Medicine & Health

466 items

International journal of ophthalmology Sep 18, 2026
This systematic review evaluates 34 studies from 2018 to 2025, finding that AI often exceeds 90% accuracy and can match or outperform expert clinicians in diagnosing common ocular diseases such as diabetic retinopathy, glaucoma, retinopathy of prematurity, and age-related macular degeneration, yet real-world deployment remains constrained by three gaps—disjointed integration into clinical workflows, lack of transparency in AI decision-making, and poor generalizability across diverse populations—and it proposes actionable pathways to bridge the "last-mile gap" between research and clinical practice.
This systematic review evaluates 34 studies from 2018 to 2025, finding that AI often exceeds 90% accuracy and can match or outperform expert clinicians in diagnosing common ocular diseases such as diabetic retinopathy, glaucoma, retinopathy of prematurity, and age-related macular degeneration, yet real-world deployment remains constrained by three gaps—disjointed integration into clinical workflows, lack of transparency in AI decision-making, and poor generalizability across diverse populations—and it proposes actionable pathways to bridge the "last-mile gap" between research and clinical practice.
Why Isn't AI in Eye Clinics Yet? A Systematic Review of Barriers and Pathways for AI-Based Fundus Image Diagnostics
This systematic review evaluates 34 studies from 2018 to 2025, finding that AI often exceeds 90% accuracy and can match or outperform expert clinicians in diagnosing common ocular diseases such as diabetic retinopathy, glaucoma, retinopathy of prematurity, and age-related macular degeneration, yet real-world deployment remains constrained by three gaps—disjointed integration into clinical workflows, lack of transparency in AI decision-making, and poor generalizability across diverse populations—and it proposes actionable pathways to bridge the "last-mile gap" between research and clinical practice.
This systematic review evaluates 34 studies from 2018 to 2025, finding that AI often exceeds 90% accuracy and can match or outperform expert clinicians in diagnosing common ocular diseases such as diabetic retinopathy, glaucoma, retinopathy of prematurity, and age-related macular degeneration, yet real-world deployment remains constrained by three gaps—disjointed integration into clinical workflows, lack of transparency in AI decision-making, and poor generalizability across diverse populations—and it proposes actionable pathways to bridge the "last-mile gap" between research and clinical practice.
Journal of the American College of Surgeons Sep 18, 2026
Using clinical notes from 105 patients in the NSQIP Breast Reconstruction pilot program (July 1, 2024–February 28, 2025), manually de-identified and processed with a customized ChatGPT 4.1 workflow targeting individual variables against a faculty plastic surgeon reference standard, this study evaluated 9,048 data points and found overall abstraction accuracy of 99.33% (61 errors) for the LLM versus 98.19% (164 errors) for human abstraction, with McNemar and Chi-square p<0.001; the LLM exceeded human abstraction for operative and postoperative variables but was slightly lower for preoperative variables, and the most frequent LLM errors involved prior breast surgical history (29/61) and prepectoral versus subpectoral implant or expander placement.
Using clinical notes from 105 patients in the NSQIP Breast Reconstruction pilot program (July 1, 2024–February 28, 2025), manually de-identified and processed with a customized ChatGPT 4.1 workflow targeting individual variables against a faculty plastic surgeon reference standard, this study evaluated 9,048 data points and found overall abstraction accuracy of 99.33% (61 errors) for the LLM versus 98.19% (164 errors) for human abstraction, with McNemar and Chi-square p<0.001; the LLM exceeded human abstraction for operative and postoperative variables but was slightly lower for preoperative variables, and the most frequent LLM errors involved prior breast surgical history (29/61) and prepectoral versus subpectoral implant or expander placement.
Large Language Model Data Abstraction Demonstrates Accuracy and Reliability for NSQIP
Using clinical notes from 105 patients in the NSQIP Breast Reconstruction pilot program (July 1, 2024–February 28, 2025), manually de-identified and processed with a customized ChatGPT 4.1 workflow targeting individual variables against a faculty plastic surgeon reference standard, this study evaluated 9,048 data points and found overall abstraction accuracy of 99.33% (61 errors) for the LLM versus 98.19% (164 errors) for human abstraction, with McNemar and Chi-square p<0.001; the LLM exceeded human abstraction for operative and postoperative variables but was slightly lower for preoperative variables, and the most frequent LLM errors involved prior breast surgical history (29/61) and prepectoral versus subpectoral implant or expander placement.
