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Terence Tao blog RSS This guest opinion piece by Po-Shen Loh, published on Terence Tao's blog, proposes that the mathematics community and all industries should publicly adopt the axiom 'We (humans) should help humanity flourish,' and argues from it that further AI advance will create more high-skill human oversight jobs than there are people to fill them, eventually forcing AI progress to slow; it also discusses how pure mathematics contributes to human flourishing and what practical changes adopting the axiom might bring.
This guest opinion piece by Po-Shen Loh, published on Terence Tao's blog, proposes that the mathematics community and all industries should publicly adopt the axiom 'We (humans) should help humanity flourish,' and argues from it that further AI advance will create more high-skill human oversight jobs than there are people to fill them, eventually forcing AI progress to slow; it also discusses how pure mathematics contributes to human flourishing and what practical changes adopting the axiom might bring.
This guest opinion piece by Po-Shen Loh, published on Terence Tao's blog, proposes that the mathematics community and all industries should publicly adopt the axiom 'We (humans) should help humanity flourish,' and argues from it that further AI advance will create more high-skill human oversight jobs than there are people to fill them, eventually forcing AI progress to slow; it also discusses how pure mathematics contributes to human flourishing and what practical changes adopting the axiom might bring.
This guest opinion piece by Po-Shen Loh, published on Terence Tao's blog, proposes that the mathematics community and all industries should publicly adopt the axiom 'We (humans) should help humanity flourish,' and argues from it that further AI advance will create more high-skill human oversight jobs than there are people to fill them, eventually forcing AI progress to slow; it also discusses how pure mathematics contributes to human flourishing and what practical changes adopting the axiom might bring.
World Journal of Methodology This letter to the editor responds to a study by Zhou et al published in the World Journal of Gastroenterology on the concept of artificial wisdom (AW) applied to historical medical inquiry, itself a response to an AI-human analysis of Alexander the Great's cause of death; while acknowledging the innovative integration of generative AI with clinical reasoning, the letter highlights epistemic and ethical limitations, arguing that AI systems shaped by transient data and iterative obsolescence cannot access enduring or timeless truths, that the persuasive fluency of large language models risks creating an illusion of certainty and conflating probabilistic synthesis with wisdom, and that transparency, accountability, and human moral stewardship are essential safeguards, ultimately proposing a
This letter to the editor responds to a study by Zhou et al published in the World Journal of Gastroenterology on the concept of artificial wisdom (AW) applied to historical medical inquiry, itself a response to an AI-human analysis of Alexander the Great's cause of death; while acknowledging the innovative integration of generative AI with clinical reasoning, the letter highlights epistemic and ethical limitations, arguing that AI systems shaped by transient data and iterative obsolescence cannot access enduring or timeless truths, that the persuasive fluency of large language models risks creating an illusion of certainty and conflating probabilistic synthesis with wisdom, and that transparency, accountability, and human moral stewardship are essential safeguards, ultimately proposing a
This letter to the editor responds to a study by Zhou et al published in the World Journal of Gastroenterology on the concept of artificial wisdom (AW) applied to historical medical inquiry, itself a response to an AI-human analysis of Alexander the Great's cause of death; while acknowledging the innovative integration of generative AI with clinical reasoning, the letter highlights epistemic and ethical limitations, arguing that AI systems shaped by transient data and iterative obsolescence cannot access enduring or timeless truths, that the persuasive fluency of large language models risks creating an illusion of certainty and conflating probabilistic synthesis with wisdom, and that transparency, accountability, and human moral stewardship are essential safeguards, ultimately proposing a
This letter to the editor responds to a study by Zhou et al published in the World Journal of Gastroenterology on the concept of artificial wisdom (AW) applied to historical medical inquiry, itself a response to an AI-human analysis of Alexander the Great's cause of death; while acknowledging the innovative integration of generative AI with clinical reasoning, the letter highlights epistemic and ethical limitations, arguing that AI systems shaped by transient data and iterative obsolescence cannot access enduring or timeless truths, that the persuasive fluency of large language models risks creating an illusion of certainty and conflating probabilistic synthesis with wisdom, and that transparency, accountability, and human moral stewardship are essential safeguards, ultimately proposing a
