Literature DB >> 26265491

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes.

Niels Peek1, Carlo Combi2, Roque Marin3, Riccardo Bellazzi4.   

Abstract

BACKGROUND: Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998.
OBJECTIVES: To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future.
METHODS: We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers.
RESULTS: We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication.
CONCLUSIONS: There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Artificial Intelligence in Medicine; History of science; Literature review

Mesh:

Year:  2015        PMID: 26265491     DOI: 10.1016/j.artmed.2015.07.003

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  10 in total

1.  Data Mining for Biomedicine and Healthcare.

Authors:  Zhengxing Huang; Jose M Juarez; Xiang Li
Journal:  J Healthc Eng       Date:  2017-08-20       Impact factor: 2.682

Review 2.  Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?

Authors:  Brian Wahl; Aline Cossy-Gantner; Stefan Germann; Nina R Schwalbe
Journal:  BMJ Glob Health       Date:  2018-08-29

3.  Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation.

Authors:  Muhammad Arsalan; Muhammad Owais; Tahir Mahmood; Se Woon Cho; Kang Ryoung Park
Journal:  J Clin Med       Date:  2019-09-11       Impact factor: 4.241

4.  The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis.

Authors:  Bach Xuan Tran; Roger S McIntyre; Carl A Latkin; Hai Thanh Phan; Giang Thu Vu; Huong Lan Thi Nguyen; Kenneth K Gwee; Cyrus S H Ho; Roger C M Ho
Journal:  Int J Environ Res Public Health       Date:  2019-06-18       Impact factor: 3.390

5.  Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study.

Authors:  Stefanie Jauk; Diether Kramer; Alexander Avian; Andrea Berghold; Werner Leodolter; Stefan Schulz
Journal:  J Med Syst       Date:  2021-03-01       Impact factor: 4.460

Review 6.  Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging.

Authors:  Cai-Wei Yang; Xi-Jiao Liu; Si-Yun Liu; Shang Wan; Zheng Ye; Bin Song
Journal:  Contrast Media Mol Imaging       Date:  2020-11-26       Impact factor: 3.161

7.  Development of NLP-Integrated Intelligent Web System for E-Mental Health.

Authors:  Abid Hassan; M D Iftekhar Ali; Rifat Ahammed; Sami Bourouis; Mohammad Monirujjaman Khan
Journal:  Comput Math Methods Med       Date:  2021-12-13       Impact factor: 2.238

8.  Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening.

Authors:  Glauco Cardozo; Guilherme Brasil Pintarelli; Guilherme Rettore Andreis; Annelise Correa Wengerkievicz Lopes; Jefferson Luiz Brum Marques
Journal:  Biomed Res Int       Date:  2022-03-29       Impact factor: 3.411

9.  Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?

Authors:  Joshua Watson; Carolyn A Hutyra; Shayna M Clancy; Anisha Chandiramani; Armando Bedoya; Kumar Ilangovan; Nancy Nderitu; Eric G Poon
Journal:  JAMIA Open       Date:  2020-04-10

10.  Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques.

Authors:  Ali Hussain; Hee-Eun Choi; Hyo-Jung Kim; Satyabrata Aich; Muhammad Saqlain; Hee-Cheol Kim
Journal:  Diagnostics (Basel)       Date:  2021-05-04
  10 in total

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