Using clinical notes from 105 patients in the NSQIP Breast Reconstruction pilot program (July 1, 2024–February 28, 2025), manually de-identified and processed with a customized ChatGPT 4.1 workflow targeting individual variables against a faculty plastic surgeon reference standard, this study evaluated 9,048 data points and found overall abstraction accuracy of 99.33% (61 errors) for the LLM versus 98.19% (164 errors) for human abstraction, with McNemar and Chi-square p<0.001; the LLM exceeded human abstraction for operative and postoperative variables but was slightly lower for preoperative variables, and the most frequent LLM errors involved prior breast surgical history (29/61) and prepectoral versus subpectoral implant or expander placement.
The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association Sep 18, 2026
In this prospective, single-center study, speech samples from 40 children aged 2 to 17 (including individuals with velopharyngeal dysfunction, conditions associated with VPD, and healthy participants) were collected during speech-language pathologist-guided evaluation with consensus CAPS-A-AM ratings, and mel spectrograms of high vowels /i/ and /u/ from sustained vowels, isolated words, and sentences were used to train logistic regression, an EfficientNet-V2-S attention multiple-instance-learning CNN, and a CNN-XGBoost hybrid for binary hypernasality classification at two CAPS-A-AM thresholds (absent 0 versus any hypernasality 1-4, and absent/borderline 0-1 versus mild-to-severe 2-4); multiple independent modeling approaches detected clinically rated hypernasality, with EfficientNet-V2-S a
In this prospective, single-center study, speech samples from 40 children aged 2 to 17 (including individuals with velopharyngeal dysfunction, conditions associated with VPD, and healthy participants) were collected during speech-language pathologist-guided evaluation with consensus CAPS-A-AM ratings, and mel spectrograms of high vowels /i/ and /u/ from sustained vowels, isolated words, and sentences were used to train logistic regression, an EfficientNet-V2-S attention multiple-instance-learning CNN, and a CNN-XGBoost hybrid for binary hypernasality classification at two CAPS-A-AM thresholds (absent 0 versus any hypernasality 1-4, and absent/borderline 0-1 versus mild-to-severe 2-4); multiple independent modeling approaches detected clinically rated hypernasality, with EfficientNet-V2-S a
Artificial Intelligence-Based Hypernasality Diagnosis Using CAPS-A-AM Rated Speech Samples in Pediatric Velopharyngeal Dysfunction
In this prospective, single-center study, speech samples from 40 children aged 2 to 17 (including individuals with velopharyngeal dysfunction, conditions associated with VPD, and healthy participants) were collected during speech-language pathologist-guided evaluation with consensus CAPS-A-AM ratings, and mel spectrograms of high vowels /i/ and /u/ from sustained vowels, isolated words, and sentences were used to train logistic regression, an EfficientNet-V2-S attention multiple-instance-learning CNN, and a CNN-XGBoost hybrid for binary hypernasality classification at two CAPS-A-AM thresholds (absent 0 versus any hypernasality 1-4, and absent/borderline 0-1 versus mild-to-severe 2-4); multiple independent modeling approaches detected clinically rated hypernasality, with EfficientNet-V2-S a
In this prospective, single-center study, speech samples from 40 children aged 2 to 17 (including individuals with velopharyngeal dysfunction, conditions associated with VPD, and healthy participants) were collected during speech-language pathologist-guided evaluation with consensus CAPS-A-AM ratings, and mel spectrograms of high vowels /i/ and /u/ from sustained vowels, isolated words, and sentences were used to train logistic regression, an EfficientNet-V2-S attention multiple-instance-learning CNN, and a CNN-XGBoost hybrid for binary hypernasality classification at two CAPS-A-AM thresholds (absent 0 versus any hypernasality 1-4, and absent/borderline 0-1 versus mild-to-severe 2-4); multiple independent modeling approaches detected clinically rated hypernasality, with EfficientNet-V2-S a
Medical Science Monitor Sep 18, 2026
This narrative review synthesizes recent clinical studies and technological advances to map virtual reality (VR) applications in otolaryngology nursing across patient-facing uses (preoperative education, pain and anxiety relief, vestibular rehabilitation, swallowing assessment and therapy, hearing rehabilitation, and tinnitus management) and nurse-facing uses (professional training and skills development), reporting that vestibular rehabilitation is supported by strong evidence from randomized controlled trials while swallowing therapy and tinnitus management rest mainly on preliminary exploratory studies, and discussing technical, economic, and clinical barriers alongside prospects for integrating VR with artificial intelligence, wearable biosensors, and telerehabilitation platforms.