World Journal of Methodology This paper assesses why high-performing artificial intelligence systems for ocular image processing seldom translate into improved patient outcomes, locating the central problem in the disparity between pixel-level performance metrics and their clinical significance, naming data bias, domain shift, and label noise alongside the lack of prospective randomized deployment trials as primary obstacles, and outlining a path through stringent external validation, established decision criteria, ongoing surveillance in real clinical practice, transparent reporting standards, and deliberate human-factors engineering, with the goal of converting algorithmic accuracy into meaningful diagnostic precision for glaucoma, diabetic retinopathy, and macular conditions (specifically diabetic macular edema and
This paper assesses why high-performing artificial intelligence systems for ocular image processing seldom translate into improved patient outcomes, locating the central problem in the disparity between pixel-level performance metrics and their clinical significance, naming data bias, domain shift, and label noise alongside the lack of prospective randomized deployment trials as primary obstacles, and outlining a path through stringent external validation, established decision criteria, ongoing surveillance in real clinical practice, transparent reporting standards, and deliberate human-factors engineering, with the goal of converting algorithmic accuracy into meaningful diagnostic precision for glaucoma, diabetic retinopathy, and macular conditions (specifically diabetic macular edema and
This paper assesses why high-performing artificial intelligence systems for ocular image processing seldom translate into improved patient outcomes, locating the central problem in the disparity between pixel-level performance metrics and their clinical significance, naming data bias, domain shift, and label noise alongside the lack of prospective randomized deployment trials as primary obstacles, and outlining a path through stringent external validation, established decision criteria, ongoing surveillance in real clinical practice, transparent reporting standards, and deliberate human-factors engineering, with the goal of converting algorithmic accuracy into meaningful diagnostic precision for glaucoma, diabetic retinopathy, and macular conditions (specifically diabetic macular edema and
This paper assesses why high-performing artificial intelligence systems for ocular image processing seldom translate into improved patient outcomes, locating the central problem in the disparity between pixel-level performance metrics and their clinical significance, naming data bias, domain shift, and label noise alongside the lack of prospective randomized deployment trials as primary obstacles, and outlining a path through stringent external validation, established decision criteria, ongoing surveillance in real clinical practice, transparent reporting standards, and deliberate human-factors engineering, with the goal of converting algorithmic accuracy into meaningful diagnostic precision for glaucoma, diabetic retinopathy, and macular conditions (specifically diabetic macular edema and
medRxiv This study built a proof-of-concept information extraction pipeline using three open-source LLMs (Athena-v3-AWQ, Gemma3-27B, and Llama-3.1-70B-Instruct) to extract 11 natural history study characteristics from PubMed abstracts labeled "2" in the CZI DRSM corpus (148 gold-standard and 3,547 full-corpus abstracts), finding that all three models exceeded 99% processing success, that Gemma was best overall on expert rating (68.0% of outputs rated "good") and full-corpus runtime (~16 minutes), that Llama scored higher on automated Token F1 (0.874 vs. 0.723), and that Athena performed worst largely because it copied source text verbatim rather than synthesizing it.
This study built a proof-of-concept information extraction pipeline using three open-source LLMs (Athena-v3-AWQ, Gemma3-27B, and Llama-3.1-70B-Instruct) to extract 11 natural history study characteristics from PubMed abstracts labeled "2" in the CZI DRSM corpus (148 gold-standard and 3,547 full-corpus abstracts), finding that all three models exceeded 99% processing success, that Gemma was best overall on expert rating (68.0% of outputs rated "good") and full-corpus runtime (~16 minutes), that Llama scored higher on automated Token F1 (0.874 vs. 0.723), and that Athena performed worst largely because it copied source text verbatim rather than synthesizing it.
