Medicine & Health

466 items

Therapeutic delivery Sep 17, 2026
This review searched PubMed, Scopus, Web of Science, and Google Scholar for literature up to March 2026 and synthesized the preclinical evidence, safety and regulatory considerations, and clinical development path for intranasal cannabidiol (CBD) delivered via nanostructured lipid carriers (NLCs) in Dravet syndrome (DS) and Lennox-Gastaut syndrome (LGS), noting that intranasal NLC-CBD increased brain CBD levels, enhanced brain targeting, and prolonged central exposure in animal models, while properly designed clinical trials are still needed to establish safety, pharmacokinetics, and efficacy in pediatric DS and LGS patients.
This review searched PubMed, Scopus, Web of Science, and Google Scholar for literature up to March 2026 and synthesized the preclinical evidence, safety and regulatory considerations, and clinical development path for intranasal cannabidiol (CBD) delivered via nanostructured lipid carriers (NLCs) in Dravet syndrome (DS) and Lennox-Gastaut syndrome (LGS), noting that intranasal NLC-CBD increased brain CBD levels, enhanced brain targeting, and prolonged central exposure in animal models, while properly designed clinical trials are still needed to establish safety, pharmacokinetics, and efficacy in pediatric DS and LGS patients.
Nanostructured lipid carriers for intranasal cannabidiol delivery in Dravet and Lennox-Gastaut syndromes: bridging preclinical promise to clinical translation
This review searched PubMed, Scopus, Web of Science, and Google Scholar for literature up to March 2026 and synthesized the preclinical evidence, safety and regulatory considerations, and clinical development path for intranasal cannabidiol (CBD) delivered via nanostructured lipid carriers (NLCs) in Dravet syndrome (DS) and Lennox-Gastaut syndrome (LGS), noting that intranasal NLC-CBD increased brain CBD levels, enhanced brain targeting, and prolonged central exposure in animal models, while properly designed clinical trials are still needed to establish safety, pharmacokinetics, and efficacy in pediatric DS and LGS patients.
This review searched PubMed, Scopus, Web of Science, and Google Scholar for literature up to March 2026 and synthesized the preclinical evidence, safety and regulatory considerations, and clinical development path for intranasal cannabidiol (CBD) delivered via nanostructured lipid carriers (NLCs) in Dravet syndrome (DS) and Lennox-Gastaut syndrome (LGS), noting that intranasal NLC-CBD increased brain CBD levels, enhanced brain targeting, and prolonged central exposure in animal models, while properly designed clinical trials are still needed to establish safety, pharmacokinetics, and efficacy in pediatric DS and LGS patients.
bioRxiv Sep 17, 2026
The authors introduce SPHERE, a model-free method that turns sensitive datasets into shareable synthetic twins that AI systems and collaborators can use directly while the original records never leave the local environment; across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving statistical structure (means, variances and correlations reproduced exactly, effect size and P value numerically identical in linear analysis, nonlinear machine-learning utility retained, each twin generated in seconds on a laptop), frontier AI agents on the twin reach the same scientific conclusions as on the original records, analyses reproduce genome- and proteome-wide results at UK Biobank scale and recover landmark f
The authors introduce SPHERE, a model-free method that turns sensitive datasets into shareable synthetic twins that AI systems and collaborators can use directly while the original records never leave the local environment; across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving statistical structure (means, variances and correlations reproduced exactly, effect size and P value numerically identical in linear analysis, nonlinear machine-learning utility retained, each twin generated in seconds on a laptop), frontier AI agents on the twin reach the same scientific conclusions as on the original records, analyses reproduce genome- and proteome-wide results at UK Biobank scale and recover landmark f
SPHERE: Making Sensitive Data Directly Usable and Shareable in the Age of AI
The authors introduce SPHERE, a model-free method that turns sensitive datasets into shareable synthetic twins that AI systems and collaborators can use directly while the original records never leave the local environment; across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving statistical structure (means, variances and correlations reproduced exactly, effect size and P value numerically identical in linear analysis, nonlinear machine-learning utility retained, each twin generated in seconds on a laptop), frontier AI agents on the twin reach the same scientific conclusions as on the original records, analyses reproduce genome- and proteome-wide results at UK Biobank scale and recover landmark f
The authors introduce SPHERE, a model-free method that turns sensitive datasets into shareable synthetic twins that AI systems and collaborators can use directly while the original records never leave the local environment; across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving statistical structure (means, variances and correlations reproduced exactly, effect size and P value numerically identical in linear analysis, nonlinear machine-learning utility retained, each twin generated in seconds on a laptop), frontier AI agents on the twin reach the same scientific conclusions as on the original records, analyses reproduce genome- and proteome-wide results at UK Biobank scale and recover landmark f
Studies in health technology and informatics Sep 17, 2026
Combining a systematic literature review with an experimental evaluation, this study tested ten freely available large language models on five synthetic German clinical notes using standardized prompts, finding that all models substantially increased text length and consistently reduced the density of technical terms and abbreviations, yet no model achieved consistent improvements across all readability indices, with Mistral, ChatGPT, and Copilot showing the highest efficiency in balancing linguistic simplification and text length, suggesting that conventional readability metrics should be extended with domain-specific measures.
