Large language models in biomedicine and healthcare: applications, challenges, ethics, and the path to clinical impact

Large language models in biomedicine and healthcare: what matters now

Healthcare runs on text and signals: notes, labs, images, genomics, and the endless stream of literature. Large language models (LLMs) give us a way to read, summarize, and reason across it at speed. When used well, they cut clicks, surface insights, and help teams make better calls faster.

The catch: privacy, bias, and workflow fit. You don't need a moonshot. You need clear use cases, strong guardrails, and tight integration with clinical systems. Below is a practical guide-what LLMs do well today, where they struggle, and how to implement safely.

Where LLMs deliver value today

Genomics

• Variant effect prediction: Models like Enformer and GPN learn long-range genomic interactions and predict how variants influence gene expression. Useful for prioritizing variants in rare disease workups and research pipelines.
• Cis-regulatory region mapping: DNABERT and Nucleotide Transformer learn motifs and context to score promoters, enhancers, and splice sites-even in low-data settings.
• DNA-protein interactions: Self-supervised models (e.g., MoDNA, GROVER) predict transcription factor binding and regulatory elements directly from sequence.

Transcriptomics

• Cell-type annotation: scBERT, Geneformer, and scGPT transfer knowledge from millions of cells to label new datasets and expose rare states.
• Batch effect correction: tGPT and SCimilarity create unified embeddings that travel across studies, aiding atlas-scale queries.
• Perturbation prediction: scGPT forecasts gene and drug responses by modeling gene-gene interactions; promising for target discovery and hypothesis testing.
• Spatial labels: Nicheformer blends single-cell and spatial data to predict niches and regions, supporting tissue mapping and tumor microenvironment studies.

Proteomics

• Structure prediction: ESM-style models, MSA Transformer, and ProLLaMA push sequence-to-structure accuracy and support protein engineering workflows.
• Function annotation: FAPM and ProteinChat link sequences and 3D embeddings to Gene Ontology terms and catalytic activities, helpful when homologs are scarce.
• Protein-protein interactions: ProLLM uses a "chain of thought" strategy to reason about signaling pathways and predict interaction pairs.

Drug discovery

• De novo generation: MolFM and TGM-DLM generate valid, property-aligned molecules guided by text prompts and diffusion over SMILES tokens.
• Drug-target interactions (DTIs): DTI-LM and DLM-DTI improve warm- and cold-start DTI predictions with compact architectures that run on modest hardware.
• Compound screening: Generalist therapeutic LLMs (e.g., Tx-LLM) unify molecules, proteins, and text to triage libraries and suggest repurposing leads.

Biomedical informatics

• Question answering: Retrieval-augmented systems like BioRAG reason over millions of papers to answer clinical or research questions with citations.
• Summarization: BioMedLM-level models condense notes, trials, and guidelines; reader studies often prefer their summaries for completeness and clarity.
• Clinical decision support (early signals): Studies in precision oncology show LLMs can suggest options that complement specialist judgment-useful as a second set of eyes, not a final arbiter.

Implementation playbook for healthcare teams

• Start with a narrow, high-friction task: discharge summary drafts, prior-auth letter drafts, tumor board evidence packs, variant triage notes, or trial-matching candidate lists.
• Keep data private: use de-identified corpora for training; run inference in a protected environment; contractually ban secondary data use.
• Build on RAG: retrieve from guidelines, pathways, formularies, institutional policies, and local order sets so the model cites what your clinicians trust.
• Wire into existing systems: FHIR, SQL, or graph backends as tools; let the model read but not write without review. Keep audit logs.
• Human-in-the-loop: clinicians approve outputs; require rationale and sources; mandate uncertainty flags.
• Measure what matters: time saved, error rates, rework, guideline adherence, and patient outcomes. Sunset what doesn't move the needle.
• Iterate with guardrails: prompt templates, content filters, PHI redaction, and fallbacks when confidence is low.

Data governance, bias, and safety

• Privacy by design: de-identify training data; restrict PHI exposure; enforce least-privilege access. HIPAA obligations still apply. See HHS HIPAA Privacy Rule.
• Bias checks: stratify metrics by sex, age, race/ethnicity, language, insurance, and site. Track drift over time and retrain with counterfactuals where feasible.
• Transparency: require citations, highlight uncertainty, and expose which data sources were used. Keep versioned prompts and models.
• Regulatory awareness: for patient-facing or device-like use, review the FDA's direction on AI/ML-enabled medical devices: FDA guidance portal.
• Incident response: define pathways to report harmful outputs, misclassification, or data leakage; pause and patch quickly.

Technical choices that matter

• RAG vs. fine-tuning: RAG is safer for fast updates and institutional specificity; fine-tune when you need stable style or domain reasoning.
• Structured data: encode labs, meds, and vitals as prompts; or let the model call tools (FHIR Server, SQL, graph) for up-to-date values.
• Efficiency: use LoRA/QLoRA, 4-8 bit quantization, and domain adapters to cut compute and cost.
• Evaluation: blend automated metrics (factuality, citation accuracy) with expert review. For research tasks, pre-register evaluations to reduce hindsight bias.
• Security: isolate workloads, scrub prompts/outputs for PHI, and disable training on user data by default.

Known limitations (plan around these)

• Data scarcity and bias: rare diseases, pediatric cohorts, and underrepresented populations remain under-labeled; models mirror those gaps.
• Interpretability: attention maps aren't explanations. Use rationales, citations, and counterfactual tests; keep humans in control.
• Cost: large models strain GPU budgets. Prefer smaller domain models with retrieval; scale only if outcomes justify it.
• Biological plausibility: generated molecules or interactions may be synthetically impractical or physiologically off. Add constraints and expert review.
• Multimodality: integrating sequence, structure, expression, images, and EHR is still early. Expect brittle edges across modalities.
• Evaluation ground truth: biology is noisy; many "labels" are provisional. Use multiple assays or literature triangulation where possible.

12-month sample roadmap

• Q1: pick 1-2 low-risk use cases; deploy RAG prototype; secure data agreements; define metrics.
• Q2: pilot with 10-30 clinicians or scientists; weekly feedback; cut prompts that confuse; add guardrails.
• Q3: expand to a second department; integrate with FHIR/graph tools; formalize bias and safety reviews.
• Q4: cost-optimizations (LoRA, quantization); external validation; publish internal guidance and training.
• Ongoing: monitor drift, re-evaluate models quarterly, and keep a kill switch.

What "good" looks like

• Clinicians save minutes per note and get better first drafts; fewer clicks in the EHR.
• Researchers prioritize variants, interactions, or compounds with higher hit rates downstream.
• Outputs include sources, uncertainty, and match institutional guidelines.
• Governance is clear: data use, auditing, escalation, and model lifecycle all documented.

Key model examples to know (non-exhaustive)

• Genomics: Enformer, GPN, DNABERT, Nucleotide Transformer, MoDNA, GROVER
• Transcriptomics: scBERT, Geneformer, scGPT, tGPT, SCimilarity, Nicheformer
• Proteomics: ESM-2, MSA Transformer, ProLLaMA, FAPM, ProteinChat, ProLLM
• Drug discovery: MolFM, TGM-DLM, DTI-LM, DLM-DTI, Tx-LLM
• Biomedical NLP: BioBERT, PubMedBERT, BioMegatron, BioMedLM, BioGPT, BioRAG

Further learning

Want structured, role-based training for your team? Explore practical AI courses by job category at Complete AI Training.

Bottom line: start small, keep humans in the loop, and measure outcomes that matter. The teams that win will pair solid clinical judgment with disciplined use of models and data.