Clinical AI and Large Language Models in Healthcare
The application of artificial intelligence in clinical settings has expanded rapidly, driven by advances in machine learning and natural language processing — extracting insight from electronic health records, improving care through predictive analytics, and reshaping clinical documentation.
Watch — a short tour of this page, narrated in my own AI-cloned voice.
1. Overview
The application of artificial intelligence (AI) in clinical settings has expanded rapidly, driven by advances in machine learning and natural language processing. This document explores how these technologies are being used to extract meaningful insights from electronic health records (EHRs), improve patient care through predictive analytics, and enhance documentation efficiency.
2. Electronic Health Record Analytics
The advent of machine learning has revolutionised how we analyse EHR data. By leveraging complex algorithms, researchers can now predict disease outcomes and personalise treatments with unprecedented accuracy.
2.1 Reverse Time Attention Mechanism (RETAIN)
RETAIN (Choi et al., 2016)[27] processes patient records in reverse chronological order to better capture the impact of recent medical events, thereby enhancing predictive performance for tasks such as disease onset prediction and readmission risk assessment.
2.2 BERT-Style Transformers
Transformers have been adapted to the medical domain, enabling the extraction of rich clinical insights from structured and unstructured data. Models like BEHRT[28] pre-trained on large-scale EHR corpora demonstrate superior performance in predicting disease onset compared to traditional methods.
3. Clinical Natural Language Processing (NLP)
The vast majority of patient information within EHRs is unstructured text, necessitating sophisticated NLP techniques to unlock this data’s value. Tasks range from identifying medical entities and relations to generating machine-readable summaries.
3.1 Biomedical Language Models
Language models trained on biomedical text have significantly advanced the field of clinical NLP, outperforming traditional approaches in tasks such as named entity recognition and de-identification[30].
| Model | Architecture | Training Corpus | Parameters (M) | Key Tasks |
|---|---|---|---|---|
| BioBERT | BERT | PubMed + PMC (4.5B words) | 110M | NER, QA, RE |
| ClinicalBERT | BERT | MIMIC-III notes | 110M | NER, de-id, readmission |
| GatorTron | Megatron-BERT | UF Health (>82B clinical words) + PubMed + Wiki | 345M / 3.9B / 8.9B | NER, relation, QA, NLI |
| BioMedLM | GPT-2 | PubMed (4.5B tokens) | 2.7B | Medical QA (MedQA) |
| Med-PaLM 2 | PaLM 2 | Medical web + textbooks + RLHF | undisclosed (PaLM 2 base) | QA, reasoning |
4. Large Language Models in Clinical Practice
Recent advances in large language model technology have sparked interest in their potential applications within healthcare[31]. However, the deployment of such models presents significant challenges related to safety, equity, and regulatory compliance.
4.1 Ambient Documentation Assistants
LLMs are being utilised to draft clinical documentation in real-time, significantly reducing physician workload. However, this convenience comes with risks including inaccuracies and biases that must be rigorously managed.
4.2 Regulatory Frameworks for Medical AI
The deployment of AI in healthcare is subject to stringent regulatory oversight. Both the FDA (in the United States) and the European Union have established guidelines aimed at ensuring the safety, efficacy, and ethical use of medical AI products. The FDA’s “Software as a Medical Device” framework and the EU AI Act both treat most clinical decision-support AI as high-risk, imposing requirements on validation, transparency, and post-market surveillance.