverted_index.periods, | 72 |
| abstract_inverted_index.research | 36, 240, 315, 327 |
| abstract_inverted_index.revealed | 284 |
| abstract_inverted_index.systems. | 221 |
| abstract_inverted_index.temporal | 136, 269 |
| abstract_inverted_index.thematic | 125 |
| abstract_inverted_index.Dirichlet | 114, 164 |
| abstract_inverted_index.Utilizing | 161 |
| abstract_inverted_index.addition, | 243 |
| abstract_inverted_index.analysis. | 160 |
| abstract_inverted_index.auxiliary | 17 |
| abstract_inverted_index.countries | 249, 324 |
| abstract_inverted_index.databases | 100 |
| abstract_inverted_index.diagnosis | 198 |
| abstract_inverted_index.different | 70, 76, 246 |
| abstract_inverted_index.education | 206 |
| abstract_inverted_index.evolution | 137, 238 |
| abstract_inverted_index.journals, | 74 |
| abstract_inverted_index.modeling, | 118 |
| abstract_inverted_index.potential | 207 |
| abstract_inverted_index.promoting | 333 |
| abstract_inverted_index.providing | 297 |
| abstract_inverted_index.research, | 182, 190, 195, 208, 216 |
| abstract_inverted_index.research. | 336 |
| abstract_inverted_index.selecting | 314 |
| abstract_inverted_index.strategic | 330 |
| abstract_inverted_index.treatment | 200 |
| abstract_inverted_index.Allocation | 115, 165 |
| abstract_inverted_index.abstracts: | 177 |
| abstract_inverted_index.artificial | 202 |
| abstract_inverted_index.assistance | 220 |
| abstract_inverted_index.countries. | 77, 150 |
| abstract_inverted_index.directions | 294, 316 |
| abstract_inverted_index.healthcare | 184, 205 |
| abstract_inverted_index.identified | 121, 156, 171 |
| abstract_inverted_index.increasing | 25 |
| abstract_inverted_index.interests. | 241 |
| abstract_inverted_index.literature | 102 |
| abstract_inverted_index.processing | 211 |
| abstract_inverted_index.references | 320 |
| abstract_inverted_index.reflecting | 236 |
| abstract_inverted_index.relatively | 44 |
| abstract_inverted_index.researched | 288 |
| abstract_inverted_index.technique, | 169 |
| abstract_inverted_index.technology | 57 |
| abstract_inverted_index.ultimately | 155 |
| abstract_inverted_index.widespread | 4 |
| abstract_inverted_index.activities. | 22 |
| abstract_inverted_index.application | 26, 53, 194 |
| abstract_inverted_index.assessments | 34 |
| abstract_inverted_index.assistants, | 186 |
| abstract_inverted_index.determining | 326 |
| abstract_inverted_index.differences | 253 |
| abstract_inverted_index.formulating | 329 |
| abstract_inverted_index.interaction | 214 |
| abstract_inverted_index.priorities, | 328 |
| abstract_inverted_index.researchers | 244, 298, 312 |
| abstract_inverted_index.significant | 16, 252 |
| abstract_inverted_index.applications | 126 |
| abstract_inverted_index.bibliometric | 85 |
| abstract_inverted_index.conversation | 193 |
| abstract_inverted_index.distribution | 145, 274 |
| abstract_inverted_index.intelligence | 203 |
| abstract_inverted_index.intervention | 189 |
| abstract_inverted_index.trajectories | 233 |
| abstract_inverted_index.collaborative | 335 |
| abstract_inverted_index.comprehensive | 33 |
| abstract_inverted_index.developmental | 232 |
| abstract_inverted_index.distribution, | 268 |
| abstract_inverted_index.international | 334 |
| abstract_inverted_index.human-computer | 213 |
| abstract_inverted_index.systematically | 90 |
| abstract_inverted_index.characteristics | 146, 275 |
| abstract_inverted_index.identification. | 304 |
| abstract_inverted_index.<title>METHODS</title> | 80 |
| abstract_inverted_index.<title>RESULTS</title> | 153 |
| abstract_inverted_index.<title>OBJECTIVE</title> | 48 |
| abstract_inverted_index.<title>BACKGROUND</title> | 1 |
| abstract_inverted_index.<title>CONCLUSIONS</title> | 262 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.30861495 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |