Medicine & Health

455 items

American Journal of Respiratory and Critical Care Medicine Oct 1, 2026
Using an unbiased druggable-genome siRNA screen, this study identified ADAMTS14 as a regulator of YAP nuclear translocation; single-cell and spatial transcriptomics of IPF patient lungs localized ADAMTS14 to a CTHRC1-high myofibroblast population enriched in fibroblastic foci, and knockdown or knockout of ADAMTS14 in IPF patient-derived lung fibroblasts reduced pro-fibrotic, matrisome, and TGFβ-response gene expression; mechanistically, collagen V was identified as an ADAMTS14 substrate whose impaired processing shifted the COL1:COL5 ratio from about 50:1 to about 20:1, producing an unstable matrix with disorganized focal adhesions and reduced FAK-AKT signaling and force transmission.
Using an unbiased druggable-genome siRNA screen, this study identified ADAMTS14 as a regulator of YAP nuclear translocation; single-cell and spatial transcriptomics of IPF patient lungs localized ADAMTS14 to a CTHRC1-high myofibroblast population enriched in fibroblastic foci, and knockdown or knockout of ADAMTS14 in IPF patient-derived lung fibroblasts reduced pro-fibrotic, matrisome, and TGFβ-response gene expression; mechanistically, collagen V was identified as an ADAMTS14 substrate whose impaired processing shifted the COL1:COL5 ratio from about 50:1 to about 20:1, producing an unstable matrix with disorganized focal adhesions and reduced FAK-AKT signaling and force transmission.
ADAMTS14 cleaves collagen V to keep matrix stable; its loss weakens YAP nuclear translocation and IPF fibroblast activation
Using an unbiased druggable-genome siRNA screen, this study identified ADAMTS14 as a regulator of YAP nuclear translocation; single-cell and spatial transcriptomics of IPF patient lungs localized ADAMTS14 to a CTHRC1-high myofibroblast population enriched in fibroblastic foci, and knockdown or knockout of ADAMTS14 in IPF patient-derived lung fibroblasts reduced pro-fibrotic, matrisome, and TGFβ-response gene expression; mechanistically, collagen V was identified as an ADAMTS14 substrate whose impaired processing shifted the COL1:COL5 ratio from about 50:1 to about 20:1, producing an unstable matrix with disorganized focal adhesions and reduced FAK-AKT signaling and force transmission.
Using an unbiased druggable-genome siRNA screen, this study identified ADAMTS14 as a regulator of YAP nuclear translocation; single-cell and spatial transcriptomics of IPF patient lungs localized ADAMTS14 to a CTHRC1-high myofibroblast population enriched in fibroblastic foci, and knockdown or knockout of ADAMTS14 in IPF patient-derived lung fibroblasts reduced pro-fibrotic, matrisome, and TGFβ-response gene expression; mechanistically, collagen V was identified as an ADAMTS14 substrate whose impaired processing shifted the COL1:COL5 ratio from about 50:1 to about 20:1, producing an unstable matrix with disorganized focal adhesions and reduced FAK-AKT signaling and force transmission.
发表出处待核验 Oct 1, 2026
This review, using obsessive-compulsive disorder (OCD) as its example, surveys the advances and constraints of big data approaches to three questions—what causes the disorder, which treatments work best for whom, and how to widen access to evidence-based care—reporting that large-scale pooled data such as ENIGMA-OCD show some previously reported effects to be small but reproducible while others do not replicate at scale or appear to be medication effects, that the Global OCD Study harmonized methods across five international sites and recruited 250 unmedicated adults with OCD and 250 matched healthy volunteers and found relatively few case-control brain differences but more informative associations between specific brain and cognitive measures and OC clinical profiles, and that big data ar
This review, using obsessive-compulsive disorder (OCD) as its example, surveys the advances and constraints of big data approaches to three questions—what causes the disorder, which treatments work best for whom, and how to widen access to evidence-based care—reporting that large-scale pooled data such as ENIGMA-OCD show some previously reported effects to be small but reproducible while others do not replicate at scale or appear to be medication effects, that the Global OCD Study harmonized methods across five international sites and recruited 250 unmedicated adults with OCD and 250 matched healthy volunteers and found relatively few case-control brain differences but more informative associations between specific brain and cognitive measures and OC clinical profiles, and that big data ar
Using OCD as its example, a review argues that big data in psychiatry must separate etiology from pathophysiology and stay hypothesis-driven to answer a specific disorder's three core questions
This review, using obsessive-compulsive disorder (OCD) as its example, surveys the advances and constraints of big data approaches to three questions—what causes the disorder, which treatments work best for whom, and how to widen access to evidence-based care—reporting that large-scale pooled data such as ENIGMA-OCD show some previously reported effects to be small but reproducible while others do not replicate at scale or appear to be medication effects, that the Global OCD Study harmonized methods across five international sites and recruited 250 unmedicated adults with OCD and 250 matched healthy volunteers and found relatively few case-control brain differences but more informative associations between specific brain and cognitive measures and OC clinical profiles, and that big data ar
This review, using obsessive-compulsive disorder (OCD) as its example, surveys the advances and constraints of big data approaches to three questions—what causes the disorder, which treatments work best for whom, and how to widen access to evidence-based care—reporting that large-scale pooled data such as ENIGMA-OCD show some previously reported effects to be small but reproducible while others do not replicate at scale or appear to be medication effects, that the Global OCD Study harmonized methods across five international sites and recruited 250 unmedicated adults with OCD and 250 matched healthy volunteers and found relatively few case-control brain differences but more informative associations between specific brain and cognitive measures and OC clinical profiles, and that big data ar
European Journal Pharmaceutical and Medical Research Oct 1, 2026
This review discusses the fundamental concepts, workflow, statistical tools, applications, regulatory significance, advantages, challenges, and future perspectives of Analytical Quality by Design (AQbD), noting that AQbD generally begins with defining an Analytical Target Profile (ATP) and proceeds through identification of critical analytical attributes and performance characteristics, risk assessment, identification of critical method parameters, application of Design of Experiments (DoE), establishment of a Method Operable Design Region (MODR), and development of an analytical control strategy, and suggesting that integration of AQbD with multivariate analysis, chemometrics, automation, artificial intelligence, machine learning, green analytical chemistry, and real-time analytical techn
This review discusses the fundamental concepts, workflow, statistical tools, applications, regulatory significance, advantages, challenges, and future perspectives of Analytical Quality by Design (AQbD), noting that AQbD generally begins with defining an Analytical Target Profile (ATP) and proceeds through identification of critical analytical attributes and performance characteristics, risk assessment, identification of critical method parameters, application of Design of Experiments (DoE), establishment of a Method Operable Design Region (MODR), and development of an analytical control strategy, and suggesting that integration of AQbD with multivariate analysis, chemometrics, automation, artificial intelligence, machine learning, green analytical chemistry, and real-time analytical techn
AQbD review maps analytical method development from ATP to MODR and folds in ICH Q14 and Q2(R2)
This review discusses the fundamental concepts, workflow, statistical tools, applications, regulatory significance, advantages, challenges, and future perspectives of Analytical Quality by Design (AQbD), noting that AQbD generally begins with defining an Analytical Target Profile (ATP) and proceeds through identification of critical analytical attributes and performance characteristics, risk assessment, identification of critical method parameters, application of Design of Experiments (DoE), establishment of a Method Operable Design Region (MODR), and development of an analytical control strategy, and suggesting that integration of AQbD with multivariate analysis, chemometrics, automation, artificial intelligence, machine learning, green analytical chemistry, and real-time analytical techn
This review discusses the fundamental concepts, workflow, statistical tools, applications, regulatory significance, advantages, challenges, and future perspectives of Analytical Quality by Design (AQbD), noting that AQbD generally begins with defining an Analytical Target Profile (ATP) and proceeds through identification of critical analytical attributes and performance characteristics, risk assessment, identification of critical method parameters, application of Design of Experiments (DoE), establishment of a Method Operable Design Region (MODR), and development of an analytical control strategy, and suggesting that integration of AQbD with multivariate analysis, chemometrics, automation, artificial intelligence, machine learning, green analytical chemistry, and real-time analytical techn
British Journal of Radiology Oct 1, 2026
This review systematically examines the anatomy, physiology, and pathophysiological roles of epicardial adipose tissue (EAT) and pericardial adipose tissue (PAT), compares CT, MRI, and echocardiography for quantifying these depots, notes that deep-learning segmentation (with Dice coefficients reported from 0.82 to 0.98) improves accuracy and reproducibility, and summarizes conflicting imaging evidence on EAT's link to cardiovascular outcomes alongside findings that statins, GLP-1 receptor agonists, SGLT2 inhibitors, and lifestyle interventions can reduce EAT volume.