Combining a systematic literature review with an experimental evaluation, this study tested ten freely available large language models on five synthetic German clinical notes using standardized prompts, finding that all models substantially increased text length and consistently reduced the density of technical terms and abbreviations, yet no model achieved consistent improvements across all readability indices, with Mistral, ChatGPT, and Copilot showing the highest efficiency in balancing linguistic simplification and text length, suggesting that conventional readability metrics should be extended with domain-specific measures.
Large Language Models for Clinical Note Simplification: A Systematic Review and Experimental Evaluation of Medical Text Readability
Combining a systematic literature review with an experimental evaluation, this study tested ten freely available large language models on five synthetic German clinical notes using standardized prompts, finding that all models substantially increased text length and consistently reduced the density of technical terms and abbreviations, yet no model achieved consistent improvements across all readability indices, with Mistral, ChatGPT, and Copilot showing the highest efficiency in balancing linguistic simplification and text length, suggesting that conventional readability metrics should be extended with domain-specific measures.
Combining a systematic literature review with an experimental evaluation, this study tested ten freely available large language models on five synthetic German clinical notes using standardized prompts, finding that all models substantially increased text length and consistently reduced the density of technical terms and abbreviations, yet no model achieved consistent improvements across all readability indices, with Mistral, ChatGPT, and Copilot showing the highest efficiency in balancing linguistic simplification and text length, suggesting that conventional readability metrics should be extended with domain-specific measures.
Biomedical physics & engineering express Sep 17, 2026
This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.
This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.
HARU-Net: hybrid attention residual U-Net for edge-preserving denoising in cone-beam computed tomography
This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.
This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.
Journal of medical Internet research Sep 17, 2026
Following PRISMA 2020 guidelines and searching PubMed/MEDLINE, Scopus, and Web of Science (January 2018 through May 2026), this systematic review screened 1890 records and included 35 studies published between 2024 and 2026, grouping them into fine-tuning or parameter-efficient fine-tuning (7/35, 20%), retrieval-augmented generation (17/35, 48.6%), and hybrid approaches (11/35, 31.4%) for descriptive synthesis, finding that fine-tuning performed strongly on narrow task-specific applications (area under the receiver operating characteristic curve up to 0.912 for cancer detection and area under curve 0.892 for major depressive disorder prediction), that retrieval-augmented generation improved guideline adherence and diagnostic accuracy (from 71.1% to 92.1% and from 78.9% to 94.
Following PRISMA 2020 guidelines and searching PubMed/MEDLINE, Scopus, and Web of Science (January 2018 through May 2026), this systematic review screened 1890 records and included 35 studies published between 2024 and 2026, grouping them into fine-tuning or parameter-efficient fine-tuning (7/35, 20%), retrieval-augmented generation (17/35, 48.6%), and hybrid approaches (11/35, 31.4%) for descriptive synthesis, finding that fine-tuning performed strongly on narrow task-specific applications (area under the receiver operating characteristic curve up to 0.912 for cancer detection and area under curve 0.892 for major depressive disorder prediction), that retrieval-augmented generation improved guideline adherence and diagnostic accuracy (from 71.1% to 92.1% and from 78.9% to 94.
Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review
Following PRISMA 2020 guidelines and searching PubMed/MEDLINE, Scopus, and Web of Science (January 2018 through May 2026), this systematic review screened 1890 records and included 35 studies published between 2024 and 2026, grouping them into fine-tuning or parameter-efficient fine-tuning (7/35, 20%), retrieval-augmented generation (17/35, 48.6%), and hybrid approaches (11/35, 31.4%) for descriptive synthesis, finding that fine-tuning performed strongly on narrow task-specific applications (area under the receiver operating characteristic curve up to 0.912 for cancer detection and area under curve 0.892 for major depressive disorder prediction), that retrieval-augmented generation improved guideline adherence and diagnostic accuracy (from 71.1% to 92.1% and from 78.9% to 94.
Following PRISMA 2020 guidelines and searching PubMed/MEDLINE, Scopus, and Web of Science (January 2018 through May 2026), this systematic review screened 1890 records and included 35 studies published between 2024 and 2026, grouping them into fine-tuning or parameter-efficient fine-tuning (7/35, 20%), retrieval-augmented generation (17/35, 48.6%), and hybrid approaches (11/35, 31.4%) for descriptive synthesis, finding that fine-tuning performed strongly on narrow task-specific applications (area under the receiver operating characteristic curve up to 0.912 for cancer detection and area under curve 0.892 for major depressive disorder prediction), that retrieval-augmented generation improved guideline adherence and diagnostic accuracy (from 71.1% to 92.1% and from 78.9% to 94.
PLOS digital health Sep 17, 2026
This study proposes a method that builds a schizophrenia QA dataset from publicly accessible online health forums using Topic-guided Semantic Modeling (TGSM) and a two-stage Retriever-Reader pipeline, yielding 415,602 posts, 35 topics, and 1,050 QA pairs, and shows that BioBERT fine-tuned on this dataset outperforms its base version and lighter baselines such as DistilBERT on precision and exact match.
This study proposes a method that builds a schizophrenia QA dataset from publicly accessible online health forums using Topic-guided Semantic Modeling (TGSM) and a two-stage Retriever-Reader pipeline, yielding 415,602 posts, 35 topics, and 1,050 QA pairs, and shows that BioBERT fine-tuned on this dataset outperforms its base version and lighter baselines such as DistilBERT on precision and exact match.
A Guide to Building Efficient QA Systems for Medical Use: Helping Patients Gather Reliable Information
This study proposes a method that builds a schizophrenia QA dataset from publicly accessible online health forums using Topic-guided Semantic Modeling (TGSM) and a two-stage Retriever-Reader pipeline, yielding 415,602 posts, 35 topics, and 1,050 QA pairs, and shows that BioBERT fine-tuned on this dataset outperforms its base version and lighter baselines such as DistilBERT on precision and exact match.
This study proposes a method that builds a schizophrenia QA dataset from publicly accessible online health forums using Topic-guided Semantic Modeling (TGSM) and a two-stage Retriever-Reader pipeline, yielding 415,602 posts, 35 topics, and 1,050 QA pairs, and shows that BioBERT fine-tuned on this dataset outperforms its base version and lighter baselines such as DistilBERT on precision and exact match.
Die Ophthalmologie Sep 17, 2026
This study anonymously surveyed 34 ophthalmologists (median age 49 years, 47% female) from a regional physician network in Germany via online questionnaire and found that most respondents spent 20–39% of their working time on clinical documentation, approximately three quarters expressed general interest in using a reliable and secure ambient scribe system, key perceived benefits were reduced documentation time and increased time for patient care, major barriers were concerns about AI reliability, integration into existing IT systems and data protection, with locally deployed solutions preferred over cloud-based approaches.