This study built a proof-of-concept information extraction pipeline using three open-source LLMs (Athena-v3-AWQ, Gemma3-27B, and Llama-3.1-70B-Instruct) to extract 11 natural history study characteristics from PubMed abstracts labeled "2" in the CZI DRSM corpus (148 gold-standard and 3,547 full-corpus abstracts), finding that all three models exceeded 99% processing success, that Gemma was best overall on expert rating (68.0% of outputs rated "good") and full-corpus runtime (~16 minutes), that Llama scored higher on automated Token F1 (0.874 vs. 0.723), and that Athena performed worst largely because it copied source text verbatim rather than synthesizing it.
This study built a proof-of-concept information extraction pipeline using three open-source LLMs (Athena-v3-AWQ, Gemma3-27B, and Llama-3.1-70B-Instruct) to extract 11 natural history study characteristics from PubMed abstracts labeled "2" in the CZI DRSM corpus (148 gold-standard and 3,547 full-corpus abstracts), finding that all three models exceeded 99% processing success, that Gemma was best overall on expert rating (68.0% of outputs rated "good") and full-corpus runtime (~16 minutes), that Llama scored higher on automated Token F1 (0.874 vs. 0.723), and that Athena performed worst largely because it copied source text verbatim rather than synthesizing it.
World Journal of Methodology This review indicates that bone mineral density from dual-energy X-ray absorptiometry (DEXA) explains only part of fracture risk, while quantitative MRI techniques (T1ρ, T2 mapping, proton density fat fraction, diffusion-weighted imaging) and Vertebral Bone Quality (VBQ) scoring capture bone quality through collagen integrity, proteoglycan content, water distribution, and marrow adiposity; VBQ predicts vertebral fragility fractures independently of BMD with sensitivity exceeding 90% and discriminatory ability comparable to the fracture risk assessment tool and trabecular bone score, and integration with artificial intelligence can support opportunistic, radiation-free screening.
This review indicates that bone mineral density from dual-energy X-ray absorptiometry (DEXA) explains only part of fracture risk, while quantitative MRI techniques (T1ρ, T2 mapping, proton density fat fraction, diffusion-weighted imaging) and Vertebral Bone Quality (VBQ) scoring capture bone quality through collagen integrity, proteoglycan content, water distribution, and marrow adiposity; VBQ predicts vertebral fragility fractures independently of BMD with sensitivity exceeding 90% and discriminatory ability comparable to the fracture risk assessment tool and trabecular bone score, and integration with artificial intelligence can support opportunistic, radiation-free screening.
This review indicates that bone mineral density from dual-energy X-ray absorptiometry (DEXA) explains only part of fracture risk, while quantitative MRI techniques (T1ρ, T2 mapping, proton density fat fraction, diffusion-weighted imaging) and Vertebral Bone Quality (VBQ) scoring capture bone quality through collagen integrity, proteoglycan content, water distribution, and marrow adiposity; VBQ predicts vertebral fragility fractures independently of BMD with sensitivity exceeding 90% and discriminatory ability comparable to the fracture risk assessment tool and trabecular bone score, and integration with artificial intelligence can support opportunistic, radiation-free screening.
This review indicates that bone mineral density from dual-energy X-ray absorptiometry (DEXA) explains only part of fracture risk, while quantitative MRI techniques (T1ρ, T2 mapping, proton density fat fraction, diffusion-weighted imaging) and Vertebral Bone Quality (VBQ) scoring capture bone quality through collagen integrity, proteoglycan content, water distribution, and marrow adiposity; VBQ predicts vertebral fragility fractures independently of BMD with sensitivity exceeding 90% and discriminatory ability comparable to the fracture risk assessment tool and trabecular bone score, and integration with artificial intelligence can support opportunistic, radiation-free screening.
medRxiv This work builds MyoSTAT.AI, a deterministic and fully reproducible benchmarking framework for cardiac ultrasound segmentation and shear-wave elastography (SWE) velocity-field stabilization evaluated entirely on synthetic data, comparing four U-Net-based architectures (2D, 2.5D, 3D, ConvLSTM) through single-variable ablation and six stabilization methods, and reports that the ConvLSTM variant achieved the highest segmentation accuracy (Dice 0.994, IoU 0.987, an 18.6 percentage-point gain over the 2D baseline Dice 0.808), that UNet2.5D with a 3-frame temporal window reached Dice 0.983 at lower latency (433 ms vs. 1193 ms), that TensorRT FP16 deployment sustained 341 FPS on an RTX 3060 and 90.
