Artificial Intelligence (AI) is often dismissed as simply “computers that think like humans,” but it’s actually a much broader term encompassing technologies and mechanisms that perform data-driven processes like inference, prediction, classification, and generation.
In recent years, the term AI has become incredibly familiar. From text generation and image creation to search, meeting minutes, and medical support tasks, some people likely encounter it daily.
However, AI is not an entity that automatically provides correct answers to everything. Its essence lies in mechanisms that return plausible outputs based on inputs.
With this premise in mind, we will organize “the definition of AI,” “how it works,” and “approaches to its application” in a way that connects directly to current practical use.
What is AI?
We will clearly explain the development background behind why current AI has spread so rapidly.
Definition of AI
Artificial intelligence aims to perform sophisticated reasoning accurately on vast amounts of knowledge data.
Source: The Japanese Society for Artificial Intelligence, “Statement of Purpose for the Establishment of the Japanese Society for Artificial Intelligence”
The key point here is that AI is not an “entity possessing human-like will,” but rather a mechanism that returns outputs aligned with its purpose based on inputs (data). Outputs take forms such as: for images, “This is likely a dog”; for text, “Generate the next probable sentence”; for numbers, “Predict future values.”
In other words, AI is less a universal brain and more akin to a toolbox designed to perform specific tasks with high probability.
Background of AI Development
AI did not emerge suddenly but evolved through cycles of research and booms. Broadly speaking, the progression leading to current AI follows these phases:
First Boom (1950s-60s): Focus on reasoning and search
Second Boom (1980s): Focus on knowledge representation and expert systems
Third Boom (2010s-present): Focus on machine learning, particularly deep learning
During the first boom, computers gained the ability to reason and search, enabling them to solve problems like maze navigation, puzzles, and chess. Amid the Cold War, expectations were high for machine translation, but these systems proved incapable of handling the complex challenges of the real world. Their limitations became apparent, leading to a period of stagnation.
The Second Boom saw the emergence of “knowledge base” AI, which described human knowledge in a form computable by machines. Expert systems, capable of reasoning like specialists, became widespread. Japan also pursued its “Fifth Generation Computer” project, but the need for humans to input vast amounts of knowledge limited its practical applications.
The third boom, leading to the present, accelerated dramatically with the development of “machine learning,” where AI learns using big data, and the emergence of “deep learning,” which can automatically extract features. AI became capable of discovering its own rules from vast amounts of image, audio, and text data. This led to a leap in recognition and prediction technologies, paving the way for today’s generative AI.
This has propelled AI beyond the laboratory into the “practical application” phase, where it is now used in search, translation, speech recognition, image recognition, robotics, autonomous driving, and more.
How AI Works
Understanding AI’s mechanism is easier when broken down into two main phases: the Training phase and the Inference phase.
- Training Phase: Acquiring patterns from past data
- Inference Phase: Inputting new data into a trained model to return probabilities or classification results
This chapter introduces the core mechanisms underpinning AI: Machine Learning, Deep Learning, and Neural Networks.
Machine Learning, Deep Learning, and Neural Networks are the core technologies used both during the learning phase to “build models” and during the inference phase to “produce results.” We will now examine the specific roles each plays in learning and inference.
Machine Learning
Machine learning is a technology that enables computers to learn features from data and perform classification or prediction tasks. For example, a model trained on a large number of cat images can identify even unfamiliar cat images by estimating their “cat-like characteristics.”
In traditional machine learning, a key factor affecting accuracy was “which aspects to consider as features.” Here, features refer to quantifiable aspects like “contours,” “colors,” or “patterns” in images, generally called feature vectors. Classical methods often required humans to devise or select these feature vectors, which was a significant challenge.
Machine learning learning methods (learning formats) are typically categorized into the following three types:
Supervised learning: Learns using correct answer labels (e.g., creating a classification model using images labeled as dog/cat)
Unsupervised Learning: Grouping similar items without provided labels (e.g., automatically clustering patient attributes or behavioral data)
Reinforcement Learning: Learning actions that maximize “scores (rewards)” through trial and error
Deep Learning
Deep learning is a particularly advanced technique within machine learning and represents the current state of AI technology. Its key difference lies in the model’s ability to progressively learn feature extraction from data itself, a task traditionally handled by humans.
Using image recognition as an example, it starts by capturing simple elements like edges (contours), then moves to parts like eyes and ears, and further to faces and overall shapes… the idea is that it automatically builds up “clues for recognition” in multiple stages. As a result, accuracy tends to improve with more data trained, leading to practical applications in many fields like image recognition, speech recognition, and natural language processing.
However, it is not a panacea. As training data increases, so does the likelihood of noise (unwanted information) being introduced. Therefore, in practical applications, improving data quality (through labeling, preprocessing, adjusting training conditions, etc.) enhances accuracy and stability.
Neural Networks
The foundation of deep learning is the neural network. This is a mathematical model inspired by the neural circuits (neurons) in the human brain, processing information from input to output through multiple layers (input layer, hidden layers, output layer). It is called “deep learning” because it has multiple hidden layers (i.e., it is deep).
Within each layer, inputs are weighted, and information exceeding a certain threshold passes to the next layer. This process ultimately yields outputs like “probability of being a dog.”
However, it’s important to note that as the structure becomes more complex, it becomes harder for humans to intuitively understand “why that conclusion was reached” (the black box problem). Therefore, in medical and healthcare contexts, operational design incorporating explainability and verification processes becomes crucial.