This review systematically examines the anatomy, physiology, and pathophysiological roles of epicardial adipose tissue (EAT) and pericardial adipose tissue (PAT), compares CT, MRI, and echocardiography for quantifying these depots, notes that deep-learning segmentation (with Dice coefficients reported from 0.82 to 0.98) improves accuracy and reproducibility, and summarizes conflicting imaging evidence on EAT's link to cardiovascular outcomes alongside findings that statins, GLP-1 receptor agonists, SGLT2 inhibitors, and lifestyle interventions can reduce EAT volume.
Epicardial and pericardial fat are distinguished as two functionally distinct cardiac depots, with deep-learning segmentation and standardization advancing their imaging quantification toward cardiovascular risk stratification
This review systematically examines the anatomy, physiology, and pathophysiological roles of epicardial adipose tissue (EAT) and pericardial adipose tissue (PAT), compares CT, MRI, and echocardiography for quantifying these depots, notes that deep-learning segmentation (with Dice coefficients reported from 0.82 to 0.98) improves accuracy and reproducibility, and summarizes conflicting imaging evidence on EAT's link to cardiovascular outcomes alongside findings that statins, GLP-1 receptor agonists, SGLT2 inhibitors, and lifestyle interventions can reduce EAT volume.
This review systematically examines the anatomy, physiology, and pathophysiological roles of epicardial adipose tissue (EAT) and pericardial adipose tissue (PAT), compares CT, MRI, and echocardiography for quantifying these depots, notes that deep-learning segmentation (with Dice coefficients reported from 0.82 to 0.98) improves accuracy and reproducibility, and summarizes conflicting imaging evidence on EAT's link to cardiovascular outcomes alongside findings that statins, GLP-1 receptor agonists, SGLT2 inhibitors, and lifestyle interventions can reduce EAT volume.
Investigative Radiology Oct 1, 2026
In a two-center study of 378 patients with focal hepatic lesions (190 benign, 227 lesions; 188 malignant, 2681 lesions), radiomic features were extracted from nine PCD-CT spectral datasets (40, 50, 70, 90, 110, 140, 180 keV, virtual non-contrast [VNC], and iodine density maps [IDM]) after nnU-Net segmentation confirmed by an abdominal radiologist, multiple machine-learning models were trained with lesion-level predictions aggregated per patient, and the Random Forest model trained on VNC achieved the highest performance (AUC 0.899; 95% CI 0.840-0.951; accuracy 83.5%), while cross-domain analysis showed similar mean AUCs across training reconstructions (0.75 to 0.83) and test reconstruction choice mattered: 110, 140, 180 keV and VNC were most stable (median AUC >= 0.
In a two-center study of 378 patients with focal hepatic lesions (190 benign, 227 lesions; 188 malignant, 2681 lesions), radiomic features were extracted from nine PCD-CT spectral datasets (40, 50, 70, 90, 110, 140, 180 keV, virtual non-contrast [VNC], and iodine density maps [IDM]) after nnU-Net segmentation confirmed by an abdominal radiologist, multiple machine-learning models were trained with lesion-level predictions aggregated per patient, and the Random Forest model trained on VNC achieved the highest performance (AUC 0.899; 95% CI 0.840-0.951; accuracy 83.5%), while cross-domain analysis showed similar mean AUCs across training reconstructions (0.75 to 0.83) and test reconstruction choice mattered: 110, 140, 180 keV and VNC were most stable (median AUC >= 0.