This work builds MyoSTAT.AI, a deterministic and fully reproducible benchmarking framework for cardiac ultrasound segmentation and shear-wave elastography (SWE) velocity-field stabilization evaluated entirely on synthetic data, comparing four U-Net-based architectures (2D, 2.5D, 3D, ConvLSTM) through single-variable ablation and six stabilization methods, and reports that the ConvLSTM variant achieved the highest segmentation accuracy (Dice 0.994, IoU 0.987, an 18.6 percentage-point gain over the 2D baseline Dice 0.808), that UNet2.5D with a 3-frame temporal window reached Dice 0.983 at lower latency (433 ms vs. 1193 ms), that TensorRT FP16 deployment sustained 341 FPS on an RTX 3060 and 90.
This work builds MyoSTAT.AI, a deterministic and fully reproducible benchmarking framework for cardiac ultrasound segmentation and shear-wave elastography (SWE) velocity-field stabilization evaluated entirely on synthetic data, comparing four U-Net-based architectures (2D, 2.5D, 3D, ConvLSTM) through single-variable ablation and six stabilization methods, and reports that the ConvLSTM variant achieved the highest segmentation accuracy (Dice 0.994, IoU 0.987, an 18.6 percentage-point gain over the 2D baseline Dice 0.808), that UNet2.5D with a 3-frame temporal window reached Dice 0.983 at lower latency (433 ms vs. 1193 ms), that TensorRT FP16 deployment sustained 341 FPS on an RTX 3060 and 90.
This work builds MyoSTAT.AI, a deterministic and fully reproducible benchmarking framework for cardiac ultrasound segmentation and shear-wave elastography (SWE) velocity-field stabilization evaluated entirely on synthetic data, comparing four U-Net-based architectures (2D, 2.5D, 3D, ConvLSTM) through single-variable ablation and six stabilization methods, and reports that the ConvLSTM variant achieved the highest segmentation accuracy (Dice 0.994, IoU 0.987, an 18.6 percentage-point gain over the 2D baseline Dice 0.808), that UNet2.5D with a 3-frame temporal window reached Dice 0.983 at lower latency (433 ms vs. 1193 ms), that TensorRT FP16 deployment sustained 341 FPS on an RTX 3060 and 90.
World Journal of Methodology This review outlines current applications of artificial intelligence across the perioperative cancer pathway in onco-anaesthesia: preoperatively, machine learning and deep learning models enhance risk stratification through automated frailty assessment, electronic health record phenotyping, and prediction of cancer-specific outcomes; intraoperatively, AI-enabled technologies such as closed-loop anaesthesia delivery systems, predictive haemodynamic monitoring, and automated depth-of-anaesthesia control optimize drug dosing, reduce physiological stress, and may help preserve perioperative immune function; postoperatively, AI-driven integration of multimodal data including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms facilitates early detection of comp
This review outlines current applications of artificial intelligence across the perioperative cancer pathway in onco-anaesthesia: preoperatively, machine learning and deep learning models enhance risk stratification through automated frailty assessment, electronic health record phenotyping, and prediction of cancer-specific outcomes; intraoperatively, AI-enabled technologies such as closed-loop anaesthesia delivery systems, predictive haemodynamic monitoring, and automated depth-of-anaesthesia control optimize drug dosing, reduce physiological stress, and may help preserve perioperative immune function; postoperatively, AI-driven integration of multimodal data including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms facilitates early detection of comp
This review outlines current applications of artificial intelligence across the perioperative cancer pathway in onco-anaesthesia: preoperatively, machine learning and deep learning models enhance risk stratification through automated frailty assessment, electronic health record phenotyping, and prediction of cancer-specific outcomes; intraoperatively, AI-enabled technologies such as closed-loop anaesthesia delivery systems, predictive haemodynamic monitoring, and automated depth-of-anaesthesia control optimize drug dosing, reduce physiological stress, and may help preserve perioperative immune function; postoperatively, AI-driven integration of multimodal data including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms facilitates early detection of comp
