The remarkable growth of generated data volumes in various fields of science, industry, and business over recent decades creates new challenges in information presentation and analysis. Biological research, medical imaging, genomics, climate studies, financial systems, and even social media generate millions of data points daily, and analyzing these data using conventional methods is time-consuming and sometimes impossible. This is where data visualization becomes one of the most important tools for data analysis and information transfer. The goal of data visualization is not only to produce informative plots but also to turn complex data into patterns that can be observed, analyzed, and interpreted easily.
From a cognitive science point of view, the human brain is more efficient at processing visual information than long lists of numbers. Studies have shown that humans can identify visual patterns, color differences, motion direction, point density, and spatial relationships in a fraction of a second, whereas extracting this information from numerical tables requires considerably more time and effort. Therefore, data visualization is one of the most effective analytical tools across various scientific disciplines. Here we will discuss using AI in Data Visualization.
Data Visualization: A Brief History
The origins of data visualization date back to the 18th and 19th centuries through the work of pioneers such as William Playfair, who introduced bar charts and pie charts, and Florence Nightingale, who developed statistical graphics for medical data analysis. During the 20th century, the development of computers and statistical software enabled researchers to create increasingly sophisticated visualizations. In the early 21st century, visualization libraries such as Matplotlib for Python and ggplot2 for R made high-quality, reproducible scientific visualization widely accessible.
The integration of artificial intelligence into data visualization began with expert systems and early machine learning techniques during the 1980s and 1990s. These approaches were capable of assisting users by recommending appropriate chart types and supporting exploratory data analysis. However, the major breakthrough occurred during the 2010s with rapid advances in deep learning, the increasing computational power of GPUs, and the explosive growth of big data, which enabled AI systems to analyze increasingly complex datasets.
During the 2010s, visualization platforms such as Tableau and Microsoft Power BI introduced intelligent recommendation features that automatically suggested suitable visualizations based on data characteristics. At the same time, deep neural networks were increasingly employed to identify hidden patterns, detect anomalies, and extract meaningful information from large datasets. Since the early 2020s, generative AI models have further transformed scientific visualization by generating images from natural-language descriptions, assisting in figure design, and supporting visual data interpretation. Meanwhile, AI has become increasingly integrated into scientific computing platforms, including AutoML frameworks, and has been widely adopted by organizations such as NASA and CERN to analyze and visualize large-scale astronomical and high-energy particle physics data.
AI Application in Data Visualization
With the rapid increase in data volume, humans are no longer able to analyze data efficiently without computational assistance. AI has transformed data visualization from a largely manual process into an intelligent and interactive one. Traditionally, analysts had to examine datasets, select the relevant variables, determine the most appropriate visualization, and then generate plots using specialized software. This process was highly dependent on the analyst’s experience and expertise and was often time-consuming and based on trial and error.
Today, AI automates many of these tasks. AI-based systems can analyze data structures, identify relationships among variables, and even recommend the most appropriate charts or visualizations for effectively communicating information. In other words, AI not only assists in creating visualizations but also supports the decision-making process by determining how information should be presented.
In general, AI contributes to data visualization in three major stages:
1- Data Processing:
In real-world applications, data are typically collected from multiple sources and often suffer from quality issues. Datasets may contain missing values, duplicate records, inconsistencies, or outliers. Traditionally, analysts had to address these problems manually, making the process both time-consuming and prone to human error. AI can automate many of these tasks. Machine learning algorithms can integrate data from multiple sources, identify important variables, detect outliers, handle missing data, and improve the overall quality of datasets.
2- Visualization:
AI can analyze the characteristics of a dataset and recommend the most appropriate visualization techniques. It can determine the type of data, distinguish between numerical and categorical variables, identify clusters or trends, and then suggest the visualization that best represents the underlying information.
3- Insight Generation:
In the past, visualization software mainly generated charts and graphs, leaving the interpretation to the user. Today, AI can analyze the generated visualizations and automatically extract meaningful insights. For example, it can identify declining sales trends over the past three months, detect anomalous observations, or reveal potential correlations between variables. Thus, AI systems do more than simply generate visual representations; they also interpret them and transform traditional static reports into intelligent decision-support systems.
AI Applications in Scientific Visualization
In scientific visualization, datasets are often high-dimensional, noisy, and large-scale. In such cases, AI has revolutionized the way scientific data are analyzed, interpreted, and visualized.
1- Pattern Discovery and Feature Extraction:
AI algorithms can identify hidden structures and relationships within complex datasets. Dimensionality reduction and clustering techniques project high-dimensional data into two- or three-dimensional spaces, making underlying patterns easier to recognize. For example, in biology, AI is widely used to visualize genomic data and protein interaction networks.
2- Automatic Visualization Generation:
AI-powered tools and generative models can create appropriate charts, figures, and scientific illustrations based on user prompts or data characteristics, reducing the need for manual design.
3- Image Enhancement and Interpretation:
AI improves the quality of scientific images through noise reduction, super-resolution reconstruction, image segmentation, and interactive visualization in virtual and augmented reality environments. For example, in astronomy, AI processes data from the James Webb Space Telescope to enhance and visualize distant galaxies.
4- Simulation and Prediction:
AI can simulate the dynamics of complex phenomena, such as black hole evolution, molecular interactions, and chemical reactions, while also visualizing predicted trends and future scenarios through interactive animations.
Challenges and Future Directions
Despite its remarkable capabilities, AI still has several important limitations in data visualization.
a) AI as a Black Box:
Users cannot always determine why AI recommends a particular visualization or reaches a specific conclusion. As a result, the decision-making process of many AI models lacks transparency.
b) Automation Bias:
Excessive reliance on automated systems may lead users to accept AI-generated recommendations without sufficient critical evaluation.
c) Data Bias:
If the training data contain biases, the generated visualizations and conclusions may also be biased or misleading.
d) Scalability and Complexity:
Although no-code and low-code AI tools have become increasingly popular, analyzing very large or highly complex datasets still requires domain expertise and advanced analytical skills.
Conclusion
Data visualization is not merely the process of creating charts; rather, it is the integration of statistics, visual design, communication, audience understanding, and data storytelling. Therefore, AI alone cannot produce effective visualizations without human knowledge and expertise.
The quality of data visualization depends not only on technological advances but also on three fundamental types of knowledge:
- Data literacy: understanding what the data represent, what conclusions can be drawn from them, and what limitations they have.
- Design literacy: understanding how visual elements such as color, layout, hierarchy, and chart selection can communicate information clearly and effectively.
- Domain knowledge: understanding which findings are scientifically or practically meaningful within a specific field, such as medicine, biology, engineering, or business.
AI can support each of these areas, but it cannot replace them. Rather than eliminating the role of designers and analysts, AI is transforming their role from manual executors into evaluators, decision-makers, and strategic guides.
Furthermore, AI can function as a conversational design assistant by engaging users in critical discussions about visualization choices. It can answer questions such as: Why is this chart not appropriate? How would the visualization change for a non-expert audience? or are the selected colors accessible to people with color vision deficiency? Such interactions create an active learning process through continuous dialogue rather than passive feedback. Consequently, AI has the potential to serve as a design mentor and critical assistant, helping users improve both the quality of their visualizations and their own visualization skills.
In the future, scientific visualization is expected to evolve toward AI systems capable of understanding research questions, automatically generating the most appropriate visualizations, extracting meaningful insights, and even recommending future experiments. Such intelligent systems may also facilitate the discovery of previously overlooked “dark data” and accelerate scientific discovery through more effective visualization and interpretation.
Source: https://doi.org/10.1021/acs.jchemed.4c00228
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