Name: AI 正在改變醫療保健的 4 種方式！ (4 Ways Artificial Intelligence is Transforming Healthcare)
Uploaded: 2026-02-15T18:25:32.000Z
Duration: 9 min 41 s

Artificial intelligence, or AI, is taking the world by storm, and

人工智慧，也就是 AI，正在席捲全球，

進階被動語態

its impact will be felt in all corners of society, including medicine.

它的影響將遍及社會的各個角落，包括醫學領域。

Here are four ways AI is transforming the medical field.

以下是 AI 正在改變醫學領域的四種方式。

Jubbal med schoolinsiders.com the first way AI is changing medicine is by assisting with diagnosing and treating patients.

Jubbal med schoolinsiders.com AI 改變醫學的第一種方式，是協助診斷和治療病患。

Misdiagnosing a disease happens relatively infrequently, but

疾病的誤診發生的頻率相對不高，但

it can occur due to factors such as physician fatigue, errors in diagnostic modalities, or limited resources in underprivileged areas.

卻可能因為醫師疲勞、診斷工具的錯誤，或是資源匱乏地區的資源有限等因素而發生。

While the idea of AI taking over disease diagnos may sound alarming, we should view it as a supportive tool for physicians.

雖然 AI 取代疾病診斷聽起來可能有點嚇人，但我們應該將它視為醫師的輔助工具。

For example, the Convolutional Neural Network, or cnn, is a diagnostic modality that can analyze thousands of images from public datasets and patient medical records to identify patterns, enabling them to quickly and accurately diagnose diseases.

例如，卷積神經網路，也就是 CNN，是一種診斷工具，它可以分析來自公開資料集和病患病歷的數千張影像，找出模式，進而能夠快速準確地診斷疾病。

Researchers recently used CNNs to diagnose Kawasaki disease, or KD, an inflammatory disease of the blood vessels in children that can prove fatal if left untreated.

研究人員最近使用 CNN 來診斷川崎氏症，也就是 KD，這是一種兒童血管發炎的疾病，如果不及時治療，可能會致命。

A diagnostic hurdle with KD is that symptoms are usually vague and can overcome overlap with other childhood illnesses.

KD 的一個診斷難點是，它的症狀通常很模糊，而且容易和其他兒童疾病混淆。

To reduce the rate of misdiagnosis, researchers compiled images from KD patients from all over the world, building a CNN that can identify its common signs.

為了降低誤診率，研究人員收集了來自世界各地 KD 病患的影像，建立了一個 CNN 來辨識其常見的跡象。

The CNN proved both sensitive and specific for diagnosing Kawasaki disease, making diagnosis possible by merely taking a photo with a smartphone.

這個 CNN 在診斷川崎氏症時，被證明既敏感又具特異性，讓診斷變得非常簡單，只需要用智慧型手機拍張照片就能完成。

Of course, clinical judgment currently remains irreplaceable, and

當然，臨床判斷目前仍然是不可取代的，

physicians should still rely on a thorough history, physical exam, and the relevant labs or imaging.

醫師仍應依賴詳細的病史、身體檢查，以及相關的實驗室或影像檢查。

These CNNs can help guide physicians in the right direction, especially in resource locales, where it can be expensive for patients to do more thorough testing.

這些 CNN 有助於引導醫師朝正確的方向診斷，尤其是在資源匱乏的地區，那裡的病患進行更詳細的檢測可能費用昂貴。

Preliminary efforts have also demonstrated AI can aid clinicians in diagnosing colorectal cancer, lung cancer, liver, cirrhosis, and

初步的研究也顯示，AI 可以協助臨床醫師診斷大腸癌、肺癌、肝硬化，以及

In one study, board certified pathologists diagnosed colorectal cancer with 96.9% accuracy, and

在一項研究中，有證照的病理學家診斷大腸癌的準確度為 96.9%，而

AI slightly outperformed them, reaching an accurate diagnosis 98% of the time.

AI 的表現略勝一籌，準確診斷率達到 98%。

However, doctors shouldn't forget about the limitations of AI in diagnosing disease.

不過，醫師們還是別忘了 AI 在診斷疾病上的侷限性。

Biases can exist in any of the data sets, which can influence the way AI interprets the diagnosis.

任何資料集都可能存在偏見，這會影響 AI 解讀診斷的方式。

For example, if a data set is mostly made up of older patients, AI might not be able to accurately interpret findings for a younger age group after diagnosis.

舉例來說，如果一個資料集大部分都是老年病患，那麼 AI 在診斷後，可能就無法準確解讀年輕族群的發現。

AI can also aid physicians in treatment, particularly in the era of personalized medicine.

AI 也能協助醫師治療，尤其是在個人化醫療的時代。

Personalized medicine is a form of medicine that uses information about a person's genetics to prevent, diagnose, or treat disease.

個人化醫療是一種利用個人基因資訊來預防、診斷或治療疾病的醫學形式。

One specific example of this is is RHEUMATOID arthritis, an autoimmune disease where the body attacks itself, especially in joints like the wrist and finger joints.