This review outlines current applications of artificial intelligence across the perioperative cancer pathway in onco-anaesthesia: preoperatively, machine learning and deep learning models enhance risk stratification through automated frailty assessment, electronic health record phenotyping, and prediction of cancer-specific outcomes; intraoperatively, AI-enabled technologies such as closed-loop anaesthesia delivery systems, predictive haemodynamic monitoring, and automated depth-of-anaesthesia control optimize drug dosing, reduce physiological stress, and may help preserve perioperative immune function; postoperatively, AI-driven integration of multimodal data including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms facilitates early detection of comp
medRxiv Using 2,704 colorectal cancer discharge notes from MIMIC-IV and a 46-symptom inventory derived from the MSAS and EORTC QLQ-CR29, this study benchmarked dictionary-based rule matching, pretrained clinical NER, zero-shot Claude Haiku and Gemini 3.5 Flash, and two hybrid variants (LLM output plus post-hoc rule-based negation filtering) against a 200-note two-rater adjudicated gold standard, finding that Gemini 3.5 Flash performed best (Macro F1=0.70, Micro F1=0.86, Macro Precision=0.74), followed by Claude Haiku (Macro F1=0.63, Macro Recall=0.71), both substantially outperforming rule-based (Macro F1=0.44) and NER (Macro F1=0.38) methods, while post-hoc negation filtering paradoxically degraded LLM performance (Gemini+Hybrid Macro F1=0.58; Claude+Hybrid Macro F1=0.54).
Using 2,704 colorectal cancer discharge notes from MIMIC-IV and a 46-symptom inventory derived from the MSAS and EORTC QLQ-CR29, this study benchmarked dictionary-based rule matching, pretrained clinical NER, zero-shot Claude Haiku and Gemini 3.5 Flash, and two hybrid variants (LLM output plus post-hoc rule-based negation filtering) against a 200-note two-rater adjudicated gold standard, finding that Gemini 3.5 Flash performed best (Macro F1=0.70, Micro F1=0.86, Macro Precision=0.74), followed by Claude Haiku (Macro F1=0.63, Macro Recall=0.71), both substantially outperforming rule-based (Macro F1=0.44) and NER (Macro F1=0.38) methods, while post-hoc negation filtering paradoxically degraded LLM performance (Gemini+Hybrid Macro F1=0.58; Claude+Hybrid Macro F1=0.54).
Using 2,704 colorectal cancer discharge notes from MIMIC-IV and a 46-symptom inventory derived from the MSAS and EORTC QLQ-CR29, this study benchmarked dictionary-based rule matching, pretrained clinical NER, zero-shot Claude Haiku and Gemini 3.5 Flash, and two hybrid variants (LLM output plus post-hoc rule-based negation filtering) against a 200-note two-rater adjudicated gold standard, finding that Gemini 3.5 Flash performed best (Macro F1=0.70, Micro F1=0.86, Macro Precision=0.74), followed by Claude Haiku (Macro F1=0.63, Macro Recall=0.71), both substantially outperforming rule-based (Macro F1=0.44) and NER (Macro F1=0.38) methods, while post-hoc negation filtering paradoxically degraded LLM performance (Gemini+Hybrid Macro F1=0.58; Claude+Hybrid Macro F1=0.54).
Using 2,704 colorectal cancer discharge notes from MIMIC-IV and a 46-symptom inventory derived from the MSAS and EORTC QLQ-CR29, this study benchmarked dictionary-based rule matching, pretrained clinical NER, zero-shot Claude Haiku and Gemini 3.5 Flash, and two hybrid variants (LLM output plus post-hoc rule-based negation filtering) against a 200-note two-rater adjudicated gold standard, finding that Gemini 3.5 Flash performed best (Macro F1=0.70, Micro F1=0.86, Macro Precision=0.74), followed by Claude Haiku (Macro F1=0.63, Macro Recall=0.71), both substantially outperforming rule-based (Macro F1=0.44) and NER (Macro F1=0.38) methods, while post-hoc negation filtering paradoxically degraded LLM performance (Gemini+Hybrid Macro F1=0.58; Claude+Hybrid Macro F1=0.54).
