The recent rise of Large Language Models (LLMs) has marked a decisive step in the integration of artificial intelligence into the medical field. These tools, capable of generating, summarizing, or rephrasing natural language, are now emerging as powerful assets for decision support, research acceleration, and administrative task optimization.

In the face of these innovations, a major challenge remains: enabling healthcare professionals—even those without IT expertise—to understand the basic functioning, potential benefits, and limitations of these models. This first article in a series dedicated to the use of LLMs in healthcare aims to lay the foundations for a shared and accessible understanding.

Understanding What an LLM Is

A large language model is an artificial intelligence architecture trained on vast corpora of texts, with the goal of predicting the next word in a sentence based on its beginning. Relying on billions of parameters and occurrences, it generates plausible responses in fluent language.

The tool does not think like a human; it has neither consciousness nor understanding in the true sense. It manipulates linguistic sequences according to statistical rules learned during training. For example, it can summarize a scientific article, propose a list of differential diagnoses based on symptoms, or suggest wording suitable for a lay audience.

This ability to manipulate language paves the way for numerous applications in medical practice—provided that its technical and epistemological limits are well understood.

A Helpful Metaphor: The Expert Virtual Assistant

For healthcare professionals, a simple analogy can help better grasp how an LLM works. It can be likened to a medical assistant with encyclopedic memory, capable of rephrasing or explaining information, suggesting hypotheses, or summarizing complex documents.

But unlike a human, it has no awareness of the meaning of what it says. It simulates medical intelligence without truly embodying it. Its relevance therefore depends on human oversight, the quality of input data, and the context of its use.

In the medical setting, this metaphor helps position the LLM in its functional role: a complementary tool, not a replacement.

Clinical Illustration: Broadening Differential Reasoning

A concrete example illustrates the potential value of an LLM. In general medicine, faced with an atypical clinical picture—chronic abdominal pain, weight loss, fatigue—the physician may, after a physical examination and normal lab tests, reach a diagnostic dead end.

Querying an LLM with the case data (anonymized and carefully worded) may bring up rare hypotheses: Whipple’s disease, digestive endometriosis, neuroendocrine tumor. These suggestions in no way replace clinical expertise but can expand the scope of differential reasoning and guide further targeted investigations.

In such cases, the LLM becomes a tool for cognitive stimulation. It doesn’t decide—it proposes. It doesn’t judge—it explores.

The Fundamental Limits of Language Models

Using LLMs should not obscure their current limitations. The first is the lack of real understanding. The model has no actual knowledge of the real world: it generates coherent linguistic sequences without grasping their content. This can lead to what are known as “hallucinations”: false, invented answers presented with confidence.

Another major limitation is source traceability. In many cases, the answers generated by LLMs do not explicitly cite their origins. This complicates information verification, which is essential in a medical context.

Finally, biases in the training data remain a critical issue. The models reproduce the dominant representations of their training corpora, which can reinforce stereotypes or yield unreliable results in specific contexts (underrepresented populations, atypical clinical situations, etc.).

Contributions to Biomedical Research

One of the fields where LLMs are currently showing the most promise is research. In medicine, the increasing complexity of scientific literature makes comprehensive monitoring difficult. LLMs allow for the processing of large volumes of text quickly, with very useful organizational and rephrasing capabilities.

Literature Summarization

Used with caution, LLMs can generate thematic summaries from a set of articles, organize data based on clinical or methodological criteria, or identify contradictions between studies.

They facilitate the drafting of preliminary systematic reviews, enable initial screening of publications, and support the exploratory phase of a scientific problem.

Exploring New Correlations

Some models can cross-reference data from publications, genetic databases, or clinical results to bring to light poorly documented correlations. These automatically generated hypotheses can then be subjected to experimental validation.

This makes LLMs a useful idea-generation tool during the research project design phase—provided it is clearly understood that they are not proof systems.

Molecule Optimization

In the pharmaceutical field, LLMs are combined with biological simulation models to design new chemical structures, predict their properties, or refine existing therapeutic candidates. Their strength lies in the rapid generation of high-potential therapeutic variants.

Pharmacovigilance Monitoring

LLMs can also continuously analyze pharmacovigilance databases, patient forums, or scientific publications to detect rare or emerging adverse effects. This ability to process weak signals fits within a broader approach of early detection and health surveillance.

Key Takeaways

LLMs will not replace healthcare professionals, but they are profoundly changing how medical knowledge is accessed. Mastering them doesn’t require deep technical expertise, but rather a basic understanding of how they work, their actual contributions, and their limitations.

The growing influence of these tools calls for progressive adoption by caregivers, researchers, and decision-makers. The potential benefits are significant, but they will only be fully realized if LLM use remains grounded in the core values of medicine: rigor, caution, and humanity.