In this section, I share a compilation of materials I have specially prepared for my conferences. It includes infographics, visualizations, charts, open‑access articles, semantic maps, and other resources on Artificial Intelligence that I have created for these forums. More content will be added soon.

Sections 

1. AI Overview: General statistics on the use of AI, based on the analysis of 288,800 articles.

2. How a Chatbot Works: An animation that explains how a chatbot functions.

3. 150+ Famous AI Tools: A dynamic visualization of well-known AI tools.

4. AI Tools by Category: A dynamic visualization grouping AI tools by category.

5. Notable AI Models: A dashboard showcasing AI models throughout history.

6. Artificial Intelligence and the Editorial Process: A curated dashboard featuring 53 open-access articles.

7. ChatGPT in Medicine: An explorable semantic map based on 2,448 articles.

8. Artificial Intelligence and Rheumatology: A dynamic presentation on the use of AI in rheumatology.

9. Artificial Intelligence and Education: A curated dashboard featuring 447 open-access articles.

10. Artificial Intelligence and Ethics: An interactive dashboard with 980 open-access articles and a semantic map.

11. Artificial Intelligence and Neuroscience: A visual dashboard with 483 open-access articles and an interactive semantic network.

Artificial Intelligence Model Types

This infographic shows different types of artificial intelligence (AI) concepts. I organized it as a hierarchy, starting with the broadest idea, Artificial Intelligence at the top, followed by more specific categories.

Mapping 288,800 articles on Artificial Intelligence

By Dr. Verónica Espinoza

The search was conducted using the open-access tool OpenAlex. The data collection was on Jan 5, 2026. The query was adjusted to filter and display only articles published between 2022 and 2025 that included the phrase "artificial intelligence" in the title or abstract. Additionally, retracted works were excluded, ensuring that only valid and up-to-date studies on artificial intelligence were included. The search results yielded a total of 288,800.

Below are various visualizations generated from this query, including the Top of Topics, Top of Publishers, and Publications by Country. The data were mapped using the Flourish tool.

Review the query here. (Note: The query count may vary depending on the data collection date)

In this animation, learn how a ChatBot works. Just click "Next Step" and discover each step from the moment you enter your instruction to when the ChatBot gives you a response.

This dataset provides an overview of 150+Famous AI Tools. I extracted the database from Kaggle [1] and then visualized it using the Flourish tool. I added a filter so you can select the tools by category. The size of the bars represents the votes users have given to each tool, that is, their popularity among users. You can hover over any tool you're interested in, and a tooltip will provide more information about it.

The following interactive dashboard is based on over 900 models that were state of the art, highly cited, or otherwise historically notable. The data was obtained from the website epoch.ai [1], which provides this open-access database. The data was then visualized in an interactive dashboard on Looker Studio for easy exploration.

As described on epoch.ai, the models were initially selected from various sources, including literature reviews, papers with code, historical accounts, previous datasets, highly-cited publications of top conferences, and suggestions from individuals. [1]  It also states that the dataset is focused on notable machine learning models. A notable model meets any of the following criteria: (i) state-of-the-art improvement on a recognized benchmark; (ii) highly cited (over 1000 citations); (iii) historical relevance; (iv) significant use [1].

The following interactive visualization is based on the titles of 2,448 open-access PubMed articles on ChatGPT and Medicine published between 2022 and 2025. The tool used was NocodeFunctions tool. Each node represents a concept, and its size corresponds to the frequency with which that concept appears in the text. The edges represent co-occurrences between nodes, meaning the number of times those concepts appear close to each other in the analyzed titles. The colors indicate thematic clusters, offering an overview of the main topics in this area. 

This section offers a dynamic presentation based on selected slides I presented at the PANLAR 2025 Congress in CDMX, Santa Fe. It includes an overview of artificial intelligence, covering its history, classification of model types, a semantic map of 167,000 academic articles, and social media analysis on AI in medicine. Additionally, it features an interactive network and a curated table of 60 open-access studies focused on AI in rheumatology.