This narrative review synthesizes recent clinical studies and technological advances to map virtual reality (VR) applications in otolaryngology nursing across patient-facing uses (preoperative education, pain and anxiety relief, vestibular rehabilitation, swallowing assessment and therapy, hearing rehabilitation, and tinnitus management) and nurse-facing uses (professional training and skills development), reporting that vestibular rehabilitation is supported by strong evidence from randomized controlled trials while swallowing therapy and tinnitus management rest mainly on preliminary exploratory studies, and discussing technical, economic, and clinical barriers alongside prospects for integrating VR with artificial intelligence, wearable biosensors, and telerehabilitation platforms.
Virtual Reality Technology in Otolaryngology Patient Care Management: A Narrative Review of Current Evidence and Future Prospects
This narrative review synthesizes recent clinical studies and technological advances to map virtual reality (VR) applications in otolaryngology nursing across patient-facing uses (preoperative education, pain and anxiety relief, vestibular rehabilitation, swallowing assessment and therapy, hearing rehabilitation, and tinnitus management) and nurse-facing uses (professional training and skills development), reporting that vestibular rehabilitation is supported by strong evidence from randomized controlled trials while swallowing therapy and tinnitus management rest mainly on preliminary exploratory studies, and discussing technical, economic, and clinical barriers alongside prospects for integrating VR with artificial intelligence, wearable biosensors, and telerehabilitation platforms.
This narrative review synthesizes recent clinical studies and technological advances to map virtual reality (VR) applications in otolaryngology nursing across patient-facing uses (preoperative education, pain and anxiety relief, vestibular rehabilitation, swallowing assessment and therapy, hearing rehabilitation, and tinnitus management) and nurse-facing uses (professional training and skills development), reporting that vestibular rehabilitation is supported by strong evidence from randomized controlled trials while swallowing therapy and tinnitus management rest mainly on preliminary exploratory studies, and discussing technical, economic, and clinical barriers alongside prospects for integrating VR with artificial intelligence, wearable biosensors, and telerehabilitation platforms.
Journal of global health Sep 18, 2026
This study compiled a national monthly series of hepatitis B notifications in mainland China from January 2004 to December 2025, applied CEEMDAN to isolate multiscale temporal components, and trained four models (GRU, CNN, SVM, and a Transformer encoder) with KOA-optimised hyperparameters on a sliding 12-month window recursively extended to 24-month horizons; the Transformer delivered the best out-of-sample fit on the held-out test split (MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, R² = 0.928), SVM ranked second, CNN outperformed GRU but not SVM, and forecasts for 2026-2027 remain elevated, signalling little improvement and even possible resurgence.
This study compiled a national monthly series of hepatitis B notifications in mainland China from January 2004 to December 2025, applied CEEMDAN to isolate multiscale temporal components, and trained four models (GRU, CNN, SVM, and a Transformer encoder) with KOA-optimised hyperparameters on a sliding 12-month window recursively extended to 24-month horizons; the Transformer delivered the best out-of-sample fit on the held-out test split (MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, R² = 0.928), SVM ranked second, CNN outperformed GRU but not SVM, and forecasts for 2026-2027 remain elevated, signalling little improvement and even possible resurgence.
CEEMDAN-decomposed time series forecasting of reported hepatitis B cases using KOA-optimised deep learning: a nationwide study in mainland China (2004-2027)
This study compiled a national monthly series of hepatitis B notifications in mainland China from January 2004 to December 2025, applied CEEMDAN to isolate multiscale temporal components, and trained four models (GRU, CNN, SVM, and a Transformer encoder) with KOA-optimised hyperparameters on a sliding 12-month window recursively extended to 24-month horizons; the Transformer delivered the best out-of-sample fit on the held-out test split (MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, R² = 0.928), SVM ranked second, CNN outperformed GRU but not SVM, and forecasts for 2026-2027 remain elevated, signalling little improvement and even possible resurgence.