Two-center PCD-CT spectral radiomics in 378 patients: VNC-trained model separates benign from malignant liver lesions at AUC 0.899, with high-keV and VNC test reconstructions most stable
In a two-center study of 378 patients with focal hepatic lesions (190 benign, 227 lesions; 188 malignant, 2681 lesions), radiomic features were extracted from nine PCD-CT spectral datasets (40, 50, 70, 90, 110, 140, 180 keV, virtual non-contrast [VNC], and iodine density maps [IDM]) after nnU-Net segmentation confirmed by an abdominal radiologist, multiple machine-learning models were trained with lesion-level predictions aggregated per patient, and the Random Forest model trained on VNC achieved the highest performance (AUC 0.899; 95% CI 0.840-0.951; accuracy 83.5%), while cross-domain analysis showed similar mean AUCs across training reconstructions (0.75 to 0.83) and test reconstruction choice mattered: 110, 140, 180 keV and VNC were most stable (median AUC >= 0.
In a two-center study of 378 patients with focal hepatic lesions (190 benign, 227 lesions; 188 malignant, 2681 lesions), radiomic features were extracted from nine PCD-CT spectral datasets (40, 50, 70, 90, 110, 140, 180 keV, virtual non-contrast [VNC], and iodine density maps [IDM]) after nnU-Net segmentation confirmed by an abdominal radiologist, multiple machine-learning models were trained with lesion-level predictions aggregated per patient, and the Random Forest model trained on VNC achieved the highest performance (AUC 0.899; 95% CI 0.840-0.951; accuracy 83.5%), while cross-domain analysis showed similar mean AUCs across training reconstructions (0.75 to 0.83) and test reconstruction choice mattered: 110, 140, 180 keV and VNC were most stable (median AUC >= 0.
British Journal of Radiology Oct 1, 2026
This review by a University of Oxford group synthesises the anatomical and physiological basis of pericoronary adipose tissue (PCAT) as a biosensor of coronary inflammation, explains how the standardised Fat Attenuation Index (FAI) Score corrects for technical, anatomical and biological variability and predicts MACE, reviews the role of artificial intelligence in automated segmentation and multi-parametric risk modelling, and compiles evidence that FAI falls after statins, anti-oxLDL antibodies, anti-TNF biologics, radiotherapy and cardiometabolic agents, concluding that PCAT imaging may complement traditional risk factors and plaque metrics while the evidence remains evolving.
This review by a University of Oxford group synthesises the anatomical and physiological basis of pericoronary adipose tissue (PCAT) as a biosensor of coronary inflammation, explains how the standardised Fat Attenuation Index (FAI) Score corrects for technical, anatomical and biological variability and predicts MACE, reviews the role of artificial intelligence in automated segmentation and multi-parametric risk modelling, and compiles evidence that FAI falls after statins, anti-oxLDL antibodies, anti-TNF biologics, radiotherapy and cardiometabolic agents, concluding that PCAT imaging may complement traditional risk factors and plaque metrics while the evidence remains evolving.
Oxford team review: pericoronary fat imaging turns coronary inflammation into a quantifiable metric, with FAI Score falling after lipid-lowering, biologics and radiotherapy
This review by a University of Oxford group synthesises the anatomical and physiological basis of pericoronary adipose tissue (PCAT) as a biosensor of coronary inflammation, explains how the standardised Fat Attenuation Index (FAI) Score corrects for technical, anatomical and biological variability and predicts MACE, reviews the role of artificial intelligence in automated segmentation and multi-parametric risk modelling, and compiles evidence that FAI falls after statins, anti-oxLDL antibodies, anti-TNF biologics, radiotherapy and cardiometabolic agents, concluding that PCAT imaging may complement traditional risk factors and plaque metrics while the evidence remains evolving.
