There’s no denying that artificial intelligence is changing healthcare at a remarkable pace.
In just a few years, generative AI has evolved from an experimental technology to a practical tool used across healthcare and life sciences. Researchers use it to summarize scientific literature. Pharmaceutical companies use it to draft clinical trial materials. Hospitals use it to streamline documentation and patient communications. And—perhaps most importantly—patients themselves increasingly turn to AI-powered chatbots to better understand diagnoses, medications, and lab results.
As AI continues to transform healthcare communication, plain language will determine whether it succeeds. The technology is impressive, and its potential is enormous. But amid the excitement surrounding AI, one critical question often gets overlooked: Will people actually understand what AI produces?
More content does not always equal better communication
Every day, thousands of journal articles are published, clinical trials generate new data, regulatory agencies release guidance, and healthcare organizations create educational materials for patients and providers alike. But where information has always been abundant, understanding has historically been lacking.
Health literacy remains a significant public health issue. Many adults struggle to interpret medical terminology, understand treatment options, or navigate complex healthcare decisions. Even highly educated patients can become overwhelmed when faced with an unfamiliar diagnosis or emotionally charged medical situation.
Sure, artificial intelligence can generate content in seconds. But creating understanding takes real, thoughtful, and informed communication.
There’s no “AI” in “human”
One of AI’s greatest strengths is its ability to rewrite technical content into simpler language, and recent studies suggest that large language models can substantially improve the readability of patient-facing materials. In one proof-of-concept study, researchers found that AI tools reduced the reading level of FDA drug safety communications while largely preserving technical accuracy.
Other researchers have highlighted AI’s potential to increase access to plain-language medical information, improve health literacy, and help patients prepare for conversations with their healthcare teams. These are meaningful advances, but readability is only one component of effective communication, and AI does make mistakes.
Additionally, plain language is more than merely simplifying words. A sentence can be shorter without being clearer; a paragraph can be grammatically simple while still leaving readers confused about what matters most. When it comes to creating content that suits the needs of its readers, the key is to design information around the needs of the specific audience—context that AI does not always have, and cannot always imagine. In other words, the best science communication requires human understanding.
Communication requires judgment, not just translation
Imagine two versions of the same clinical trial summary. The first accurately describes the study’s endpoints, methodology, and statistical significance. The second explains why the study matters to someone considering participation, defines unfamiliar terminology, anticipates common questions, and provides context without sacrificing scientific accuracy.
Both are technically correct, but only one truly communicates. This is where human expertise becomes indispensable. Effective science communicators make decisions AI cannot reliably make on its own, such as:
- What does this audience already know?
- Which details are essential, and which create unnecessary cognitive burden?
- Where might readers misinterpret a finding?
- What emotional state is the audience likely experiencing?
- How do we simplify without oversimplifying?
These are editorial decisions that require empathy, context, and experience—actual lived experience.
AI is not coming for science communicators
The real risk is assuming that AI has already replaced the people behind science communication. Researchers have identified recurring concerns with AI-generated, patient-facing materials, including oversimplification, lack of transparency, inconsistent performance on complex medical questions—not to mention the potential to widen existing health inequities if AI-generated content isn’t carefully evaluated.
Successful implementation of AI requires ongoing oversight from healthcare professionals, patients, and communication experts—in other words, people. As AI accelerates content production, human review is and will be increasingly important.
Instead of spending hours writing first drafts, communicators can focus on higher-value work, such as verifying scientific accuracy, improving organization and readability, identifying ambiguous language, ensuring cultural and linguistic inclusivity, adapting content for different audiences, and maintaining a consistent organizational voice.
In reality, AI can and will change how communicators work—not why they are needed in the first place. Though our processes may shift, our purpose will not.
The key to trust has aways been clarity
The healthcare system is dependent on trust: patients must trust researchers and clinicians, participants must trust clinical trials, and communities must trust public health recommendations. Federal agencies have long recognized this principle. The FDA’s Plain Language initiative emphasizes that public-facing health information should be easy to find, understand, and use—not simply scientifically accurate.
The CDC likewise continues to emphasize health literacy and plain-language guidance as essential components of effective public health communication.
AI can help organizations communicate faster, but only people can determine whether those communications build true understanding.
The goal hasn’t changed
Though AI will help produce content faster, science communicators will ensure that information remains accurate, accessible, empathetic, and actionable. This partnership has the potential to improve patient education, increase participation in clinical research, strengthen public trust, and ultimately support better health outcomes.
Healthcare communication has always been about helping people make informed decisions during some of the most important and stressful moments of their lives. Artificial intelligence may change how we create content, but it should never change our commitment to creating understanding.
