Medicine & Health

468 items

medRxiv Sep 16, 2026
This study updated an automated tumour infiltrating lymphocyte (TIL) assessment pipeline, proposing TRIGS aligned with clinical scoring guidelines and foundation model-based SAM-TIL, and showed in 166 neoadjuvantly treated patients in the TransNEO cohort that they predicted pathological complete response with odds ratios of 1.95 (95% CI 1.22-3.03, p=0.005) and 2.32 (95% CI 1.43-3.77, p=0.001), and in 277 triple negative and HER2-positive patients in The Cancer Genome Atlas showed overall survival hazard ratios of 0.79 (95% CI 0.63-1.00, p=0.05) and 0.80 (95% CI 0.67-0.97, p=0.02), with correlation to gold standard clinical assessment of 0.59-0.69 and no substantial difference from gold standard assessment in predicting pathological complete response (AUC 0.60-0.
This study updated an automated tumour infiltrating lymphocyte (TIL) assessment pipeline, proposing TRIGS aligned with clinical scoring guidelines and foundation model-based SAM-TIL, and showed in 166 neoadjuvantly treated patients in the TransNEO cohort that they predicted pathological complete response with odds ratios of 1.95 (95% CI 1.22-3.03, p=0.005) and 2.32 (95% CI 1.43-3.77, p=0.001), and in 277 triple negative and HER2-positive patients in The Cancer Genome Atlas showed overall survival hazard ratios of 0.79 (95% CI 0.63-1.00, p=0.05) and 0.80 (95% CI 0.67-0.97, p=0.02), with correlation to gold standard clinical assessment of 0.59-0.69 and no substantial difference from gold standard assessment in predicting pathological complete response (AUC 0.60-0.
Tumour region identification guided scoring (TRIGS) and foundation model-based Tumour Infiltrating Lymphocyte scoring are prognostic for pathological complete response/event free survival in the triple negative patients in the PARTNER randomized controlled trial
This study updated an automated tumour infiltrating lymphocyte (TIL) assessment pipeline, proposing TRIGS aligned with clinical scoring guidelines and foundation model-based SAM-TIL, and showed in 166 neoadjuvantly treated patients in the TransNEO cohort that they predicted pathological complete response with odds ratios of 1.95 (95% CI 1.22-3.03, p=0.005) and 2.32 (95% CI 1.43-3.77, p=0.001), and in 277 triple negative and HER2-positive patients in The Cancer Genome Atlas showed overall survival hazard ratios of 0.79 (95% CI 0.63-1.00, p=0.05) and 0.80 (95% CI 0.67-0.97, p=0.02), with correlation to gold standard clinical assessment of 0.59-0.69 and no substantial difference from gold standard assessment in predicting pathological complete response (AUC 0.60-0.
This study updated an automated tumour infiltrating lymphocyte (TIL) assessment pipeline, proposing TRIGS aligned with clinical scoring guidelines and foundation model-based SAM-TIL, and showed in 166 neoadjuvantly treated patients in the TransNEO cohort that they predicted pathological complete response with odds ratios of 1.95 (95% CI 1.22-3.03, p=0.005) and 2.32 (95% CI 1.43-3.77, p=0.001), and in 277 triple negative and HER2-positive patients in The Cancer Genome Atlas showed overall survival hazard ratios of 0.79 (95% CI 0.63-1.00, p=0.05) and 0.80 (95% CI 0.67-0.97, p=0.02), with correlation to gold standard clinical assessment of 0.59-0.69 and no substantial difference from gold standard assessment in predicting pathological complete response (AUC 0.60-0.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Sep 16, 2026
This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp
This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp
Artificial Intelligence in Otolaryngology: Current Applications, Limitations, and Future Perspectives
This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp
This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp
Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics Sep 16, 2026
This review proposes and organizes a framework of emerging clinical trial models for central nervous system tumors, including master protocol designs, Bayesian adaptive frameworks, trials as active discovery platforms embedding longitudinal tissue sampling, window-of-opportunity designs and multi-omic profiling, and decentralized models with artificial intelligence tools, arguing that trials should be reimagined as dynamic, biologically integrated, learning-based systems rather than static tests of individual agents in order to accelerate therapeutic progress in neuro-oncology.