▶To explore this presentation, click this link

This dashboard maps approximately 447 open-access articles on Artificial Intelligence in Education, created using Looker Studio. It includes article titles, years, citation counts, authors, and DOIs, along with filters for detailed exploration. The data was sourced from OpenAlex and includes open-access articles published between 2022 and 2025 using the search criteria: titles containing (AI OR “artificial intelligence”) AND Education. 

The following section includes an interactive dashboard featuring 980 open-access articles on ethics and AI, as well as a semantic map created from the abstracts of these articles. This section is based on data collected from OpenAlex, focusing exclusively on open-access articles published between 2022 and 2025. A total of 980 articles were retrieved using the following search parameters: title containing (AI OR “artificial intelligence”) AND ethics. The visualizations were created using Looker Studio, and the semantic network was generated with the Nocodefunction tool.

B) Semantic Map on Ethics of Artificial Intelligence

The following visualization is based on the abstracts of the articles from the previous dashboard. Each node represents a concept, and the size of the nodes corresponds to the frequency with which that concept appears throughout the text. The edges represent co-occurrences between nodes, that is, the number of times those nodes appear close to each other in the analyzed text. The colors indicate themes grouped by thematic similarities, providing an overview of the ongoing work in this area.

Some relevant themes and concepts observed in this semantic map are:

• Ethical and Societal Considerations: "ethics", "ethical dilemmas", "societal", "fairness", "transparency", "privacy", "trust", "regulation", "governance".

• Research and Development: "research", "development", "innovation", "methodology", "technical", "conceptual".

• Impact and Implications: "impact", "challenge", "risk", "benefit", "consequence", "problem", "solution".

The following section includes an interactive dashboard containing 483 open-access articles on "Neuroscience and Artificial Intelligence", as well as an interactive semantic map created from the abstracts of these articles. The articles were collected from PubMed within a date range from 2020 to 2025 using the query “AI”[Title] OR “Artificial Intelligence”[Title]) AND (Neuroscience[Title] OR Neuro[Title]) NOT “Editorial:”[Title]. The dashboard was created using Looker Studio, and the semantic network was generated using the Scimago Graphica tool.

B) Semantic Map on Neuroscience and  Artificial Intelligence

The following visualization is based on the abstracts of the articles from the previous dashboard. Each node represents a concept, and the size of the nodes corresponds to the frequency with which that concept appears throughout the text. The edges represent co-occurrences between nodes, that is, the number of times those nodes appear close to each other in the analyzed text. The colors indicate themes grouped by thematic similarities, providing an overview of the ongoing work in this area.

Some relevant themes and concepts observed in this semantic map are:

• Neuroimaging and Computational Methods:
  “image”, “neuroimage”, “segmentation”, “algorithm”, “MRI”, showing a strong focus on AI for brain image analysis.

• Clinical Diagnosis and Neurological Disorders:
  “diagnosis”, “clinical”, “neurological”, “disease”, “neurodegenerative”, reflecting applications of AI in medical decision-making and early detection.

• Neurointervention and Psychiatry:
  “neuromodulation”, “brain stimulation”, “psychiatry”, “pain”, emphasizing AI’s role in treatment optimization.

• Explainability and Cognitive Insights:
  “explainable”, “cognitive”, “human”, highlighting interest in interpretable models for clinical use.

• Oncology and Tumor Analysis:
  “oncology”, “tumor”, “spinal cord”, pointing to emerging AI applications in neuro-oncology.

▶ To interact with the network, click on the nodes of interest or use the zoom function to explore it in greater detail.

By Dr. Verónica Espinoza

All the sections presented are original research materials prepared by Dr. Veronica Espinoza for her conferences.

These are open‑access educational resources; however, they may not be edited, either in whole or in part. If you use these materials, proper credit must be given

Please, if you use the resources from this tab, cite them in the following way: 

Espinoza-González V. (n.d.). AI Resources. Veronica Espinoza. https://www.veronicaespinoza.org/ai-resources