AI medical terminology: 10 key terms to understand

1. Agentic AI

An agentic AI system, or AI agent, can carry out certain tasks without human intervention, distinguishing it from a traditional AI system's reliance on human input. Agentic AI systems can handle complex decision-making processes and even make autonomous behavior changes.

AI agents are often trained using evolutionary algorithms and reinforcement learning.

Traditional AI, including tools like ChatGPT, relies on a human user to input a prompt to direct the tool's response within predefined constraints. In contrast, agentic AI systems are goal-oriented and can operate with a certain level of autonomy -- without a human. They don't rely on prompts and can target specific objectives with strategy and reasoning, adapting when necessary.

AI agents have shown promise in increasing efficiency in patient-facing healthcare communications. For example, AI agents could automate appointment reminder calls to patients and tackle other administrative tasks.

Agentic AI holds promise in healthcare, but such an autonomous tool naturally poses security, ethical and operational threats as human oversight gives way to a relatively self-sustaining type of tech.

2. Algorithmic bias

Algorithmic bias, also known as AI bias or machine learning bias, results from an algorithm being trained on an incomplete or poor-quality data set. When the training data is skewed or contains cognitive biases, those biases are transferred to the algorithm and its results.

For example, in healthcare, if an AI-enabled clinical decision-making algorithm is trained using only data from male patients or patients of only one race, it only produces results representative of those populations. Nondiverse clinical data sets can negatively affect health equity efforts.

The root of a biased algorithm often lies in the data it's built upon, so it's essential to identify cognitive, sample, automation and prejudice biases during data collection and preparation.

Maintaining unbiased algorithms requires due diligence at every machine learning development cycle stage, not just during data collection and preparation. Stewards of responsible AI should also consider the presence of bias during the development phase and after deployment.

Strategies to reduce algorithmic bias include identifying potential sources of bias, following an established framework to address bias and documenting how data is selected.

3. Artificial intelligence

AI systems learn from large amounts of data and use those lessons to make predictions. AI simulates human intelligence and promises to perform tasks efficiently and effectively.

AI tools are driven by algorithms, which act as instructions a computer follows to perform a computation or solve a problem.

Generally, there are four types of AI categorized based on functionality: reactive, limited memory machines, theory of mind and self-aware.

AI tools use predefined logic or rules-based learning to understand patterns in data using machine learning or neural networks to simulate the human brain and generate insights through deep learning.

AI models can use these learning approaches to engage in computer vision, a process for deriving information from images and videos. These models can also use natural language processing (NLP) to derive insights from text and generative AI (GenAI) to create content.

AI models can be classified as either explainable -- meaning users have some insight into the "how" and "why" of an AI tool's decision-making -- or black box, a phenomenon in which the tool's decision-making process is hidden from users.

Currently, all AI models are considered narrow or weak AI. These are tools designed to perform specific tasks within certain parameters. Artificial general intelligence, or strong AI, is a theoretical system under which an AI model could be applied to any task.

Much of the conversation around AI in healthcare is centered on currently realized AI -- i.e., tools that exist for practical applications today or in the very near future.

To understand health AI, one must have a basic understanding of data analytics in healthcare. Data analytics aims to extract useful information and insights from various data points or sources. In healthcare, information for analytics is typically collected from sources such as electronic health records (EHRs), claims data and peer-reviewed clinical research.

Analytics efforts often aim to help health systems meet a key strategic goal, such as improving patient outcomes, enhancing chronic disease management, advancing precision medicine or guiding population health management.

However, these initiatives require analyzing vast amounts of data, which is often time- and resource-intensive. AI presents a promising way to streamline the healthcare analytics process.

4. Black box AI

A black box AI system is one in which the system's decision-making process remains hidden from users. It's known for its lack of transparency and is the opposite of explainable, or white box, AI.

Black box AI's inner workings are a mystery to the user and its developers, as it provides an output without explaining how it reached that conclusion. For example, OpenAI's ChatGPT is a black box AI system.

Black box AI systems are typically trained on real-world data and refine their output as they receive more data. Common concerns include AI bias, lack of transparency and security challenges. Due to their nature, it's difficult to determine, validate and understand the source of the information a black box AI system provides.

In healthcare, understanding potential sources of AI bias is crucial to ensuring that an AI tool is equitable and accurate. As such, researchers have pushed for additional transparency and auditing frameworks for healthcare AI tools to increase their explainability.

5. Cognitive computing

Cognitive computing typically refers to systems that simulate human reasoning and thought processes to augment human cognition. Cognitive computing tools can help aid decision-making and assist humans in solving complex problems by parsing through vast amounts of data and combining information from various sources to suggest solutions.

Cognitive computing systems must be able to learn and adapt as inputs change, interact organically with users, remember previous interactions to help define problems, and understand contextual elements to deliver the best possible answer based on available information.

To achieve this, these tools use self-learning frameworks, machine learning, deep learning, NLP, speech and object recognition, sentiment analysis and robotics to provide users with real-time analyses.

Cognitive computing's focus on supplementing human decision-making power makes it promising for various healthcare use cases, including patient record summarization and acting as a medical assistant to clinicians.

6. Deep learning

Deep learning is a subset of machine learning that analyzes data to mimic how humans process information. Deep learning algorithms use artificial neural networks (ANNs) to imitate the brain's neural pathways.

ANNs use a layered algorithmic architecture, allowing insights to be derived from how data is filtered through each layer and how those layers interact. This enables deep learning tools to extract more complex patterns from data than their simpler AI- and machine learning-based counterparts. ANN inputs and outputs remain independent of one another.