This review proposes and organizes a framework of emerging clinical trial models for central nervous system tumors, including master protocol designs, Bayesian adaptive frameworks, trials as active discovery platforms embedding longitudinal tissue sampling, window-of-opportunity designs and multi-omic profiling, and decentralized models with artificial intelligence tools, arguing that trials should be reimagined as dynamic, biologically integrated, learning-based systems rather than static tests of individual agents in order to accelerate therapeutic progress in neuro-oncology.
Accelerating discovery: Transformative clinical trial models in neuro-oncology
This review proposes and organizes a framework of emerging clinical trial models for central nervous system tumors, including master protocol designs, Bayesian adaptive frameworks, trials as active discovery platforms embedding longitudinal tissue sampling, window-of-opportunity designs and multi-omic profiling, and decentralized models with artificial intelligence tools, arguing that trials should be reimagined as dynamic, biologically integrated, learning-based systems rather than static tests of individual agents in order to accelerate therapeutic progress in neuro-oncology.
This review proposes and organizes a framework of emerging clinical trial models for central nervous system tumors, including master protocol designs, Bayesian adaptive frameworks, trials as active discovery platforms embedding longitudinal tissue sampling, window-of-opportunity designs and multi-omic profiling, and decentralized models with artificial intelligence tools, arguing that trials should be reimagined as dynamic, biologically integrated, learning-based systems rather than static tests of individual agents in order to accelerate therapeutic progress in neuro-oncology.
Anesthesiology Sep 16, 2026
Using 203,434 ICU admissions from 2001 to 2022 across more than 200 hospitals in the MIMIC-III, MIMIC-IV, eICU, and HiRID databases, the study developed and externally validated a multimodal deep-learning model that predicts subsequent inpatient mortality from time-invariant variables, time-variant variables, clinical notes, and chest x-ray images available within the first 24 h of ICU admission; with structured data alone the model reached an AUROC of 0.92 (95% CI, 0.90 to 0.93), an AUPRC of 0.53 (95% CI, 0.49 to 0.57), and a Brier score of 0.19 (95% CI, 0.18 to 0.20), external validation across eight eICU institutions yielded AUROCs of 0.84 to 0.92, and in the subgroup with both notes and imaging, adding text and images raised the AUROC modestly from 0.87 (95% CI, 0.85 to 0.89) to 0.
Using 203,434 ICU admissions from 2001 to 2022 across more than 200 hospitals in the MIMIC-III, MIMIC-IV, eICU, and HiRID databases, the study developed and externally validated a multimodal deep-learning model that predicts subsequent inpatient mortality from time-invariant variables, time-variant variables, clinical notes, and chest x-ray images available within the first 24 h of ICU admission; with structured data alone the model reached an AUROC of 0.92 (95% CI, 0.90 to 0.93), an AUPRC of 0.53 (95% CI, 0.49 to 0.57), and a Brier score of 0.19 (95% CI, 0.18 to 0.20), external validation across eight eICU institutions yielded AUROCs of 0.84 to 0.92, and in the subgroup with both notes and imaging, adding text and images raised the AUROC modestly from 0.87 (95% CI, 0.85 to 0.89) to 0.
Development and External Validation of a Multimodal Artificial Intelligence Mortality Prediction Model of Critically Ill Patients Using Multicenter Data
Using 203,434 ICU admissions from 2001 to 2022 across more than 200 hospitals in the MIMIC-III, MIMIC-IV, eICU, and HiRID databases, the study developed and externally validated a multimodal deep-learning model that predicts subsequent inpatient mortality from time-invariant variables, time-variant variables, clinical notes, and chest x-ray images available within the first 24 h of ICU admission; with structured data alone the model reached an AUROC of 0.92 (95% CI, 0.90 to 0.93), an AUPRC of 0.53 (95% CI, 0.49 to 0.57), and a Brier score of 0.19 (95% CI, 0.18 to 0.20), external validation across eight eICU institutions yielded AUROCs of 0.84 to 0.92, and in the subgroup with both notes and imaging, adding text and images raised the AUROC modestly from 0.87 (95% CI, 0.85 to 0.89) to 0.