This study anonymously surveyed 34 ophthalmologists (median age 49 years, 47% female) from a regional physician network in Germany via online questionnaire and found that most respondents spent 20–39% of their working time on clinical documentation, approximately three quarters expressed general interest in using a reliable and secure ambient scribe system, key perceived benefits were reduced documentation time and increased time for patient care, major barriers were concerns about AI reliability, integration into existing IT systems and data protection, with locally deployed solutions preferred over cloud-based approaches.
Survey of Acceptance and Requirements for AI Ambient Scribe Systems in German Ophthalmology
This study anonymously surveyed 34 ophthalmologists (median age 49 years, 47% female) from a regional physician network in Germany via online questionnaire and found that most respondents spent 20–39% of their working time on clinical documentation, approximately three quarters expressed general interest in using a reliable and secure ambient scribe system, key perceived benefits were reduced documentation time and increased time for patient care, major barriers were concerns about AI reliability, integration into existing IT systems and data protection, with locally deployed solutions preferred over cloud-based approaches.
This study anonymously surveyed 34 ophthalmologists (median age 49 years, 47% female) from a regional physician network in Germany via online questionnaire and found that most respondents spent 20–39% of their working time on clinical documentation, approximately three quarters expressed general interest in using a reliable and secure ambient scribe system, key perceived benefits were reduced documentation time and increased time for patient care, major barriers were concerns about AI reliability, integration into existing IT systems and data protection, with locally deployed solutions preferred over cloud-based approaches.
Clinical and molecular hepatology Sep 17, 2026
This review organizes current work toward an AI Virtual Cell (AIVC) for the liver into three complementary modeling routes—generative models that represent cell states, dynamics and transport models that infer state transitions, and pretrained or foundation models that test whether learned representations transfer across donors, etiologies, disease stages, and platforms—with perturbation-response prediction as a cross-cutting assessment, concluding that published models demonstrate only individual components such as atlas integration, inferred trajectories, transferable representations, and retrospective response programs, and do not yet constitute a prospectively validated liver simulator, so near-term use should prioritize experiment selection and hypothesis generation while clinical dec
This review organizes current work toward an AI Virtual Cell (AIVC) for the liver into three complementary modeling routes—generative models that represent cell states, dynamics and transport models that infer state transitions, and pretrained or foundation models that test whether learned representations transfer across donors, etiologies, disease stages, and platforms—with perturbation-response prediction as a cross-cutting assessment, concluding that published models demonstrate only individual components such as atlas integration, inferred trajectories, transferable representations, and retrospective response programs, and do not yet constitute a prospectively validated liver simulator, so near-term use should prioritize experiment selection and hypothesis generation while clinical dec
Toward AI Virtual Cells for Hepatology: Representation, Generation, Dynamics, and Intervention in Single-Cell Models
This review organizes current work toward an AI Virtual Cell (AIVC) for the liver into three complementary modeling routes—generative models that represent cell states, dynamics and transport models that infer state transitions, and pretrained or foundation models that test whether learned representations transfer across donors, etiologies, disease stages, and platforms—with perturbation-response prediction as a cross-cutting assessment, concluding that published models demonstrate only individual components such as atlas integration, inferred trajectories, transferable representations, and retrospective response programs, and do not yet constitute a prospectively validated liver simulator, so near-term use should prioritize experiment selection and hypothesis generation while clinical dec
This review organizes current work toward an AI Virtual Cell (AIVC) for the liver into three complementary modeling routes—generative models that represent cell states, dynamics and transport models that infer state transitions, and pretrained or foundation models that test whether learned representations transfer across donors, etiologies, disease stages, and platforms—with perturbation-response prediction as a cross-cutting assessment, concluding that published models demonstrate only individual components such as atlas integration, inferred trajectories, transferable representations, and retrospective response programs, and do not yet constitute a prospectively validated liver simulator, so near-term use should prioritize experiment selection and hypothesis generation while clinical dec
PLOS digital health Sep 17, 2026
The study presents SAFE-AI, a framework that restricts a large language model to zero-shot entity extraction from emergency medical services charts and then makes determinations through a deterministic rule-based computational graph built from a clinician-defined ontology of clinical guidelines, reporting 97.9% accuracy for detecting epinephrine overdose and 91.6% for detecting delays in epinephrine administration across 300 pediatric out-of-hospital cardiac arrest charts containing 18,402 lines of clinical information, outperforming the compared baseline models.
