台大人工智慧中心智慧急診網站

緊急時刻

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醫療應用案例

Hospital explores using AI to autonomously order imaging exams in the emergency department 2022-03-18
Toronto researchers are exploring the use of artificial intelligence to autonomously order imaging and other exams in the emergency department to help expedite patient triage.

急診室24小時智慧監控、模擬住院率調控病床…台北馬偕拚AI醫療超有感上工 2022-03-17
心率、血壓等留觀病人的生理數據，全都透過二十四小時動態監測在急診室「管理大看板」中一一顯示，一旦超過設定的危機值，圖示就會從綠色笑臉變成紅色苦瓜臉，並發出示警，藉此提醒醫護人員。

New AI algorithm can quickly detect x-rays that are positive for fractures 2022-02-16
Emergency room and urgent care clinics are typically busy and patients often have to wait many hours before they can be seen, evaluated and receive treatment.

醫護戴手套難打字高雄長庚啟用AI口述病歷輸入 2021-12-13
為解決醫護因穿著不便打字的防護衣、工作手套導致難以輸入病歷的問題，高雄長庚醫院與台灣人工智慧實驗室合作，開發AI零接觸口述輸入系統，已在急診室和篩檢站啟用。

東港安泰「愛因斯坦人工智慧醫療系統」　30秒找到腦出血救命 2021-10-05
DeepCT的AI輔助判讀系統能在執行電腦斷層掃描檢查時，同步完成顱內出血的判讀，標示可能出血位置，血量，並在看診電腦上發出警示，提醒醫師先察看CT片確認，可縮短病人停留在急診室的時間，平均每位病人可縮短5~8分鐘，夜間時段更能夠節省40~50分鐘，讓病人能獲得即時的醫療處置，降低失能及死亡率，並可加速急診流程，增加急診病床週轉率，提昇急診醫療照護品質與效率。

搶救心肌梗塞人工智慧急診上線 2021-09-06
醫院急診分秒必爭，隨著AI人工智慧不斷發展，透過AI協助可望解決急診壅塞，並縮短病患等待救治的時間；在台中中國醫藥大學附設醫院將AI運用到急診搶救心肌梗塞病患，台南奇美醫學中心發展「急診胸痛AI病情預測系統」，都大幅減低誤判的機率，給患者最及時有效的治療。

彰化秀傳醫院導入人工智慧系統，減少腦出血病人急診停留時間 2021-08-16
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 2021-05-19
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.

擊退急診醫師的大敵！AI讓敗血症死亡率降低，還能解決急診壅塞 2021-05-17
急診室如同戰場，每天湧入大量病患，除了考驗醫護人員快速應變能力，排除急診壅塞也是一大難題。在AI智能科技輔助下，不僅加速檢傷分類、醫療診斷、減少病人短時間再回診，甚至能對付醫生的大敵—敗血症，讓敗血症病人死亡率下降超過1成以上。

三總研發AI心電圖救護車上立即診斷爭取黃金時間 2021-05-17
急性心肌梗塞經常讓患者命懸一線，若及早判讀心電圖，可爭取黃金救援時間。三軍總醫院與台北市及新北市消防局合作，在救護車導入「心電圖AI判讀系統」，讓醫療團隊在患者到院之前，可以藉由心電圖AI輔助，即時判讀患者病況，同時也讓醫院端收到訊息，及早準備，盡快讓患者接受治療。

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學術相關文章

Development and validation of a practical machine-learning triage algorithm for 2022-02-16
Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS)

Artificial intelligence-assisted reduction in patients’ waiting time for outpatient 2022-02-16
Many studies suggest that patient satisfaction is significantly negatively correlated with the waiting time. A well-designed healthcare system should not keep patients waiting too long for an appointment and consultation.

Applying Problem Transformation Methods for Predicting 3-Days 2021-09-11
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 2021-07-07
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 2021-05-20
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 2021-05-20
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 2021-05-20
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: 2021-05-17
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 2021-05-07
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 2021-02-03
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.

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