Using 203,434 ICU admissions from 2001 to 2022 across more than 200 hospitals in the MIMIC-III, MIMIC-IV, eICU, and HiRID databases, the study developed and externally validated a multimodal deep-learning model that predicts subsequent inpatient mortality from time-invariant variables, time-variant variables, clinical notes, and chest x-ray images available within the first 24 h of ICU admission; with structured data alone the model reached an AUROC of 0.92 (95% CI, 0.90 to 0.93), an AUPRC of 0.53 (95% CI, 0.49 to 0.57), and a Brier score of 0.19 (95% CI, 0.18 to 0.20), external validation across eight eICU institutions yielded AUROCs of 0.84 to 0.92, and in the subgroup with both notes and imaging, adding text and images raised the AUROC modestly from 0.87 (95% CI, 0.85 to 0.89) to 0.
bioRxiv Sep 16, 2026
This work develops a method to infer a Corpus-Wide Causal Score (CWCS) for a gene-disease pair by integrating network-based causal signals in a gene regulatory network (CWCS-Net) with corpus-wide literature evidence from PubMed abstracts quantified by a newly developed Truth Discovery algorithm (CWCS-TD), achieving a causal class F1 score of 0.600 across ten diseases using OMIM as an external expert-curated reference, outperforming GPT-4o (0.505) and MMed-Llama 3 (0.522).
This work develops a method to infer a Corpus-Wide Causal Score (CWCS) for a gene-disease pair by integrating network-based causal signals in a gene regulatory network (CWCS-Net) with corpus-wide literature evidence from PubMed abstracts quantified by a newly developed Truth Discovery algorithm (CWCS-TD), achieving a causal class F1 score of 0.600 across ten diseases using OMIM as an external expert-curated reference, outperforming GPT-4o (0.505) and MMed-Llama 3 (0.522).
Corpus-wide causality: Algorithm design & application for aggregating gene-disease causal evidence
This work develops a method to infer a Corpus-Wide Causal Score (CWCS) for a gene-disease pair by integrating network-based causal signals in a gene regulatory network (CWCS-Net) with corpus-wide literature evidence from PubMed abstracts quantified by a newly developed Truth Discovery algorithm (CWCS-TD), achieving a causal class F1 score of 0.600 across ten diseases using OMIM as an external expert-curated reference, outperforming GPT-4o (0.505) and MMed-Llama 3 (0.522).
This work develops a method to infer a Corpus-Wide Causal Score (CWCS) for a gene-disease pair by integrating network-based causal signals in a gene regulatory network (CWCS-Net) with corpus-wide literature evidence from PubMed abstracts quantified by a newly developed Truth Discovery algorithm (CWCS-TD), achieving a causal class F1 score of 0.600 across ten diseases using OMIM as an external expert-curated reference, outperforming GPT-4o (0.505) and MMed-Llama 3 (0.522).
medRxiv Sep 16, 2026
Using harmonised clinical data from two Phase 3 trials (2,918 participants), this study trained monthly tabular models from baseline to therapy end for time-resolved prediction of end-of-therapy (EOT) outcomes and post-treatment relapse, finding that EOT outcome prediction improved after month 3 (ROC-AUC up to 0.84, driven by sputum-smear and solid culture), whereas relapse prediction among those with favourable EOT outcomes and completed follow-up improved only modestly through month 3 (ROC-AUC 0.58-0.63) before declining, with age, sex, clinical symptoms and bacterial burden contributing most; large language model-derived embedding models matched tabular relapse models throughout therapy and outperformed tabular EOT models at months 3 and 4 (ΔROC-AUC 0.12 and 0.
Using harmonised clinical data from two Phase 3 trials (2,918 participants), this study trained monthly tabular models from baseline to therapy end for time-resolved prediction of end-of-therapy (EOT) outcomes and post-treatment relapse, finding that EOT outcome prediction improved after month 3 (ROC-AUC up to 0.84, driven by sputum-smear and solid culture), whereas relapse prediction among those with favourable EOT outcomes and completed follow-up improved only modestly through month 3 (ROC-AUC 0.58-0.63) before declining, with age, sex, clinical symptoms and bacterial burden contributing most; large language model-derived embedding models matched tabular relapse models throughout therapy and outperformed tabular EOT models at months 3 and 4 (ΔROC-AUC 0.12 and 0.