其中一個具體的例子是類風濕性關節炎，這是一種自體免疫疾病，身體會攻擊自身，尤其是在手腕和手指關節等部位。

Since rheumatoid arthritis is such a complex and chronic disease, there are a variety of medications used to treat patients, and

由於類風濕性關節炎是一種複雜且慢性的疾病，有各種藥物用於治療病患，

treatment often depends on what each patient best responds to.

治療方式通常取決於每位病患的最佳反應。

Researchers at Mayo Clinic used genetic data and patients clinical characteristics to develop a machine learning algorithm to predict patient response to methotrexate, one of the most important rheumatoid arthritis drugs.

梅約診所的研究人員利用基因資料和病患的臨床特徵，開發了一種機器學習演算法，用來預測病患對 methotrexate 的反應，而 methotrexate 是治療類風濕性關節炎最重要的藥物之一。

Instead of having both doctors and patients wait months to determine the efficacy of a certain drug, these models can direct both doctors and patients toward more effective treatments, immediately saving the patient both time and money.

這些模型可以引導醫師和病患找到更有效的治療方法，立即為病患節省時間和金錢，而不是讓醫師和病患一起等待數月才能確定某種藥物的療效。

Similarly, researchers at the Georgia Institute of Technology and Ovarian Cancer Institute utilized machine learning algorithms to determine treatment effectiveness with 90% accuracy for certain chemotherapies in ovarian cancer patients.

同樣地，喬治亞理工學院和卵巢癌研究所的研究人員也利用機器學習演算法，以 90% 的準確度預測卵巢癌病患對某些化療的療效。

Utilizing AI to predict a patient's response to chemotherapy can save valuable time and spare patients from the destructive physical side effects and emotional burden and costs associated with weeks of treatment that may prove ineffective.

利用 AI 預測病患對化療的反應，可以節省寶貴的時間，並讓病患免於承受數週無效治療所帶來的破壞性身體副作用、情感負擔和相關費用。

Beyond diagnosis and treatment, AI has powerful potential in predicting the occurrence and progression of chronic diseases like hypertension, diabetes, and

除了診斷和治療，AI 在預測高血壓、糖尿病和腎臟病等慢性病的發生和進展方面，也展現出強大的潛力，

kidney disease, which can help patients live longer, healthier lives.

這能幫助病患活得更長久、更健康。

Let's take a closer look at diabetes using various machine learning models.

我們就來用各種機器學習模型，仔細看看糖尿病。

Researchers are developing predictive models to estimate a patient's current glucose level based on multiple factors such as their previous glucose levels, body mass index, external stress, and

研究人員正在開發預測模型，根據病患過去的血糖水準、身體質量指數、外部壓力，

甚至睡眠時數等多重因素，來估計病患目前的血糖值。

This can help patients anticipate when their blood sugar levels are critically high or low, enabling them to better prepare for emergencies.

這能幫助病患預期血糖何時會過高或過低，讓他們能更好地為緊急情況做準備。

Researchers have also used AI to develop models that predict the likelihood of developing diabetes based on various risk factors.

研究人員也利用 AI 開發模型，根據各種風險因素預測罹患糖尿病的可能性。

Early diagnosis and treatment of diabetes can prevent complications like diabetic kidney disease and blindness, which are emotionally distressing and costly for both patients and the healthcare system.

早期診斷和治療糖尿病，可以預防像糖尿病腎臟病和失明等併發症，這些併發症對病患和醫療體系來說，都會造成情感上的痛苦和高昂的費用。

In addition to patient care, AI is also furthering medicine by transforming the way medical research is conducted, particularly in clinical trials.

除了病患照護，AI 也在改變醫學研究的進行方式，特別是在臨床試驗方面，為醫學領域帶來更多進展。

Clinical trials study different interventions in patients, usually in the form of new vaccines or medications, to determine which treatment is more effective.

臨床試驗會針對病患研究不同的介入措施，通常是新的疫苗或藥物，來判斷哪種治療方法更有效。

In clinical practice, one of the first steps is identifying eligible patients, which is both time and resource consuming.

在臨床實務中，辨識符合條件的病患是首要步驟之一，這既耗時又耗費資源。

Since researchers must design and print brochures and manually screen clinics for eligible patients with access to medical records, AI can quickly identify which patients fit the right criteria to streamline the process.

研究人員必須設計並印製手冊，還要手動篩選診所，找出有權限存取病歷的病患，而 AI 可以快速找出符合條件的病患，讓流程更順暢。

In addition, AI has already proven effective in the development of new treatments.

此外，AI 在新療法開發上已經證明瞭它的有效性。

The first step in certain clinical research is discovering possible drugs.

某些臨床研究的第一步是發現潛在的藥物。

However, despite extensive lab testing, the discovery process can result in a waste of valuable time and resources.