The study presents SAFE-AI, a framework that restricts a large language model to zero-shot entity extraction from emergency medical services charts and then makes determinations through a deterministic rule-based computational graph built from a clinician-defined ontology of clinical guidelines, reporting 97.9% accuracy for detecting epinephrine overdose and 91.6% for detecting delays in epinephrine administration across 300 pediatric out-of-hospital cardiac arrest charts containing 18,402 lines of clinical information, outperforming the compared baseline models.
A Medically Grounded LLM Agent-Based Tool to Detect Patient Safety Events in Medical Records
The study presents SAFE-AI, a framework that restricts a large language model to zero-shot entity extraction from emergency medical services charts and then makes determinations through a deterministic rule-based computational graph built from a clinician-defined ontology of clinical guidelines, reporting 97.9% accuracy for detecting epinephrine overdose and 91.6% for detecting delays in epinephrine administration across 300 pediatric out-of-hospital cardiac arrest charts containing 18,402 lines of clinical information, outperforming the compared baseline models.
The study presents SAFE-AI, a framework that restricts a large language model to zero-shot entity extraction from emergency medical services charts and then makes determinations through a deterministic rule-based computational graph built from a clinician-defined ontology of clinical guidelines, reporting 97.9% accuracy for detecting epinephrine overdose and 91.6% for detecting delays in epinephrine administration across 300 pediatric out-of-hospital cardiac arrest charts containing 18,402 lines of clinical information, outperforming the compared baseline models.
Studies in health technology and informatics Sep 17, 2026
This technical case report develops and evaluates a system that combines local large language models with rule-based validation against HUGO Gene Nomenclature to extract and structure mutational eligibility criteria from clinical trial records (identifying mutated genes, distinguishing inclusion from exclusion criteria, and assigning them to individual study arms); applied to 4,918 clinical trials it produced structured representations for 1,010 studies, and expert review of 42 trials showed 88.1% of studies correctly annotated with 80% precision at the level of individual eligibility criteria, with failures mainly due to hallucinated genes and misinterpreted abbreviations.
This technical case report develops and evaluates a system that combines local large language models with rule-based validation against HUGO Gene Nomenclature to extract and structure mutational eligibility criteria from clinical trial records (identifying mutated genes, distinguishing inclusion from exclusion criteria, and assigning them to individual study arms); applied to 4,918 clinical trials it produced structured representations for 1,010 studies, and expert review of 42 trials showed 88.1% of studies correctly annotated with 80% precision at the level of individual eligibility criteria, with failures mainly due to hallucinated genes and misinterpreted abbreviations.
Automated Extraction of Genetic Eligibility Criteria from Clinical Trial Records Using LLMs: A Technical Case Report
This technical case report develops and evaluates a system that combines local large language models with rule-based validation against HUGO Gene Nomenclature to extract and structure mutational eligibility criteria from clinical trial records (identifying mutated genes, distinguishing inclusion from exclusion criteria, and assigning them to individual study arms); applied to 4,918 clinical trials it produced structured representations for 1,010 studies, and expert review of 42 trials showed 88.1% of studies correctly annotated with 80% precision at the level of individual eligibility criteria, with failures mainly due to hallucinated genes and misinterpreted abbreviations.
This technical case report develops and evaluates a system that combines local large language models with rule-based validation against HUGO Gene Nomenclature to extract and structure mutational eligibility criteria from clinical trial records (identifying mutated genes, distinguishing inclusion from exclusion criteria, and assigning them to individual study arms); applied to 4,918 clinical trials it produced structured representations for 1,010 studies, and expert review of 42 trials showed 88.1% of studies correctly annotated with 80% precision at the level of individual eligibility criteria, with failures mainly due to hallucinated genes and misinterpreted abbreviations.

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