This study compiled a national monthly series of hepatitis B notifications in mainland China from January 2004 to December 2025, applied CEEMDAN to isolate multiscale temporal components, and trained four models (GRU, CNN, SVM, and a Transformer encoder) with KOA-optimised hyperparameters on a sliding 12-month window recursively extended to 24-month horizons; the Transformer delivered the best out-of-sample fit on the held-out test split (MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, R² = 0.928), SVM ranked second, CNN outperformed GRU but not SVM, and forecasts for 2026-2027 remain elevated, signalling little improvement and even possible resurgence.
World journal of transplantation Sep 18, 2026
This review summarizes applications of artificial intelligence and machine learning across the transplantation surgery care pathway, covering preoperative anatomical assessment, graft weight estimation and candidate selection, perioperative prediction of massive transfusion, hemorrhage and acute kidney injury plus robotic-assisted surgery, postoperative early prediction of sepsis, pneumonia and graft dysfunction with long-term monitoring, and cross-cutting innovations such as hyperspectral imaging and automated histopathological evaluation, while noting that multimodal models integrating electronic health records, intraoperative signals, ultrasound and histology can bridge diagnostic, prognostic and therapeutic decision-making, though clinical translation still requires rigorous validation
This review summarizes applications of artificial intelligence and machine learning across the transplantation surgery care pathway, covering preoperative anatomical assessment, graft weight estimation and candidate selection, perioperative prediction of massive transfusion, hemorrhage and acute kidney injury plus robotic-assisted surgery, postoperative early prediction of sepsis, pneumonia and graft dysfunction with long-term monitoring, and cross-cutting innovations such as hyperspectral imaging and automated histopathological evaluation, while noting that multimodal models integrating electronic health records, intraoperative signals, ultrasound and histology can bridge diagnostic, prognostic and therapeutic decision-making, though clinical translation still requires rigorous validation
Artificial Intelligence and Machine Learning in Transplantation Surgery Care Pathway
This review summarizes applications of artificial intelligence and machine learning across the transplantation surgery care pathway, covering preoperative anatomical assessment, graft weight estimation and candidate selection, perioperative prediction of massive transfusion, hemorrhage and acute kidney injury plus robotic-assisted surgery, postoperative early prediction of sepsis, pneumonia and graft dysfunction with long-term monitoring, and cross-cutting innovations such as hyperspectral imaging and automated histopathological evaluation, while noting that multimodal models integrating electronic health records, intraoperative signals, ultrasound and histology can bridge diagnostic, prognostic and therapeutic decision-making, though clinical translation still requires rigorous validation
This review summarizes applications of artificial intelligence and machine learning across the transplantation surgery care pathway, covering preoperative anatomical assessment, graft weight estimation and candidate selection, perioperative prediction of massive transfusion, hemorrhage and acute kidney injury plus robotic-assisted surgery, postoperative early prediction of sepsis, pneumonia and graft dysfunction with long-term monitoring, and cross-cutting innovations such as hyperspectral imaging and automated histopathological evaluation, while noting that multimodal models integrating electronic health records, intraoperative signals, ultrasound and histology can bridge diagnostic, prognostic and therapeutic decision-making, though clinical translation still requires rigorous validation
Current opinion in oncology Sep 18, 2026
This review focuses on studies published over the past 18 months and examines the role of molecular PET imaging in differentiating treatment-related changes, especially pseudoprogression, from true tumor progression in gliomas, suggesting that static amino acid PET, particularly [18F]FET, may provide diagnostic information comparable to dynamic acquisition while simplifying protocols, that [68Ga]FAPI PET may add information on the tumor microenvironment, and that quantitative PET parameters are affected by reconstruction algorithms, reference regions and segmentation strategies, with artificial intelligence, radiomics and automated segmentation potentially improving the integration of multimodal and quantitative assessment.
