彰化秀傳醫院與知名 AI 醫療軟體新創公司 Deep01 合作，透過 AI 的腦出血自動判讀，即時通知醫師，並顯示於醫院的資訊系統，加速醫療決策的流程，有效減少腦出血病人在急診的停留時間。昨日（10/21）舉辦成果發表會，使用 AI 後，腦出血病人平均提早了 25 分鐘離開急診，進入下一步處置和治療，成為 AI 醫療產品在臨床應用的成功案例。
Artificial Intelligence Accurately Predicts if COVID-19 Patients
Trained to see patterns by analyzing thousands of chest X-rays, a computer program predicted with up to 80 percent accuracy which COVID-19 patients would develop life-threatening complications within four days, a new study finds.
十年後，台灣醫院距離 AI 會更近一步嗎？兩位前線醫師：流程線上化是第一步！：流程線上化是第2步！
在2020幾乎被疫情籠罩之下，數位醫療的概念無疑是整個國際的趨勢，對此，我國 經濟部近日也首度將「數位醫療」的項目， 加入了租稅範圍，修訂「生技醫藥產業發展條例」。但除了公部門的推動、科技業的發展、及學者的研究外，要 真正暸解「數位醫療」，或許還少一塊最關鍵的觀點——究竟醫生們自己是怎麼看待與科技碰撞的火花？身處其中的醫療體系人員們，對於「數位轉型」的實際心境又是什麼？
Applying Problem Transformation Methods for Predicting 3-Days
For an emergency department (ED), patient readmissions after ED discharges in a short period are usually considered as an indicator to evaluate the quality of medical services, representing whether the patients had received appropriate treatments.
Artificial intelligence to predict needs for urgent revascularization from
Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room
Artificial intelligence and machine learning in emergency medicine
Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power.
Applications of Machine Learning Approaches in Emergency Medicine; a Review Article
Using artificial intelligence and machine learning techniques in different medical fields, especially emergency medicine is rapidly growing. In this paper, studies conducted in the recent years on using artificial intelligence in emergency medicine have been collected and assessed.
Applications of artificial intelligence in the emergency department
Over the past 5 years, there has been an increasing number of applications of artificial intelligence and machine learning (AI/ML) throughout various sectors of the economy. This wide-reaching impact has fueled global
Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis:
Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems.
An artificial intelligence system for predicting the deterioration of COVID-19
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables.
A System for Predicting Hospital Admission at Emergency Department Based on Electronic Health Record
Emergency Department (ED) crowding has become an issue of delayed patient treatment and even a public healthcare problem around the world. According to recent research studies of many countries, the increasing number of patients in the emergency department which has led to unprecedented crowding and delays in care.
Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology
The COVID-19 pandemic has resulted in massive disruptions within health care, both directly as a result of the infectious disease outbreak, and indirectly because of public health measures to mitigate against transmission. This disruption has caused rapid dynamic fluctuations in demand, capacity, and even contextual aspects of health care.