For decades,the central dogma of oncology has been that a cancer’s identity is inextricably linked to its anatomical origin.This principle underpins the entire diagnostic and therapeutic framework,from histology-base...For decades,the central dogma of oncology has been that a cancer’s identity is inextricably linked to its anatomical origin.This principle underpins the entire diagnostic and therapeutic framework,from histology-based classification to site-specific treatment guidelines.Yet,this framework catastrophically fails for a substantial population of patients diagnosed with cancer of unknown primary(CUP).These patients present metastatic disease,yet their primary tumors remain elusive despite exhaustive clinical workup1.CUP,accounting for 1%-3%of all cancer diagnoses,is an enigma with devastating consequences;the median overall survival is only 2-12 months2-4.The inability to pinpoint an origin forces clinicians to rely on broad-spectrum empirical chemotherapy,such as taxane-carboplatin regimens,which have limited efficacy and exclude patients from the promise of targeted therapies and clinical trials5.CUP is not only a diagnostic challenge but also an indictment of the siloed approach to understanding malignancy:this cancer highlights the limitations of origin-based diagnostic frameworks.However,the confluence of high-dimensional biological data and advanced artificial intelligence(AI)is now poised to address this long-standing diagnostic limitation and to herald a new era for not only CUP but also oncology as a whole(Figure 1).展开更多
Large-Scale Language Models(LLMs)have initiated transformative changes in Traditional Chinese Medicine(TCM),yet existing LLM-based diagnostic approaches face challenges such as prolonged training cycles and high imple...Large-Scale Language Models(LLMs)have initiated transformative changes in Traditional Chinese Medicine(TCM),yet existing LLM-based diagnostic approaches face challenges such as prolonged training cycles and high implementation costs due to reliance on medical expertise.To address this,we propose a systematic framework integrating multimodal data and LLM technologies.First,we analyze bottlenecks in traditional diagnosis(e.g.,subjectivity)and modernization challenges.The framework leverages open-s ource foundation models(e.g.,Baichuan2,LLaMA)pre-trained on"symptom-syndrome-medication"associations,fine-tuned with clinical data to simulate diagnostic workflows.Key components include:(1)a Data Input Layer capturing tongue image features(via YOLOv5s6/U-Net),speech spectra,BERT-encoded inquiry texts,and pulse waveforms;(2)a Feature Fusion Layer constructing syndrome differentiation vectors through multimodal feature concatenation;and(3)a Prediction&Feedback Layer generating diagnostic probabilities with reinforcement learning based on clinical efficacy.Finally,we discuss critical issues,including risks of physician replacement,professional competence degradation,and liability attr ibution in diagnostic errors.This framework aims t o enhance TCM diagnostic efficiency while ensuring clinical reliability.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.32270688,31801117,and 82430107 to X.L.,and 32500589 to H.S.)the China Postdoctoral Science Foundation(Grant Nos.BX20240253 and 2024M762384 to H.S.)+1 种基金the Natural Science Foundation of Tianjin(Grant No.24JCQNJC01280 to H.S.)Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-3-003A).
文摘For decades,the central dogma of oncology has been that a cancer’s identity is inextricably linked to its anatomical origin.This principle underpins the entire diagnostic and therapeutic framework,from histology-based classification to site-specific treatment guidelines.Yet,this framework catastrophically fails for a substantial population of patients diagnosed with cancer of unknown primary(CUP).These patients present metastatic disease,yet their primary tumors remain elusive despite exhaustive clinical workup1.CUP,accounting for 1%-3%of all cancer diagnoses,is an enigma with devastating consequences;the median overall survival is only 2-12 months2-4.The inability to pinpoint an origin forces clinicians to rely on broad-spectrum empirical chemotherapy,such as taxane-carboplatin regimens,which have limited efficacy and exclude patients from the promise of targeted therapies and clinical trials5.CUP is not only a diagnostic challenge but also an indictment of the siloed approach to understanding malignancy:this cancer highlights the limitations of origin-based diagnostic frameworks.However,the confluence of high-dimensional biological data and advanced artificial intelligence(AI)is now poised to address this long-standing diagnostic limitation and to herald a new era for not only CUP but also oncology as a whole(Figure 1).
文摘Large-Scale Language Models(LLMs)have initiated transformative changes in Traditional Chinese Medicine(TCM),yet existing LLM-based diagnostic approaches face challenges such as prolonged training cycles and high implementation costs due to reliance on medical expertise.To address this,we propose a systematic framework integrating multimodal data and LLM technologies.First,we analyze bottlenecks in traditional diagnosis(e.g.,subjectivity)and modernization challenges.The framework leverages open-s ource foundation models(e.g.,Baichuan2,LLaMA)pre-trained on"symptom-syndrome-medication"associations,fine-tuned with clinical data to simulate diagnostic workflows.Key components include:(1)a Data Input Layer capturing tongue image features(via YOLOv5s6/U-Net),speech spectra,BERT-encoded inquiry texts,and pulse waveforms;(2)a Feature Fusion Layer constructing syndrome differentiation vectors through multimodal feature concatenation;and(3)a Prediction&Feedback Layer generating diagnostic probabilities with reinforcement learning based on clinical efficacy.Finally,we discuss critical issues,including risks of physician replacement,professional competence degradation,and liability attr ibution in diagnostic errors.This framework aims t o enhance TCM diagnostic efficiency while ensuring clinical reliability.
