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Algorithms
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Transforming Healthcare with Language Models and Multimodal AI: From Theory...
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Transforming Healthcare with Language Models and Multimodal AI: From Theory to Practice

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 4621

Editor

Dr. Hao Liu

E-Mail Website
Guest Editor
School of Computing, Montclair State University, Montclair, NJ 07043, USA
Interests: clinical informatics; natural language processing; machine learning; data mining; knowledge representation; ontology engineering

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of artificial intelligence (AI) in healthcare to this Special Issue, “Transforming Healthcare with Language Models and Multimodal AI: From Theory to Practice”. This Special Issue explores the emerging applications of large language models (LLMs) and multimodal AI in healthcare, with particular emphasis on their integration into clinical workflows and decision support systems. We seek original research papers that investigate how these advanced AI technologies can enhance healthcare delivery while addressing associated challenges and opportunities. The Special Issue aims to showcase innovative approaches that leverage the latest advances in LLMs and multimodal AI to improve healthcare delivery. Studies demonstrating real-world impact through clinical trials or pilot implementations are highly encouraged. Submissions should emphasize both technical excellence and clinical relevance, with clear discussion of the potential benefits and limitations of proposed approaches.

Dr. Hao Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • clinical applications of LLMs in medical documentation, summarization, and knowledge synthesis
  • multimodal AI systems integrating medical imaging, clinical text, and structured health data
  • LLM-powered clinical decision support systems and diagnostic reasoning
  • integration of foundation models with domain-specific medical knowledge
  • zero-shot and few-shot learning approaches in clinical applications
  • multi-modal retrieval and reasoning across medical literature, imaging, and patient records
  • ethical considerations in deploying LLMs in healthcare settings
  • evaluation frameworks for assessing LLM performance in clinical contexts
  • privacy-preserving techniques for training and deploying healthcare LLMs
  • interpretability and explainability of multimodal AI systems in clinical settings

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

28 pages, 2161 KB  
Article
LLM-Linked Chatbot Platforms for Seeded Clinical Trial Randomization Workflows: A Benchmarking Study of Reproducibility, Allocation Integrity, and Operational Traceability
by Carlos Fernando Mourão, Luiz Eduardo Juliasse, Adam Lowenstein, Bruno César de Vasconcelos Gurgel, Rodrigo dos Santos Pereira and Gutemberg Gomes Alves
Algorithms 2026, 19(7), 551; https://doi.org/10.3390/a19070551 - 6 Jul 2026
Viewed by 181
Abstract
Randomization sequence generation is essential in randomized controlled trials, but access to trial-management systems or statistical support may be limited in some settings. This in silico technical benchmarking study evaluated whether four LLM-linked chatbot interfaces can faithfully execute pre-specified deterministic Python code to [...] Read more.
Randomization sequence generation is essential in randomized controlled trials, but access to trial-management systems or statistical support may be limited in some settings. This in silico technical benchmarking study evaluated whether four LLM-linked chatbot interfaces can faithfully execute pre-specified deterministic Python code to generate a randomized sequence under fixed-seed conditions. In Experiment 1, four investigators performed 1200 fixed-seed Python runs across two sample-size scenarios (n = 30 and n = 50), benchmarked against seeded Excel/VBA and R Console workflows. In Experiment 2, the same investigators performed 320 NL-only runs without code submission or seed specification. A supplementary permuted block benchmark (n = 60; blocks of six) added 640 runs across both prompting conditions. Fixed-seed code execution achieved 100% exact reproducibility, allocation integrity, format compliance, and operational completion across all platforms. NL-only prompting preserved allocation integrity, format compliance, and operational completion (100%) but yielded 0% exact reproducibility in both simple and permuted block randomization. These findings support only a constrained interpretation: chatbot-mediated reproducibility depends on executable code, fixed-seed specification, preserved documentation, and human verification. These interfaces should not replace dedicated randomization software or validated trial-management systems. Full article
(This article belongs to the Special Issue Transforming Healthcare with Language Models and Multimodal AI: From Theory to Practice)
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24 pages, 471 KB  
Article
Transforming Medical Data Access: The Role and Challenges of Recent Language Models in SQL Query Automation
by Nikola Tanković, Robert Šajina and Ivan Lorencin
Algorithms 2025, 18(3), 124; https://doi.org/10.3390/a18030124 - 21 Feb 2025
Cited by 4 | Viewed by 3597
Abstract
Generating accurate SQL queries from natural language is critical for enabling non-experts to interact with complex databases, particularly in high-stakes domains like healthcare. This paper presents an extensive evaluation of state-of-the-art large language models (LLM), including LLaMA 3.3, Mixtral, Gemini, Claude 3.5, GPT-4o, [...] Read more.
Generating accurate SQL queries from natural language is critical for enabling non-experts to interact with complex databases, particularly in high-stakes domains like healthcare. This paper presents an extensive evaluation of state-of-the-art large language models (LLM), including LLaMA 3.3, Mixtral, Gemini, Claude 3.5, GPT-4o, and Qwen for transforming medical questions into executable SQL queries using the MIMIC-3 and TREQS datasets. Our approach employs LLMs with various prompts across 1000 natural language questions. The experiments are repeated multiple times to assess performance consistency, token efficiency, and cost-effectiveness. We explore the impact of prompt design on model accuracy through an ablation study, focusing on the role of table data samples and one-shot learning examples. The results highlight substantial trade-offs between accuracy, consistency, and computational cost between the models. This study also underscores the limitations of current models in handling medical terminology and provides insights to improve SQL query generation in the healthcare domain. Future directions include implementing RAG pipelines based on embeddings and reranking models, integrating ICD taxonomies, and refining evaluation metrics for medical query performance. By bridging these gaps, language models can become reliable tools for medical database interaction, enhancing accessibility and decision-making in clinical settings. Full article
(This article belongs to the Special Issue Transforming Healthcare with Language Models and Multimodal AI: From Theory to Practice)
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