Like machine learning models, deep learning algorithms can be supervised, unsupervised or somewhere in between. These are the four main types of deep learning used in healthcare:

• Deep neural networks. DNNs have a greater depth of layers. The deeper the DNN, the more data translation and analysis tasks can be performed to refine the model's output.
• Convolutional neural networks. CNNs are specifically applicable to visual data. With a CNN, users can evaluate and extract features from images to enhance image classification.
• Recurrent neural networks. RNNs generate insights using temporal or sequential data. These networks use information from previous layers' inputs to influence later inputs and outputs. RNNs are commonly used to address challenges related to NLP, language translation, image recognition and speech captioning. In healthcare, RNNs have the potential to bolster applications such as clinical trial cohort selection.
• Generative adversarial networks. GANs use multiple neural networks to create synthetic data instead of real-world data. Like other types of GenAI, GANs are popular for voice, video and image generation. GANs can generate synthetic medical images to train diagnostic and predictive analytics-based tools.

Recently, deep learning technology has shown promise in improving the diagnostic pathway for brain tumors.

7. Generative AI

GenAI tools use a prompt involving text, images, videos or other machine-readable inputs to generate new content. GenAI models are trained on vast data sets to create realistic responses to users' prompts.

GenAI tools typically rely on other AI approaches, like NLP and machine learning, to generate content that reflects the characteristics of the model's training data. There are multiple types of GenAI, including large language models, GANs, RNNs, variational autoencoders, autoregressive models and transformer models.

Since ChatGPT's release in November 2022, GenAI has garnered significant attention from stakeholders across industries, including healthcare. The technology has demonstrated considerable potential for automating specific administrative tasks.

EHR vendors are using GenAI to streamline clinical workflows, health systems are pursuing the technology to optimize revenue cycle management, and payers are investigating how GenAI can improve member experience. On the clinical side, researchers are assessing how GenAI could improve healthcare-associated infection surveillance programs.

Despite the excitement around the technology, healthcare stakeholders should be aware that GenAI can exhibit bias like other advanced analytics tools. Additionally, GenAI models can hallucinate by perceiving patterns that are imperceptible to humans or nonexistent, leading the tools to generate nonsensical, inaccurate or false outputs.

8. Machine learning

Machine learning is a subset of AI in which algorithms learn from patterns in data without being explicitly trained. Machine learning tools are often used to make predictions about potential future outcomes.

Unlike rules-based AI, machine learning techniques can use increased exposure to large, novel data sets to learn and improve their performance. The following machine learning categories benefit healthcare applications in different ways:

• Supervised learning. This method uses algorithms trained on labeled data -- data inputs associated with corresponding outputs -- to identify specific patterns. This helps the tool make accurate predictions when presented with new data.
• Unsupervised learning. This type of machine learning uses unlabeled data to train algorithms to discover and flag unknown patterns and relationships among data points.
• Semi-supervised learning. This method relies on a mix of supervised and unsupervised learning approaches during training.
• Reinforcement learning. This type of machine learning algorithm relies on a feedback loop for algorithm training. It uses labeled data inputs to take various actions, such as making a prediction, and generates an output. If the algorithm's action and output align with the programmer's goals, its behavior is reinforced with a reward. In this way, algorithms developed using reinforcement techniques generate data, interact with their environment and learn a series of actions to achieve a desired result.

These approaches to pattern recognition make machine learning particularly useful in healthcare applications such as medical imaging and clinical decision support.

9. Natural language processing

NLP is a branch of AI concerned with how computers process, understand and manipulate human language in verbal and written forms.

Using machine learning and text mining techniques, NLP is often used to convert unstructured language into a structured format for analysis, translate from one language to another, summarize information or answer a user's queries.

There are also two subsets of NLP:

• Natural language understanding. NLU addresses computer reading comprehension, focusing heavily on determining the meaning of a piece of text. NLU tools use a sentence's grammatical structure and intended meaning to help establish a structure for how the computer should understand the relationship between words and phrases. As a result, the tool can accurately capture the nuances of human language.
• Natural language generation. NLG helps computers write human-like responses. These tools combine NLP analysis with rules from the output language, such as syntax, lexicons, semantics and morphology, to choose how to phrase a response when prompted. NLG drives GenAI technologies like OpenAI's ChatGPT.

In healthcare, NLP can sift through unstructured data, such as EHRs, to support various use cases. To date, the approach has supported the development of a patient-facing chatbot, helped detect bias in opioid misuse classifiers and flagged contributing factors to patient safety events.

10. Synthetic data

Synthetic data -- an alternative to real-world data -- is artificially produced information used as a test data set when developing machine learning and deep learning models. All AI models rely on data to learn, but gathering accurate, real-world data at such a high volume can be challenging. Synthetic data solves this problem by offering a quicker, more cost-effective and secure alternative.

Synthetic data is created algorithmically, depending on the use case. Alternatively, real-world data can be challenging to validate and isn't customizable.

Additionally, synthetic data eases privacy concerns that might come with using real-world data. In healthcare, developers can use synthetic information that resembles real data without risking the exposure of protected health information.

Although synthetic data has many benefits, some experts argue that synthetic data isn't equipped to train medical machine learning models due to its potential for bias, data leakage and limitations in patient cohort generation.

Editor's note: This article was updated in April 2025 to reflect the latest terms in healthcare AI.

Jill McKeon has covered healthcare cybersecurity and privacy news since 2021.