What Generative AI Excels At and Struggles With
To effectively utilize generative AI, it’s crucial to understand its strengths and limitations rather than relying on it for everything.
While generative AI is highly reliable for tasks like refining text or organizing information, caution is essential when it comes to judgments requiring evidence or scenarios demanding the accuracy of up-to-date information.
Here, we outline its strengths and weaknesses based on common practical use cases.
Strengths
text,” “summarizing concisely,” and “organizing clearly.” It’s better suited for generating a rough draft for human refinement than for building concepts from scratch.
• Drafting: Quickly assembles text from scratch
• Summarization: Extracts key points from lengthy texts
• Paraphrasing: Simplifies technical language into plain terms
• Structuring: Excels at organizing content (e.g., bullet points → paragraphs, paragraphs → tables)
• Argument organization: Helps establish logical sequencing
• Gap detection: Identifies missing elements
• Template creation: Rapidly generates standard documents like notices or emails
• FAQ creation: Can structure questions and answers
• Template creation: Can draft initial versions for consent forms, etc.
Areas of weakness (caution points)
On the other hand, generative AI does not automatically guarantee “accuracy” or “evidence” even if the text sounds natural. It’s safest to use it with the understanding that human verification is always required, especially for content needing fact-checking or highly individualized information.
• Unsubstantiated assertions: May make definitive statements that sound plausible
• Incorporation of misinformation: Can be confident yet incorrect
• Weak with the latest information: May fail to reflect updates or changes
• Weak with specific rules: Prone to misunderstanding internal policies
• Weak with individual circumstances: Difficult to accurately grasp each patient’s background
• Poor at citing sources: Cannot automatically guarantee evidence
• Cannot make responsible judgments: Final decisions require human oversight
Latest AI Trends and Use Cases
According to the Ministry of Internal Affairs and Communications’ FY2025 survey, 49.7% of Japanese companies reported having established “policies for utilizing generative AI,” an increase from the previous year. However, many small and medium-sized enterprises still lack clear utilization policies, showing disparities in AI adoption readiness across industries and company sizes.
出典：総務省 「第Ⅰ部 特集 広がりゆく「社会基盤」としてのデジタル」
Source: Ministry of Internal Affairs and Communications, “Part I: Special Feature: Digital as an Expanding ‘Social Infrastructure’”
Nevertheless, AI is expected to deliver benefits across a wide range of areas beyond just improving operational efficiency. These include compensating for labor shortages, enhancing quality control, and improving safety. Many companies are already proceeding with partial implementations.
Here, based on these latest AI trends, we introduce how AI is being utilized across various industries, accompanied by specific case studies.
Manufacturing
In manufacturing, autonomous robots equipped with AI enable long-hour continuous operation, advancing labor reduction on production lines. In particular, the automation of defective product inspection using image recognition AI is expanding, achieving improved accuracy in quality checks and reduced inspection burdens. Furthermore, inventory optimization based on demand forecasting enables the reduction of excess inventory. Furthermore, AI safety monitoring systems detect dangerous actions and suspicious movements, strengthening safety management and theft prevention. This technology is gaining attention as a solution to address labor shortages and the challenge of transferring technical skills.
Agriculture
In agriculture, efforts are advancing to automate harvesting tasks, traditionally reliant on manual labor, using AI-equipped robots. AI predicts harvest yields, stabilizing shipment volumes and contributing to maintaining trust with business partners. Additionally, AI image recognition detects pests, enabling targeted pesticide application only where needed, reducing costs and improving safety. Furthermore, AI cameras assess crop size and shape, automatically sorting non-standard items and helping standardize quality.
Real Estate
In the real estate industry, AI supports appraisal price calculations and automatically proposes optimal properties based on conditions like desired area, budget, and floor plan, streamlining customer service and search operations. This not only reduces employee workload but also enables smoother property searches for users. Furthermore, in large facilities, AI security cameras detect crowd levels and suspicious behavior, contributing to the early detection of accidents and incidents. This technology is gaining attention for simultaneously addressing labor shortages and enhancing security.
Healthcare
In healthcare, AI-powered image recognition is advancing diverse clinical support functions, including early cancer detection, patient anomaly detection, medical record analysis, and infectious disease prediction. Its ability to rapidly analyze vast datasets helps prevent missed diagnoses and improves diagnostic accuracy. Furthermore, in nursing facilities, AI-based monitoring and care plan creation support have been introduced, contributing to reducing the burden on staff.
Additionally, AI utilization is accelerating in specialized fields, such as enabling the creation of precise treatment plans in orthodontics.
To Make AI “Practical for Clinical Use,” Purpose-Built Tools Are the Shortcut
As we’ve seen, while AI applications are expanding, it is not a panacea. That’s precisely why, in clinical settings, AI designed specifically for workflow integration tends to yield more direct results than adapting generic AI through workarounds.
For example, cephalometric analysis—critical in orthodontics—holds high potential for patient explanations. However, as a specialized field, it presents a significant hurdle for general dentists. This is where DIP Ceph, a cloud-based AI cephalometric analysis system, comes in. It was developed with the dual philosophy of enabling “scientific treatment explanations” and “streamlining analysis.”
Furthermore, its developer, Dental Brain Inc., views true dental digitization as “streamlining operations,” advocating for reduced burden, improved quality, and data-driven dental care.
As a starting point for gathering information and considering AI adoption, please also review the following page.