Time-resolved predictability of end-of-therapy outcome and relapse after cure in Phase 3 tuberculosis trials
Using harmonised clinical data from two Phase 3 trials (2,918 participants), this study trained monthly tabular models from baseline to therapy end for time-resolved prediction of end-of-therapy (EOT) outcomes and post-treatment relapse, finding that EOT outcome prediction improved after month 3 (ROC-AUC up to 0.84, driven by sputum-smear and solid culture), whereas relapse prediction among those with favourable EOT outcomes and completed follow-up improved only modestly through month 3 (ROC-AUC 0.58-0.63) before declining, with age, sex, clinical symptoms and bacterial burden contributing most; large language model-derived embedding models matched tabular relapse models throughout therapy and outperformed tabular EOT models at months 3 and 4 (ΔROC-AUC 0.12 and 0.
Using harmonised clinical data from two Phase 3 trials (2,918 participants), this study trained monthly tabular models from baseline to therapy end for time-resolved prediction of end-of-therapy (EOT) outcomes and post-treatment relapse, finding that EOT outcome prediction improved after month 3 (ROC-AUC up to 0.84, driven by sputum-smear and solid culture), whereas relapse prediction among those with favourable EOT outcomes and completed follow-up improved only modestly through month 3 (ROC-AUC 0.58-0.63) before declining, with age, sex, clinical symptoms and bacterial burden contributing most; large language model-derived embedding models matched tabular relapse models throughout therapy and outperformed tabular EOT models at months 3 and 4 (ΔROC-AUC 0.12 and 0.
Research Square Sep 15, 2026
This preprint trained five backbones under duplicate-controlled image-level partitioning (387 thyroid ultrasound images and 1,332 fine-needle aspiration cytology blocks from 385 public cases, labelled by postoperative diagnosis) and applied the frozen models to TN3K (n = 1,228, dataset-provided benign/malignant labels) and to a DDTI endpoint derived from radiologist TI-RADS categories (n = 637), finding ultrasound ensemble AUROC 0.825 and ResNet-50 0.888 on TN3K versus 0.477 and 0.437 against the TI-RADS-derived endpoint, with a cytology benchmark AUROC of 0.986 (0.970–0.998) against 0.733 for the ultrasound ensemble, while noting that cohort, acquisition and endpoint changed together so the contrast cannot be attributed to label definition alone.
This preprint trained five backbones under duplicate-controlled image-level partitioning (387 thyroid ultrasound images and 1,332 fine-needle aspiration cytology blocks from 385 public cases, labelled by postoperative diagnosis) and applied the frozen models to TN3K (n = 1,228, dataset-provided benign/malignant labels) and to a DDTI endpoint derived from radiologist TI-RADS categories (n = 637), finding ultrasound ensemble AUROC 0.825 and ResNet-50 0.888 on TN3K versus 0.477 and 0.437 against the TI-RADS-derived endpoint, with a cytology benchmark AUROC of 0.986 (0.970–0.998) against 0.733 for the ultrasound ensemble, while noting that cohort, acquisition and endpoint changed together so the contrast cannot be attributed to label definition alone.
Cross-cohort evaluation of thyroid ultrasound deep learning across different outcome definitions: a duplicate-controlled benchmark
This preprint trained five backbones under duplicate-controlled image-level partitioning (387 thyroid ultrasound images and 1,332 fine-needle aspiration cytology blocks from 385 public cases, labelled by postoperative diagnosis) and applied the frozen models to TN3K (n = 1,228, dataset-provided benign/malignant labels) and to a DDTI endpoint derived from radiologist TI-RADS categories (n = 637), finding ultrasound ensemble AUROC 0.825 and ResNet-50 0.888 on TN3K versus 0.477 and 0.437 against the TI-RADS-derived endpoint, with a cytology benchmark AUROC of 0.986 (0.970–0.998) against 0.733 for the ultrasound ensemble, while noting that cohort, acquisition and endpoint changed together so the contrast cannot be attributed to label definition alone.