This review by a University of Oxford group synthesises the anatomical and physiological basis of pericoronary adipose tissue (PCAT) as a biosensor of coronary inflammation, explains how the standardised Fat Attenuation Index (FAI) Score corrects for technical, anatomical and biological variability and predicts MACE, reviews the role of artificial intelligence in automated segmentation and multi-parametric risk modelling, and compiles evidence that FAI falls after statins, anti-oxLDL antibodies, anti-TNF biologics, radiotherapy and cardiometabolic agents, concluding that PCAT imaging may complement traditional risk factors and plaque metrics while the evidence remains evolving.
American Journal of Respiratory Cell and Molecular Biology Oct 1, 2026
In 22 C57BL/6 mice (11 given triple oropharyngeal bleomycin at 0.25 mg/kg to induce pulmonary fibrosis and 11 given saline), respiratory-gated µCT was acquired at baseline and days 7, 14, and 21, a U-Net deep-learning algorithm segmented the lungs into left and right lobes that were further divided into apical and caudal regions using airway landmarks for four subregions, and regional volumes, aeration compartments, and ventilation maps were extracted; saline-treated mice showed stable metrics with minimal interanimal variability, whereas bleomycin-treated animals showed early and heterogeneous fibrotic changes with the apical regions most affected and the caudal-right region displaying a compensatory functional increase, so that µCT-based regional analysis detected localized dysfunction a
In 22 C57BL/6 mice (11 given triple oropharyngeal bleomycin at 0.25 mg/kg to induce pulmonary fibrosis and 11 given saline), respiratory-gated µCT was acquired at baseline and days 7, 14, and 21, a U-Net deep-learning algorithm segmented the lungs into left and right lobes that were further divided into apical and caudal regions using airway landmarks for four subregions, and regional volumes, aeration compartments, and ventilation maps were extracted; saline-treated mice showed stable metrics with minimal interanimal variability, whereas bleomycin-treated animals showed early and heterogeneous fibrotic changes with the apical regions most affected and the caudal-right region displaying a compensatory functional increase, so that µCT-based regional analysis detected localized dysfunction a
Four-region µCT analysis detects compensatory ventilation in the right caudal lobe of a mouse fibrosis model that whole-lung readouts miss
In 22 C57BL/6 mice (11 given triple oropharyngeal bleomycin at 0.25 mg/kg to induce pulmonary fibrosis and 11 given saline), respiratory-gated µCT was acquired at baseline and days 7, 14, and 21, a U-Net deep-learning algorithm segmented the lungs into left and right lobes that were further divided into apical and caudal regions using airway landmarks for four subregions, and regional volumes, aeration compartments, and ventilation maps were extracted; saline-treated mice showed stable metrics with minimal interanimal variability, whereas bleomycin-treated animals showed early and heterogeneous fibrotic changes with the apical regions most affected and the caudal-right region displaying a compensatory functional increase, so that µCT-based regional analysis detected localized dysfunction a
In 22 C57BL/6 mice (11 given triple oropharyngeal bleomycin at 0.25 mg/kg to induce pulmonary fibrosis and 11 given saline), respiratory-gated µCT was acquired at baseline and days 7, 14, and 21, a U-Net deep-learning algorithm segmented the lungs into left and right lobes that were further divided into apical and caudal regions using airway landmarks for four subregions, and regional volumes, aeration compartments, and ventilation maps were extracted; saline-treated mice showed stable metrics with minimal interanimal variability, whereas bleomycin-treated animals showed early and heterogeneous fibrotic changes with the apical regions most affected and the caudal-right region displaying a compensatory functional increase, so that µCT-based regional analysis detected localized dysfunction a
American Journal of Respiratory Cell and Molecular Biology Oct 1, 2026
Combining human cord blood monocytes with a neonatal mouse influenza model, this study found that neonatal monocytes sustain higher TLR2 expression after LTA or IAV stimulation, while TLR2 gene deletion, anti-TLR2 antibody blockade, or myeloid-specific TLR2 deletion improved survival of IAV-infected neonatal mice (for example, 61% versus 25% survival for TLR2−/− versus wild-type neonates, and 46% versus 7% with anti-TLR2 antibody), accompanied by reduced neutrophils at 6 days post-infection, lower histopathology scores in myeloid conditional knockouts, and no further rise in IL-6, TNF-α, MCP-1, CXCL1, and CXCL2 from 3 to 6 days post-infection, suggesting that myeloid TLR2 signaling exacerbates neonatal susceptibility to respiratory viral infection by amplifying pulmonary inflammation.
