ReviewMolecular therapy. Nucleic acids2024
Large language model to multimodal large language model: A journey to shape the biological macromolecules to biological sciences and medicine.
Review in Molecular therapy. Nucleic acids, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Artificial Intelligence-Driven Antimicrobial Peptide Discovery: Prediction, Generation, Mining and Optimization.Probiotics and antimicrobial proteins · 2026Review
- Artificial intelligence-enabled cytopathological interpretation of fluorescence microscopy images via a multimodal large language model.Quantitative biology (Beijing, China) · 2026Article
- Article
- Evaluation of the performance of ChatGPT-4 and ChatGPT-4o as a learning tool in endodontics.International endodontic journal · 2026Article
- Engineering challenges and translational opportunities in emerging gene delivery platforms.Nature biomedical engineering · 2026Review
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The Power of Multimodality in Multimodal Large Language Models, Unimodal ChatGPT 5.0, and Human Clinical Experts on a Wound Care Certification Examination: Cross-Sectional Comparative Study.JMIR formative research · 2026Article
- Comprehensive evaluation of ACMG/AMP-based variant classification tools.Bioinformatics (Oxford, England) · 2026Article
- Prompt engineering-enabled GenAI tools to map the landscape of crosstalk within the gut-brain axis (GBA) and its associated factors.International journal of surgery (London, England) · 2026Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- AI-Assisted Response Surface Methodology for Growth Optimization and Industrial Applicability Evaluation of the DiatomBioengineering (Basel, Switzerland) · 2025Article
- Automating inductive thematic analyses of health content using large language models: a proof-of-concept study using social media data.JAMIA open · 2025Article
- Generative artificial intelligence, integrative bioinformatics, and single-cell analysis reveal Alzheimer's genetic and immune landscape.Molecular therapy. Nucleic acids · 2025Article
- Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations.Bioinformatics (Oxford, England) · 2025Article
- Vascular senescence and aging: mechanisms, clinical implications, and therapeutic prospects.Biogerontology · 2025Review
- A review of recent artificial intelligence for traditional medicine.Journal of traditional and complementary medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
After ChatGPT was released, large language models (LLMs) became more popular. Academicians use ChatGPT or LLM models for different purposes, and the use of ChatGPT or LLM is increasing from medical science to diversified areas. Recently, the multimodal LLM (MLLM) has also become popular. Therefore, we comprehensively illustrate the LLM and MLLM models for a complete understanding. We also aim for simple and extended reviews of LLMs and MLLMs for a broad category of readers, such as researchers, students in diversified fields, and other academicians. The review article illustrates the LLM and MLLM models, their working principles, and their applications in diversified fields. First, we demonstrate the technical concept of LLMs, working principle, Black Box, and the evolution of LLMs. To explain the working principle, we discuss the tokenization process, token representation, and token relationships. We also extensively demonstrate the application of LLMs in biological macromolecules, medical science, biological science, and other areas. We illustrate the multimodal applications of LLMs or MLLMs. Finally, we illustrate the limitations, challenges, and future prospects of LLMs. The review acts as a booster dose for clinicians, a primer for molecular biologists, and a catalyst for scientists, and also benefits diversified academicians.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.