This preprint trained five backbones under duplicate-controlled image-level partitioning (387 thyroid ultrasound images and 1,332 fine-needle aspiration cytology blocks from 385 public cases, labelled by postoperative diagnosis) and applied the frozen models to TN3K (n = 1,228, dataset-provided benign/malignant labels) and to a DDTI endpoint derived from radiologist TI-RADS categories (n = 637), finding ultrasound ensemble AUROC 0.825 and ResNet-50 0.888 on TN3K versus 0.477 and 0.437 against the TI-RADS-derived endpoint, with a cytology benchmark AUROC of 0.986 (0.970–0.998) against 0.733 for the ultrasound ensemble, while noting that cohort, acquisition and endpoint changed together so the contrast cannot be attributed to label definition alone.
Journal of minimally invasive surgery Sep 15, 2026
This review reorganizes the surgical AI literature around decision points rather than algorithmic type or predicted outcomes, examining PubMed-indexed studies from 2015 to 2025 and finding that preoperative AI predominantly supports patient selection and treatment planning under diagnostic uncertainty while postoperative AI mainly supports time-sensitive management and prognostic assessment, thereby reframing surgical AI as an integral component of phase-specific clinical decision pathways across the surgical care continuum rather than a standalone predictive instrument.
This review reorganizes the surgical AI literature around decision points rather than algorithmic type or predicted outcomes, examining PubMed-indexed studies from 2015 to 2025 and finding that preoperative AI predominantly supports patient selection and treatment planning under diagnostic uncertainty while postoperative AI mainly supports time-sensitive management and prognostic assessment, thereby reframing surgical AI as an integral component of phase-specific clinical decision pathways across the surgical care continuum rather than a standalone predictive instrument.
Decision-centered artificial intelligence for perioperative care outside the operating room: a practical review for surgeons
This review reorganizes the surgical AI literature around decision points rather than algorithmic type or predicted outcomes, examining PubMed-indexed studies from 2015 to 2025 and finding that preoperative AI predominantly supports patient selection and treatment planning under diagnostic uncertainty while postoperative AI mainly supports time-sensitive management and prognostic assessment, thereby reframing surgical AI as an integral component of phase-specific clinical decision pathways across the surgical care continuum rather than a standalone predictive instrument.
This review reorganizes the surgical AI literature around decision points rather than algorithmic type or predicted outcomes, examining PubMed-indexed studies from 2015 to 2025 and finding that preoperative AI predominantly supports patient selection and treatment planning under diagnostic uncertainty while postoperative AI mainly supports time-sensitive management and prognostic assessment, thereby reframing surgical AI as an integral component of phase-specific clinical decision pathways across the surgical care continuum rather than a standalone predictive instrument.
Zhonghua yi xue za zhi Sep 15, 2026
From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.
From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.
Artificial Intelligence Empowering Spinal Deformity Surgery: From Clinical Assessment to Decision Integration
From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.
From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Sep 15, 2026
This article systematically reviews the current applications, main problems, and future directions of medical AI image technology in the precision diagnosis and treatment of head and neck tumors, noting that the technology enables precise quantification of imaging features and opens new paths for early identification, efficacy evaluation, and prognosis prediction, while still facing challenges such as insufficient data standardization, limited model generalization, poor interpretability, and insufficient clinical translation.
This article systematically reviews the current applications, main problems, and future directions of medical AI image technology in the precision diagnosis and treatment of head and neck tumors, noting that the technology enables precise quantification of imaging features and opens new paths for early identification, efficacy evaluation, and prognosis prediction, while still facing challenges such as insufficient data standardization, limited model generalization, poor interpretability, and insufficient clinical translation.
Advances in Medical Image-Based Artificial Intelligence for Precision Diagnosis and Treatment of Head and Neck Tumors
This article systematically reviews the current applications, main problems, and future directions of medical AI image technology in the precision diagnosis and treatment of head and neck tumors, noting that the technology enables precise quantification of imaging features and opens new paths for early identification, efficacy evaluation, and prognosis prediction, while still facing challenges such as insufficient data standardization, limited model generalization, poor interpretability, and insufficient clinical translation.
This article systematically reviews the current applications, main problems, and future directions of medical AI image technology in the precision diagnosis and treatment of head and neck tumors, noting that the technology enables precise quantification of imaging features and opens new paths for early identification, efficacy evaluation, and prognosis prediction, while still facing challenges such as insufficient data standardization, limited model generalization, poor interpretability, and insufficient clinical translation.

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