Combining human cord blood monocytes with a neonatal mouse influenza model, this study found that neonatal monocytes sustain higher TLR2 expression after LTA or IAV stimulation, while TLR2 gene deletion, anti-TLR2 antibody blockade, or myeloid-specific TLR2 deletion improved survival of IAV-infected neonatal mice (for example, 61% versus 25% survival for TLR2−/− versus wild-type neonates, and 46% versus 7% with anti-TLR2 antibody), accompanied by reduced neutrophils at 6 days post-infection, lower histopathology scores in myeloid conditional knockouts, and no further rise in IL-6, TNF-α, MCP-1, CXCL1, and CXCL2 from 3 to 6 days post-infection, suggesting that myeloid TLR2 signaling exacerbates neonatal susceptibility to respiratory viral infection by amplifying pulmonary inflammation.
Loss or blockade of myeloid TLR2 improves survival and reduces neutrophils and lung inflammation in influenza-infected neonatal mice
Combining human cord blood monocytes with a neonatal mouse influenza model, this study found that neonatal monocytes sustain higher TLR2 expression after LTA or IAV stimulation, while TLR2 gene deletion, anti-TLR2 antibody blockade, or myeloid-specific TLR2 deletion improved survival of IAV-infected neonatal mice (for example, 61% versus 25% survival for TLR2−/− versus wild-type neonates, and 46% versus 7% with anti-TLR2 antibody), accompanied by reduced neutrophils at 6 days post-infection, lower histopathology scores in myeloid conditional knockouts, and no further rise in IL-6, TNF-α, MCP-1, CXCL1, and CXCL2 from 3 to 6 days post-infection, suggesting that myeloid TLR2 signaling exacerbates neonatal susceptibility to respiratory viral infection by amplifying pulmonary inflammation.
Combining human cord blood monocytes with a neonatal mouse influenza model, this study found that neonatal monocytes sustain higher TLR2 expression after LTA or IAV stimulation, while TLR2 gene deletion, anti-TLR2 antibody blockade, or myeloid-specific TLR2 deletion improved survival of IAV-infected neonatal mice (for example, 61% versus 25% survival for TLR2−/− versus wild-type neonates, and 46% versus 7% with anti-TLR2 antibody), accompanied by reduced neutrophils at 6 days post-infection, lower histopathology scores in myeloid conditional knockouts, and no further rise in IL-6, TNF-α, MCP-1, CXCL1, and CXCL2 from 3 to 6 days post-infection, suggesting that myeloid TLR2 signaling exacerbates neonatal susceptibility to respiratory viral infection by amplifying pulmonary inflammation.
British Journal of Radiology Oct 1, 2026
This review synthesizes evidence on opportunistically identifying and quantifying coronary artery calcium (CAC) on thoracic CT performed for other clinical indications (routine thoracic CT, lung cancer screening CT, attenuation correction CT), reporting a pooled CAC prevalence of 52%, independent associations with all-cause mortality (pooled relative risk 2.13) and major adverse cardiovascular events (pooled relative risk 2.61), and reviewing novel applications including AI/machine learning automated scoring, radiomics, photon-counting CT, and CAC as a multi-system biomarker, while noting that no randomized trials assessing the clinical impact of this approach have yet been published.