This review focuses on studies published over the past 18 months and examines the role of molecular PET imaging in differentiating treatment-related changes, especially pseudoprogression, from true tumor progression in gliomas, suggesting that static amino acid PET, particularly [18F]FET, may provide diagnostic information comparable to dynamic acquisition while simplifying protocols, that [68Ga]FAPI PET may add information on the tumor microenvironment, and that quantitative PET parameters are affected by reconstruction algorithms, reference regions and segmentation strategies, with artificial intelligence, radiomics and automated segmentation potentially improving the integration of multimodal and quantitative assessment.
The Evolving Role of Nuclear Medicine in Differentiating Pseudoprogression from Tumor Progression in Gliomas
This review focuses on studies published over the past 18 months and examines the role of molecular PET imaging in differentiating treatment-related changes, especially pseudoprogression, from true tumor progression in gliomas, suggesting that static amino acid PET, particularly [18F]FET, may provide diagnostic information comparable to dynamic acquisition while simplifying protocols, that [68Ga]FAPI PET may add information on the tumor microenvironment, and that quantitative PET parameters are affected by reconstruction algorithms, reference regions and segmentation strategies, with artificial intelligence, radiomics and automated segmentation potentially improving the integration of multimodal and quantitative assessment.
This review focuses on studies published over the past 18 months and examines the role of molecular PET imaging in differentiating treatment-related changes, especially pseudoprogression, from true tumor progression in gliomas, suggesting that static amino acid PET, particularly [18F]FET, may provide diagnostic information comparable to dynamic acquisition while simplifying protocols, that [68Ga]FAPI PET may add information on the tumor microenvironment, and that quantitative PET parameters are affected by reconstruction algorithms, reference regions and segmentation strategies, with artificial intelligence, radiomics and automated segmentation potentially improving the integration of multimodal and quantitative assessment.
Journal of gastroenterology and hepatology Sep 18, 2026
This narrative review is the first to synthesize the convergence of artificial intelligence and endohepatology into four functional pillars—intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support—and proposes a three-tier readiness framework to separate demonstrated capabilities from extrapolated and conceptual applications, concluding that the field is still very early-stage but that AI has strong potential to turn endohepatology into a single machine-driven diagnostic and therapeutic platform provided standardized datasets, prospective validation, and clear regulatory and governance standards are in place.
This narrative review is the first to synthesize the convergence of artificial intelligence and endohepatology into four functional pillars—intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support—and proposes a three-tier readiness framework to separate demonstrated capabilities from extrapolated and conceptual applications, concluding that the field is still very early-stage but that AI has strong potential to turn endohepatology into a single machine-driven diagnostic and therapeutic platform provided standardized datasets, prospective validation, and clear regulatory and governance standards are in place.
Artificial Intelligence in Endohepatology: A Roadmap Toward an Intelligent One-Stop Shop for Liver-Directed Endoscopy
This narrative review is the first to synthesize the convergence of artificial intelligence and endohepatology into four functional pillars—intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support—and proposes a three-tier readiness framework to separate demonstrated capabilities from extrapolated and conceptual applications, concluding that the field is still very early-stage but that AI has strong potential to turn endohepatology into a single machine-driven diagnostic and therapeutic platform provided standardized datasets, prospective validation, and clear regulatory and governance standards are in place.
This narrative review is the first to synthesize the convergence of artificial intelligence and endohepatology into four functional pillars—intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support—and proposes a three-tier readiness framework to separate demonstrated capabilities from extrapolated and conceptual applications, concluding that the field is still very early-stage but that AI has strong potential to turn endohepatology into a single machine-driven diagnostic and therapeutic platform provided standardized datasets, prospective validation, and clear regulatory and governance standards are in place.
