AI is the driving force behind many of the technologies we rely on today, from voice assistants to personalized shopping recommendations to medical imaging analysis, but not all AI technologies are created equal. 

To gain a more robust understanding of current AI technologies–and the promise of future tech–it’s important to distinguish between specialized AI and general AI. Here’s what you need to know. 

What is general AI?

In everyday discussions, general AI refers to flexible AI systems, often powered by Large Language Models (LLMs), that are trained on vast, diverse datasets to perform a broad range of tasks. These models aim to mimic human-like versatility by generating text, answering questions, coding, and more, without being reprogrammed for each new challenge. However, they lack true human-level intelligence—often called Artificial General Intelligence or AGI, which remains a future goal.

No AI today achieves true AGI, but general-purpose LLMs are the closest we have to "jack-of-all-trades" AI. They excel in breadth but can falter in depth, especially for specialized tasks requiring precision, domain expertise, or handling of nuanced, industry-specific data.  Examples of general AI today include:

• ChatGPT and similar models from OpenAI, which are capable of reasoning, writing, coding, translating, generating creative content across various domains—but often produce generic or error-prone outputs when pushed into highly technical areas.

• Google Gemini, which combines language, vision, and other modalities to deliver multimodal responses, approximating more human-like responses. 

• Perplexity AI, which uses LLMs to provide conversational answers to queries with real-time web search integration and source citations, making it versatile for research and information gathering across topics. 

What is specialized AI?

Also known as narrow AI, specialized AI is technology designed to excel at a single task or narrow set of tasks with efficiency, accuracy and speed. Specialized AI models are trained on targeted, domain-specific datasets, allowing them to recognize intricate patterns, minimize errors, and deliver reliable results in their focused area. 

In many cases, specialized AI tools outperform general AI, because of their specificity and access to hyper-relevant data and algorithms. They achieve higher precision and reliability—crucial for tasks where mistakes can be costly. Here are a few examples of specialized AI:

• Voice assistants, like Siri, Alexa, and Google Assistant, which use speech recognition and natural language processing to complete tasks like setting reminders, answering questions, or playing music. 

• Facial recognition systems, like your phone’s Face ID or the airport security systems you experience when flying. 

• Medical imaging analysis, which augments doctors’ review of radiology imaging to detect signs of disease. 

• Traffic and navigation apps, like Google Maps and Waze, which use real-time traffic data and AI models to suggest optimal routes. 

What’s the best way to remember the difference between the two? 

Imagine you have a friend who’s really good at one specific thing—like fixing cars. They know everything about engines, tires, and brakes. If your car breaks down, they’re the person you call because they can figure out exactly what’s wrong and fix it perfectly. But if you ask them to cook a fancy dinner or paint your house, they might not be much help because that’s not their thing. 

Specialized AI is like that friend. It’s designed to be an expert at one job. For example, it might be great at recognizing faces in photos, translating languages, or recommending movies you’ll like. It’s trained on a ton of specific information for that one task, so it’s super accurate and fast at it. But if you try to use it for something totally different—like writing music or analyzing the weather—it probably won’t do a good job because it’s not built for that.

Now, picture another friend who’s pretty good at a bunch of things. They can fix a leaky faucet, cook a decent meal, and maybe even play a few songs on the guitar. They’re handy to have around because they can help with lots of little problems. But they’re not amazing at any one thing—they won’t fix your car as well as the car expert or cook as well as a chef.

General AI is like this “Jack of all trades.” It’s built to handle lots of different tasks—like answering questions, writing text, or even playing games. It’s flexible and can do a bit of everything, which makes it useful for general stuff. But because it’s not focused on just one thing, it’s not as precise or deep as a specialized AI when it comes to tough, specific challenges. If you ask General AI to perform specialized task, it will give you some results, but not very accurately. 

How does Revisto’s AI MLR solution fit in?

Revisto platform is a specialized AI tool that was purpose-built to solve the very specific challenges in the medical, legal, and regulatory (MLR) review process for life sciences companies. 

Unlike general-purpose LLMs, our proprietary AI is trained exclusively on MLR data, ensuring superior accuracy, contextual understanding, and compliance. Our algorithm understands the importance of formatting, presentation of texts, claims accuracy, required disclaimers, and other intricacies of the MLR process. 

In addition, it is capable of ingesting company SOPs, historical materials and assets, and other brand-specific knowledge, providing comprehensive analysis of marketing materials across all aspects of MLR reviews: editorial, brand guidelines, market and channel rules, claim substantiation, and fair balance. 

Even AI that is trained on broad regulations or medical and clinical data doesn’t have an understanding of MLR and therefore, can’t effectively support MLR reviewers like Revisto platform can. See it for yourself with a free demo.