This review synthesizes evidence on opportunistically identifying and quantifying coronary artery calcium (CAC) on thoracic CT performed for other clinical indications (routine thoracic CT, lung cancer screening CT, attenuation correction CT), reporting a pooled CAC prevalence of 52%, independent associations with all-cause mortality (pooled relative risk 2.13) and major adverse cardiovascular events (pooled relative risk 2.61), and reviewing novel applications including AI/machine learning automated scoring, radiomics, photon-counting CT, and CAC as a multi-system biomarker, while noting that no randomized trials assessing the clinical impact of this approach have yet been published.
Coronary calcium is detectable opportunistically on thoracic CT: pooled analyses show 52% prevalence, and AI models can automate scoring and predict cardiovascular death risk
This review synthesizes evidence on opportunistically identifying and quantifying coronary artery calcium (CAC) on thoracic CT performed for other clinical indications (routine thoracic CT, lung cancer screening CT, attenuation correction CT), reporting a pooled CAC prevalence of 52%, independent associations with all-cause mortality (pooled relative risk 2.13) and major adverse cardiovascular events (pooled relative risk 2.61), and reviewing novel applications including AI/machine learning automated scoring, radiomics, photon-counting CT, and CAC as a multi-system biomarker, while noting that no randomized trials assessing the clinical impact of this approach have yet been published.
This review synthesizes evidence on opportunistically identifying and quantifying coronary artery calcium (CAC) on thoracic CT performed for other clinical indications (routine thoracic CT, lung cancer screening CT, attenuation correction CT), reporting a pooled CAC prevalence of 52%, independent associations with all-cause mortality (pooled relative risk 2.13) and major adverse cardiovascular events (pooled relative risk 2.61), and reviewing novel applications including AI/machine learning automated scoring, radiomics, photon-counting CT, and CAC as a multi-system biomarker, while noting that no randomized trials assessing the clinical impact of this approach have yet been published.
European Journal Pharmaceutical and Medical Research Oct 1, 2026
This review examines artificial intelligence in personalized medicine across biomarker discovery, disease subtyping, risk prediction, early diagnosis, treatment-response modelling, pharmacogenomics, digital pathology, longitudinal monitoring, and clinical decision support, and discusses the data types enabling personalization, the conceptual foundations, unresolved ethical and legal issues, and the obstacles between research potential and clinical practice, while emphasizing that AI does not take over the doctor's decision-making but helps doctors access more information than they could retain within their minds all at once.
This review examines artificial intelligence in personalized medicine across biomarker discovery, disease subtyping, risk prediction, early diagnosis, treatment-response modelling, pharmacogenomics, digital pathology, longitudinal monitoring, and clinical decision support, and discusses the data types enabling personalization, the conceptual foundations, unresolved ethical and legal issues, and the obstacles between research potential and clinical practice, while emphasizing that AI does not take over the doctor's decision-making but helps doctors access more information than they could retain within their minds all at once.
This review maps AI's role in personalized medicine, from biomarker discovery to clinical decision support, while noting unresolved ethical, legal, and translation barriers
This review examines artificial intelligence in personalized medicine across biomarker discovery, disease subtyping, risk prediction, early diagnosis, treatment-response modelling, pharmacogenomics, digital pathology, longitudinal monitoring, and clinical decision support, and discusses the data types enabling personalization, the conceptual foundations, unresolved ethical and legal issues, and the obstacles between research potential and clinical practice, while emphasizing that AI does not take over the doctor's decision-making but helps doctors access more information than they could retain within their minds all at once.
This review examines artificial intelligence in personalized medicine across biomarker discovery, disease subtyping, risk prediction, early diagnosis, treatment-response modelling, pharmacogenomics, digital pathology, longitudinal monitoring, and clinical decision support, and discusses the data types enabling personalization, the conceptual foundations, unresolved ethical and legal issues, and the obstacles between research potential and clinical practice, while emphasizing that AI does not take over the doctor's decision-making but helps doctors access more information than they could retain within their minds all at once.

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