Current opinion in oncology Sep 18, 2026
This review indicates that TROP2-directed antibody-drug conjugates (ADCs) are becoming increasingly integrated into breast cancer treatment across multiple disease settings, and that although sacituzumab govitecan and datopotamab deruxtecan share TROP2 targeting and topoisomerase I inhibition, their safety profiles differ—sacituzumab govitecan is mainly associated with neutropenia and diarrhoea, whereas datopotamab deruxtecan is characterized by stomatitis, ocular surface events and a low but clinically relevant risk of interstitial lung disease/pneumonitis—differences attributed to the integrated effects of payload, linker stability, drug-to-antibody ratio, tissue distribution and target-independent uptake, with corresponding agent-specific, proactive and increasingly individualized preve
This review indicates that TROP2-directed antibody-drug conjugates (ADCs) are becoming increasingly integrated into breast cancer treatment across multiple disease settings, and that although sacituzumab govitecan and datopotamab deruxtecan share TROP2 targeting and topoisomerase I inhibition, their safety profiles differ—sacituzumab govitecan is mainly associated with neutropenia and diarrhoea, whereas datopotamab deruxtecan is characterized by stomatitis, ocular surface events and a low but clinically relevant risk of interstitial lung disease/pneumonitis—differences attributed to the integrated effects of payload, linker stability, drug-to-antibody ratio, tissue distribution and target-independent uptake, with corresponding agent-specific, proactive and increasingly individualized preve
Safety Management of Anti-TROP2 ADCs in Patients with Breast Cancer
This review indicates that TROP2-directed antibody-drug conjugates (ADCs) are becoming increasingly integrated into breast cancer treatment across multiple disease settings, and that although sacituzumab govitecan and datopotamab deruxtecan share TROP2 targeting and topoisomerase I inhibition, their safety profiles differ—sacituzumab govitecan is mainly associated with neutropenia and diarrhoea, whereas datopotamab deruxtecan is characterized by stomatitis, ocular surface events and a low but clinically relevant risk of interstitial lung disease/pneumonitis—differences attributed to the integrated effects of payload, linker stability, drug-to-antibody ratio, tissue distribution and target-independent uptake, with corresponding agent-specific, proactive and increasingly individualized preve
This review indicates that TROP2-directed antibody-drug conjugates (ADCs) are becoming increasingly integrated into breast cancer treatment across multiple disease settings, and that although sacituzumab govitecan and datopotamab deruxtecan share TROP2 targeting and topoisomerase I inhibition, their safety profiles differ—sacituzumab govitecan is mainly associated with neutropenia and diarrhoea, whereas datopotamab deruxtecan is characterized by stomatitis, ocular surface events and a low but clinically relevant risk of interstitial lung disease/pneumonitis—differences attributed to the integrated effects of payload, linker stability, drug-to-antibody ratio, tissue distribution and target-independent uptake, with corresponding agent-specific, proactive and increasingly individualized preve
FEBS letters Sep 18, 2026
This review surveys the landscape of deep learning-driven protein design pipelines, discusses tailored applications in peptide, small molecule, binder, vaccine, and antibody design, argues that current confidence metrics for filtering and evaluating designs remain optimized for static protein interfaces and can fail on underrepresented or conformationally complex targets, proposes ensemble-based methods as a promising avenue for improving design success rates, and highlights emerging strategies such as fold-switching scaffolds and molecular glues realized through engineered cyclic peptides that expand the functional scope of designed proteins.
This review surveys the landscape of deep learning-driven protein design pipelines, discusses tailored applications in peptide, small molecule, binder, vaccine, and antibody design, argues that current confidence metrics for filtering and evaluating designs remain optimized for static protein interfaces and can fail on underrepresented or conformationally complex targets, proposes ensemble-based methods as a promising avenue for improving design success rates, and highlights emerging strategies such as fold-switching scaffolds and molecular glues realized through engineered cyclic peptides that expand the functional scope of designed proteins.
Prospecting the Protein Design Landscape: From High-Affinity Binders to Functionally Switchable Proteins
This review surveys the landscape of deep learning-driven protein design pipelines, discusses tailored applications in peptide, small molecule, binder, vaccine, and antibody design, argues that current confidence metrics for filtering and evaluating designs remain optimized for static protein interfaces and can fail on underrepresented or conformationally complex targets, proposes ensemble-based methods as a promising avenue for improving design success rates, and highlights emerging strategies such as fold-switching scaffolds and molecular glues realized through engineered cyclic peptides that expand the functional scope of designed proteins.
This review surveys the landscape of deep learning-driven protein design pipelines, discusses tailored applications in peptide, small molecule, binder, vaccine, and antibody design, argues that current confidence metrics for filtering and evaluating designs remain optimized for static protein interfaces and can fail on underrepresented or conformationally complex targets, proposes ensemble-based methods as a promising avenue for improving design success rates, and highlights emerging strategies such as fold-switching scaffolds and molecular glues realized through engineered cyclic peptides that expand the functional scope of designed proteins.

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