World Journal of Methodology This narrative review summarizes contemporary strategies for managing constipation, encompassing lifestyle and dietary modifications, pharmacological therapies, behavioral interventions, and surgical options for refractory cases, noting that traditional laxatives remain the mainstay while newer agents such as prosecretory drugs, serotonergic agonists, and bile acid modulators have expanded therapeutic possibilities, that non-pharmacological approaches including biofeedback and neuromodulation provide effective alternatives in selected patients, and that substantial methodological variability in clinical trials across diagnostic criteria, endpoints, and follow-up durations limits generalizability, with future research focused on individualized treatment for refractory constipation, surgical
This narrative review summarizes contemporary strategies for managing constipation, encompassing lifestyle and dietary modifications, pharmacological therapies, behavioral interventions, and surgical options for refractory cases, noting that traditional laxatives remain the mainstay while newer agents such as prosecretory drugs, serotonergic agonists, and bile acid modulators have expanded therapeutic possibilities, that non-pharmacological approaches including biofeedback and neuromodulation provide effective alternatives in selected patients, and that substantial methodological variability in clinical trials across diagnostic criteria, endpoints, and follow-up durations limits generalizability, with future research focused on individualized treatment for refractory constipation, surgical
This narrative review summarizes contemporary strategies for managing constipation, encompassing lifestyle and dietary modifications, pharmacological therapies, behavioral interventions, and surgical options for refractory cases, noting that traditional laxatives remain the mainstay while newer agents such as prosecretory drugs, serotonergic agonists, and bile acid modulators have expanded therapeutic possibilities, that non-pharmacological approaches including biofeedback and neuromodulation provide effective alternatives in selected patients, and that substantial methodological variability in clinical trials across diagnostic criteria, endpoints, and follow-up durations limits generalizability, with future research focused on individualized treatment for refractory constipation, surgical
This narrative review summarizes contemporary strategies for managing constipation, encompassing lifestyle and dietary modifications, pharmacological therapies, behavioral interventions, and surgical options for refractory cases, noting that traditional laxatives remain the mainstay while newer agents such as prosecretory drugs, serotonergic agonists, and bile acid modulators have expanded therapeutic possibilities, that non-pharmacological approaches including biofeedback and neuromodulation provide effective alternatives in selected patients, and that substantial methodological variability in clinical trials across diagnostic criteria, endpoints, and follow-up durations limits generalizability, with future research focused on individualized treatment for refractory constipation, surgical
World Journal of Methodology This review systematically evaluates the methodological quality, clinical validity, and translational evidence of cutting-edge technologies in the precision management of gastrointestinal tumor-associated osteoporosis (GTO), summarizing their current applications in AI-assisted early screening and risk prediction, nano-enabled targeted bone protection, and multiomics-based exploration of the tumor-bone-gut axis, and discussing barriers to clinical translation such as limited AI generalizability, nanomedicine safety and manufacturing challenges, difficulties in multidimensional data integration and standardization, imperfect multidisciplinary collaboration, and ethical concerns, concluding that these technologies are expected to move GTO management from empirical practice toward precision m
This review systematically evaluates the methodological quality, clinical validity, and translational evidence of cutting-edge technologies in the precision management of gastrointestinal tumor-associated osteoporosis (GTO), summarizing their current applications in AI-assisted early screening and risk prediction, nano-enabled targeted bone protection, and multiomics-based exploration of the tumor-bone-gut axis, and discussing barriers to clinical translation such as limited AI generalizability, nanomedicine safety and manufacturing challenges, difficulties in multidimensional data integration and standardization, imperfect multidisciplinary collaboration, and ethical concerns, concluding that these technologies are expected to move GTO management from empirical practice toward precision m
