0933-3657
Published by: Elsevier
https://www.sciencedirect.com/journal/artificial-intelligence-in-medicine
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 90 | 244 | 256 | 45 |
Artificial Intelligence in Medicine explores disciplines such as Artificial intelligence, Machine learning, Pattern recognition, Data mining and Artificial neural network. Artificial intelligence and Natural language processing are closely related fields of research discussed in it. Some problems in Machine learning that were presented in Artificial Intelligence in Medicine overlapped with concepts under Classifier (UML), Decision support system and Task (project management).
The Pattern recognition study featured in it draws parallels with the field of Feature (computer vision). The work on Deep learning addressed in it expands to the thematically related Convolutional neural network.
The most cited papers investigate studies in Artificial intelligence, Machine learning, Data mining, Pattern recognition and Artificial neural network. While Data mining is the focus of the published papers, it also provides insights into the studies of Cross-validation, Medical diagnosis and Feature vector. The published papers explore research in Receiver operating characteristic and overlapping concepts in Logistic regression to expand the discourse in Pattern recognition.
Artificial Intelligence in Medicine facilitates discussions on Artificial intelligence, Deep learning, Pattern recognition, Machine learning and Convolutional neural network. Artificial Intelligence in Medicine holds forums on Artificial intelligence that merges themes from other disciplines such as Context (language use) and Natural language processing. The journal explores topics in Deep learning which can be helpful for research in disciplines like Field (computer science), Systematic review, Task (project management) and Identification (information).
In addition to Pattern recognition research, Artificial Intelligence in Medicine aims to explore topics under Contextual image classification and Regularization (mathematics). It concentrated on Machine learning research, specifically Decision tree, Interpretability, Predictive modelling, Transfer of learning and Missing data. The research on Segmentation featured in it combines topics in other fields like Encoder and Medical imaging.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Artificial Intelligence in Medicine (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Artificial Intelligence in Medicine (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 7.41% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.40% were posted by at least one author from the top 10 institutions publishing in the journal. Another 2.40% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 8.00% of all publications and 79.20% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Artificial intelligence has immensely influenced the healthcare sector, leading to transformative changes in different areas. For instance, in genetics research, AI has been employed to comprehend genetic variations associated with diseases, heralding new paths in genomics. Radiologists have used it to reduce diagnosis errors, particularly in the field of breast cancer detection.
Despite the vast areas AI has been used in medicine, there remain untouched vistas of opportunities. As advanced as AI is today, it can benefit from further development to increase accuracy and ability to handle larger and more complex datasets. Three significant areas are poised for exploration in the future:
Firstly, improving the interpretability of AI systems particularly in machine learning, can enhance their potentialtial, making them more applicable to everyday medical practice. Secondly, exploring AI's predictive analytics will lay the groundwork for predictive and preventative medicine. Lastly, advancing research in natural language processing (NLP) can greatly improve understanding and interactions between humans and AI.
Aspiring researchers interested in venturing into AI and medicine can gain insights and strategies for career development from seasoned professionals. For instance, a preschool teacher assistant interested in transitioning into AI research within medicine can start by understanding the requirements and pathway of such a career shift. Learn more about it {anchor}.
To sum up, AI has significantly improved the delivery and accessibility of healthcare. The potential of AI in medicine is vast and researchers around the globe continue to push the boundaries to discover new applications. It is an exciting time to be a part of this ever-evolving field.
Salome Kazeminia;Christoph Baur;Arjan Kuijper;Bram van Ginneken
(2020)Antonio Coronato;Muddasar Naeem;Giuseppe De Pietro;Giovanni Paragliola
(2020)Marta P. B. Fernandes;Susana M. Vieira;Francisca Pais Leite;Carlos Palos
(2020)Linfeng Li;Peng Wang;Jun Yan;Yao Wang
(2020)Oh Shu Lih;V Jahmunah;Tan Ru San;Edward J Ciaccio
(2020)Jing Zhang;Aiping Liu;Min Gao;Xiang Chen
(2020)Scott McLachlan;Kudakwashe Dube;Graham A Hitman;Norman E Fenton
(2020)Exploring computer science in the USA opens doors to some of the most lucrative careers today. For students seeking degrees that combine high earning potential with quality education, checking out the most lucrative majors offers valuable insights into fields with strong salary prospects.
Cost is another key consideration for many learners. Fortunately, there are affordable options online that allow students to earn reputable degrees without breaking the bank. Discovering the cheap online colleges is a great start for budget-conscious students eager to study computer science remotely.
Application fees can add up quickly, but some institutions have removed this barrier altogether. For those who want to simplify the admissions process and save money upfront, exploring online colleges no application fee is recommended.
Additionally, accelerated programs have gained popularity among busy professionals and motivated students. Fast track degrees are designed to help learners complete their studies in less time while maintaining academic rigor. Learn more about these programs on the page outlining the fast track degrees available in America.
