BackgroundMedicine is inherently multimodal, requiring the simultaneous interpretation and integration of insights between many data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence systems that flexibly encode, integrate, and interpret these data might better enable impactful applications ranging from scientific discovery to care delivery.MethodsTo catalyze development of these models, we curated MultiMedBench,…
NEJM AI Template
Write in a clean editor, then format for NEJM AI in one click — DocuGuru applies the official Clinical & Medical Journals template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the NEJM AI format
NEJM AI is a peer-reviewed journal published by Clinical & Medical Journals, covering Artificial Intelligence in Healthcare and Education, Machine Learning in Healthcare, Ethics and Social Impacts of AI. Over its lifetime it has published 292 papers, which have received 4,890 citations.
| Publisher | Clinical & Medical Journals |
|---|---|
| Reference style | Numbered (Vancouver) Numbered — [1], [2] in the text 1. Smith A, Jones B, Lee C. A representative article title. NEJM AI. 2023;12(3):45–58.
Formats any DOI in NEJM AI style. No sign-up. |
| Publishes research in | Artificial Intelligence in Healthcare and Education Machine Learning in Healthcare Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI) Clinical Reasoning and Diagnostic Skills |
| ISSN | 2836-9386 |
| Citation impact (2-yr) | 15.87 |
| h-index | 35 |
| i10-index | 99 |
| Top institutions publishing here | Harvard University |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in NEJM AI per year
Citation impact of NEJM AI by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in NEJM AI
BACKGROUND: Large language models (LLMs) have recently shown impressive zero-shot capabilities, whereby they can use auxiliary data, without the availability of task-specific training examples, to complete a variety of natural language tasks, such as summarization, dialogue generation, and question answering. However, despite many promising applications of LLMs in clinical medicine, adoption of these models has…
We assessed the performance of the newly released AI GPT-4 in diagnosing complex medical case challenges and compared the success rate to that of medical-journal readers. GPT-4 correctly diagnosed 57% of cases, outperforming 99.98% of simulated human readers generated from online answers. We highlight the potential for AI to be a powerful supportive tool for…