This review systematically evaluates the methodological quality, clinical validity, and translational evidence of cutting-edge technologies in the precision management of gastrointestinal tumor-associated osteoporosis (GTO), summarizing their current applications in AI-assisted early screening and risk prediction, nano-enabled targeted bone protection, and multiomics-based exploration of the tumor-bone-gut axis, and discussing barriers to clinical translation such as limited AI generalizability, nanomedicine safety and manufacturing challenges, difficulties in multidimensional data integration and standardization, imperfect multidisciplinary collaboration, and ethical concerns, concluding that these technologies are expected to move GTO management from empirical practice toward precision m
This review systematically evaluates the methodological quality, clinical validity, and translational evidence of cutting-edge technologies in the precision management of gastrointestinal tumor-associated osteoporosis (GTO), summarizing their current applications in AI-assisted early screening and risk prediction, nano-enabled targeted bone protection, and multiomics-based exploration of the tumor-bone-gut axis, and discussing barriers to clinical translation such as limited AI generalizability, nanomedicine safety and manufacturing challenges, difficulties in multidimensional data integration and standardization, imperfect multidisciplinary collaboration, and ethical concerns, concluding that these technologies are expected to move GTO management from empirical practice toward precision m
Medinformatics This work generates new EGFR inhibitor candidates by fine-tuning a GPT-2 model on roughly 500,000 molecules from the ChEMBL database and benchmarking it against an LSTM network trained on the same dataset, evaluating generated compounds for validity, distinctiveness, and novelty, filtering them by Lipinski's rule of five, synthetic accessibility, and drug-likeness scores, and docking selected candidates against EGFR (PDB ID: 1M17) to assess binding affinity, finding that GPT-2 excels at producing structurally varied molecules while the LSTM generates a larger fraction of chemically valid compounds, with many candidates showing good binding interactions with EGFR.
This work generates new EGFR inhibitor candidates by fine-tuning a GPT-2 model on roughly 500,000 molecules from the ChEMBL database and benchmarking it against an LSTM network trained on the same dataset, evaluating generated compounds for validity, distinctiveness, and novelty, filtering them by Lipinski's rule of five, synthetic accessibility, and drug-likeness scores, and docking selected candidates against EGFR (PDB ID: 1M17) to assess binding affinity, finding that GPT-2 excels at producing structurally varied molecules while the LSTM generates a larger fraction of chemically valid compounds, with many candidates showing good binding interactions with EGFR.
This work generates new EGFR inhibitor candidates by fine-tuning a GPT-2 model on roughly 500,000 molecules from the ChEMBL database and benchmarking it against an LSTM network trained on the same dataset, evaluating generated compounds for validity, distinctiveness, and novelty, filtering them by Lipinski's rule of five, synthetic accessibility, and drug-likeness scores, and docking selected candidates against EGFR (PDB ID: 1M17) to assess binding affinity, finding that GPT-2 excels at producing structurally varied molecules while the LSTM generates a larger fraction of chemically valid compounds, with many candidates showing good binding interactions with EGFR.
This work generates new EGFR inhibitor candidates by fine-tuning a GPT-2 model on roughly 500,000 molecules from the ChEMBL database and benchmarking it against an LSTM network trained on the same dataset, evaluating generated compounds for validity, distinctiveness, and novelty, filtering them by Lipinski's rule of five, synthetic accessibility, and drug-likeness scores, and docking selected candidates against EGFR (PDB ID: 1M17) to assess binding affinity, finding that GPT-2 excels at producing structurally varied molecules while the LSTM generates a larger fraction of chemically valid compounds, with many candidates showing good binding interactions with EGFR.
World Journal of Methodology This study submitted 39 frequently asked patient questions about "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease) to ChatGPT-5, Gemini-2.5, and Claude-4, had responses independently rated by three gastroenterologists for accuracy, comprehensiveness, empathy, and actionability and by 20 patients for empathy, comprehensiveness, actionability, compassion, and usefulness, and analyzed readability indices, finding significant inter-model differences across multiple physician-rated domains, with Gemini-2.5 and Claude-4 scoring higher than ChatGPT-5 for accuracy, comprehensiveness, and actionability, Claude-4 showing the highest empathy scores, uniformly high patient-rated comprehensibility across all models, patient ratings of Gemini-2.
