Generative artificial intelligence (AI) is an artificial intelligence tool that creates new content, such as text, images, audio, video, computer code, and summaries, based on patterns it has learned from existing data. Unlike traditional AI systems that primarily classify information, detect patterns, or make predictions, generative AI can produce new content in response to a user's instructions, known as a prompt. For example, generative AI tools such as ChatGPT and Google Gemini can answer questions, summarize documents, draft emails, and help users analyze information.

 

Understanding generative AI

“Generative AI relies on sophisticated machine learning models called deep learning models algorithms that simulate the learning and decision-making processes of the human brain,” says IBM. “These models work by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users' natural language requests or questions and respond with relevant new content.”

What sets generative AI apart from other types of AI is its ability to create new content rather than simply analyze existing information or predict outcomes. As Microsoft explains, traditional AI typically performs predefined tasks, while predictive AI uses historical data to forecast future outcomes. Generative AI can produce original text, images, audio, video, and computer code in response to user prompts. It can also refine its output based on human feedback and instructions. For example, generative AI can create a marketing campaign, while predictive AI can estimate how successful that campaign might be.

 

How does generative AI work?

Generative AI uses deep learning models trained on large amounts of data to identify patterns and relationships and generate new content in response to user prompts. According to IBM, the process generally involves three phases:

Training

During training, a deep learning algorithm processes large volumes of data to learn patterns and relationships. For example, a language model may learn to predict the next word in a sentence by repeatedly completing text sequences and comparing its predictions with the original data. “The result of this training is a neural network of parameters, encoded representations of the entities, patterns and relationships in the data, that can generate content autonomously in response to inputs, or prompts,” writes IBM.

 

Tuning

Tuning occurs in two ways: fine-tuning and reinforcement learning with human feedback (RLHF). During fine-tuning, the model is fed “labeled data specific to the content generation application questions or prompts the application is likely to receive, and corresponding correct answers in the desired format.” During RLHF, “human users respond to generated content with evaluations the model can use to update the model for greater accuracy or relevance.” This process involves individuals 'scoring' various outputs generated in response to the same prompt. It can also be as straightforward as having people interact with a chatbot or virtual assistant by typing or speaking corrections to its responses.

 

Generation and evaluation

When a user enters a prompt, the model uses the patterns it has learned to generate a response. The output can then be evaluated and the application adjusted to improve its performance. Some systems also use retrieval-augmented generation (RAG), which allows them to retrieve information from external sources to supplement the model's existing knowledge. These processes enable generative AI tools to produce different types of content. However, their outputs are not always accurate. Generative AI can produce plausible-sounding but incorrect information, so users should verify important details before relying on the results.

 

Applications of generative AI in healthcare

Generative AI is being used to support both clinical care and the administrative work involved in running healthcare services. In a review titled Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency, the authors examined how the technology could be applied to treatment planning, medical imaging, research, and healthcare administration.

They found that in clinical settings, generative AI can help healthcare professionals review medical information, develop treatment plans, and identify potential health risks. It can also generate synthetic data for research, which may help researchers train AI models when real patient data is limited. In medical education, the technology can be used to develop training materials and simulated clinical scenarios.

Generative AI also has applications beyond direct patient care. It can assist with medical documentation, summarize patient records, and automate some administrative tasks. These uses may help reduce the time healthcare professionals spend on paperwork and allow more time for patient care.

However, the technology has limitations. AI-generated information can be inaccurate, and synthetic data may not fully reflect real patient populations.

 

How Paubox uses generative AI

Beyond clinical care and administration, generative AI can also help healthcare organizations protect sensitive information from email-based threats. Paubox uses generative AI in its Inbound Email Security solution to help detect phishing attempts, email spoofing, and other malicious messages. Traditional email filters often rely on predefined rules, known threat indicators, and suspicious keywords. However, Paubox uses generative AI to analyze emails in context, including their language, intent, and sender behavior to identify signs of potential threats. This can help detect sophisticated phishing attempts that appear to come from trusted contacts or organizations. The system can also provide explanations for why an email has been flagged, helping administrators understand the reasons behind a security alert.

See also: What makes Paubox's generative AI different from others

 

Best practices for using generative AI

In its guidance, Microsoft identifies six principles for responsible AI: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles provide a framework for developing and using AI systems responsibly.

  • Fairness: AI systems should treat people fairly and avoid producing biased results. Users and developers should assess AI outputs for unfair assumptions or discrimination, particularly when the results could affect people's opportunities or decisions.
  • Reliability and safety: AI systems should perform consistently and safely under the conditions for which they were designed. Outputs should be checked for accuracy, and systems should be tested to identify errors and potential risks before and after implementation.
  • Privacy and security: AI systems should protect personal information and remain secure against unauthorized access or misuse. Users should understand how their data is collected, processed, and stored and avoid sharing sensitive information with tools that lack appropriate safeguards.
  • Inclusiveness: AI should be designed to accommodate people with different needs, backgrounds, and abilities. Testing systems with diverse users can help identify barriers and ensure that the technology works for a wider range of people.
  • Transparency: Users should understand when they are interacting with AI, how it is being used, and what its limitations are. Clear explanations of AI-generated content and decisions can help people assess the information and decide when further verification is needed.
  • Accountability: Individuals and organizations should take responsibility for how AI systems are developed and used. This includes establishing clear policies, monitoring performance, addressing problems, and ensuring that appropriate people oversee important decisions.

Applying these six principles can help individuals and organizations make better use of generative AI while addressing potential risks and maintaining trust in the technology.

See also: HIPAA Compliant Email: The Definitive Guide (2026 Update)

 

FAQS

Will generative AI replace human workers?

Generative AI can automate certain tasks and change how people work, but its impact varies across occupations and industries. In many cases, it is used to assist workers with tasks rather than replace the need for human judgment, expertise, and oversight.

 

Does generative AI learn from every prompt?

Not necessarily. Whether prompts are stored or used to improve a model depends on the specific tool, its settings, and its privacy policy. Users should review the provider's data-handling practices rather than assume that information entered into an AI system will remain private or be used for training.