This study submitted 39 frequently asked patient questions about "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease) to ChatGPT-5, Gemini-2.5, and Claude-4, had responses independently rated by three gastroenterologists for accuracy, comprehensiveness, empathy, and actionability and by 20 patients for empathy, comprehensiveness, actionability, compassion, and usefulness, and analyzed readability indices, finding significant inter-model differences across multiple physician-rated domains, with Gemini-2.5 and Claude-4 scoring higher than ChatGPT-5 for accuracy, comprehensiveness, and actionability, Claude-4 showing the highest empathy scores, uniformly high patient-rated comprehensibility across all models, patient ratings of Gemini-2.
This study submitted 39 frequently asked patient questions about "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease) to ChatGPT-5, Gemini-2.5, and Claude-4, had responses independently rated by three gastroenterologists for accuracy, comprehensiveness, empathy, and actionability and by 20 patients for empathy, comprehensiveness, actionability, compassion, and usefulness, and analyzed readability indices, finding significant inter-model differences across multiple physician-rated domains, with Gemini-2.5 and Claude-4 scoring higher than ChatGPT-5 for accuracy, comprehensiveness, and actionability, Claude-4 showing the highest empathy scores, uniformly high patient-rated comprehensibility across all models, patient ratings of Gemini-2.
This study submitted 39 frequently asked patient questions about "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease) to ChatGPT-5, Gemini-2.5, and Claude-4, had responses independently rated by three gastroenterologists for accuracy, comprehensiveness, empathy, and actionability and by 20 patients for empathy, comprehensiveness, actionability, compassion, and usefulness, and analyzed readability indices, finding significant inter-model differences across multiple physician-rated domains, with Gemini-2.5 and Claude-4 scoring higher than ChatGPT-5 for accuracy, comprehensiveness, and actionability, Claude-4 showing the highest empathy scores, uniformly high patient-rated comprehensibility across all models, patient ratings of Gemini-2.
World Journal of Methodology This opinion review argues that the current academic promotion system in surgery disproportionately favors quantifiable metrics such as publications and grant funding over the demonstration of clinical skill, creating a fundamental "credentialing paradox"; that AI tools, by streamlining research tasks, amplify this publication-centric culture and widen the disconnect between a surgeon's academic rank and their proficiency in the operating room, placing less-funded faculty and those who dedicate time to clinical care and education at a disadvantage; and that the profession should redefine academic success through a more holistic framework that formally recognizes and rewards excellence in clinical care, education, and mentorship alongside research output.
This opinion review argues that the current academic promotion system in surgery disproportionately favors quantifiable metrics such as publications and grant funding over the demonstration of clinical skill, creating a fundamental "credentialing paradox"; that AI tools, by streamlining research tasks, amplify this publication-centric culture and widen the disconnect between a surgeon's academic rank and their proficiency in the operating room, placing less-funded faculty and those who dedicate time to clinical care and education at a disadvantage; and that the profession should redefine academic success through a more holistic framework that formally recognizes and rewards excellence in clinical care, education, and mentorship alongside research output.
This opinion review argues that the current academic promotion system in surgery disproportionately favors quantifiable metrics such as publications and grant funding over the demonstration of clinical skill, creating a fundamental "credentialing paradox"; that AI tools, by streamlining research tasks, amplify this publication-centric culture and widen the disconnect between a surgeon's academic rank and their proficiency in the operating room, placing less-funded faculty and those who dedicate time to clinical care and education at a disadvantage; and that the profession should redefine academic success through a more holistic framework that formally recognizes and rewards excellence in clinical care, education, and mentorship alongside research output.
This opinion review argues that the current academic promotion system in surgery disproportionately favors quantifiable metrics such as publications and grant funding over the demonstration of clinical skill, creating a fundamental "credentialing paradox"; that AI tools, by streamlining research tasks, amplify this publication-centric culture and widen the disconnect between a surgeon's academic rank and their proficiency in the operating room, placing less-funded faculty and those who dedicate time to clinical care and education at a disadvantage; and that the profession should redefine academic success through a more holistic framework that formally recognizes and rewards excellence in clinical care, education, and mentorship alongside research output.