然而，儘管經過廣泛的實驗室測試，這個發現過程仍可能浪費寶貴的時間和資源。

Many drugs that are effective in a lab fail in human trials.

許多在實驗室有效的藥物，在人體試驗中卻失敗了。

Verge Genomics was one of the first companies that discovered a potential drug for for amyotrophic lateral sclerosis, also known as ALS or Lou Gehrig's disease, by using AI instead of animal or cell testing instead of cell or animal data, they used AI to analyze human data points to provide researchers with more accurate representations of effective treatments in humans.

Verge Genomics 是最早利用 AI 發現肌萎縮性脊髓側索硬化症（也稱為 ALS 或葛瑞格氏症）潛在藥物的公司之一，他們沒有使用動物或細胞數據，而是利用 AI 分析人類數據點，為研究人員提供更準確的人類有效療法代表。

This avoided the risk of drug failure when translating animal studies to human trials.

這樣就避免了將動物研究轉譯到人體試驗時，藥物失敗的風險。

If you've experimented with ChatGPT, you've experienced AI's writing capabilities firsthand.

如果你用過 ChatGPT，你就能親身體驗 AI 的寫作能力。

While these AI writing tools are free, far from perfect, they can significantly reduce the time needed to prepare and revise manuscripts.

雖然這些 AI 寫作工具是免費的，離完美還差很遠，但它們能大幅縮短準備和修改論文的時間。

ChatGPT has been used in scientific research and has even been credited as a co author on multiple papers.

ChatGPT 已經被用於科學研究，甚至被列為多篇論文的共同作者。

However, it's not without its limitations, including the hard to ignore fact that it can reference incorrect data and create fake citations.

不過，它並非沒有限制，包括一個很難忽略的事實：它可能會引用錯誤的資料並編造假引用。

Other software, such as Consensus, can be used to guide your initial literature review and provide summaries for papers that answer your research question.

其他軟體，像是 Consensus，可以用來引導你初步的文獻回顧，並為回答你研究問題的論文提供摘要。

Don't expect these tools to take over your academic writing just yet, however.

不過，別期望這些工具現在就能完全取代你的學術寫作。

They can cache grammatical errors, brainstorm ideas, and collect and synthesize data.

它們可以快取文法錯誤、腦力激盪想法，以及收集和綜合資料。

We dug into the possibilities and drawbacks of using AI writing tools in another video on the future of medical school applications.

在另一支關於醫學院申請未來的影片中，我們深入探討了使用 AI 寫作工具的可能性和缺點。

If you think you can exclusively rely on ChatGPT for your medical school or residency applications, think again.

如果你認為可以完全依賴 ChatGPT 來申請醫學院或住院醫師職位，那就再想一想吧。

The third way AI is improving medicine is by streamlining administrative tasks in healthcare.

AI 改善醫療的第三種方式是透過簡化醫療保健中的行政任務。

For example, BotMD is a company with an AI service that assists patients with clinical issues like finding physicians on call, scheduling appointments, or answering prescription related questions, questions such as the availability of certain medications or their alternatives.

舉例來說，BotMD 這間公司就提供一項 AI 服務，協助病患處理臨床上的問題，像是尋找值班醫師、預約掛號，或是回答關於處方藥物的問題，像是特定藥物的庫存或替代方案。

Allowing AI to take over these tasks can free administrative staff to focus on other obligations.

讓 AI 接手這些任務，可以讓行政人員騰出時間專注於其他職責。

AI can also simplify medical scribing since physicians don't have to take their own notes or employ medical scribes as opposed to human scribes who are subject to human error.

AI 也能簡化醫療記錄，因為醫師不必自己做筆記，也不必聘請醫療記錄員，不像真人記錄員可能會出錯。

AI works instantly and immediately understands medical terminology.

AI 的運作即時且能立即理解醫療術語。

AI technology can pre authorize insurance and optimize billing for physicians.

AI 技術可以預先授權保險並優化醫師的帳單流程。

Since billing is reliant on accurate, consistent documentation, reducing the burden of these tasks can help prevent physician burnout, a significant problem that leads to psychological distress for doctors and worse outcomes for patients.

由於帳單流程仰賴準確且一致的文件記錄，減輕這些任務的負擔，有助於預防醫師倦怠，這是一個嚴重的問題，會導致醫師的心理壓力，並造成病患較差的照護結果。

Lastly, AI is quickly being integrated into medical education at all levels of training.

最後，AI 正在快速整合到各級培訓的醫學教育中。

For example, Oscar, an Australian medical education company, allows medical students to practice their history taking skills on AI patients.

舉例來說，澳洲的醫學教育公司 Oscar 讓醫學生可以在 AI 病患身上練習病史詢問技巧。

Using this tool, students can learn to ask the proper questions and consider various diagnoses for any specific present, integrating AI more thoroughly into formal medical curricula can prepare Future Physicians for AI's increasing role in healthcare.

透過這個工具，學生可以學到如何問對問題，並針對特定病症考慮各種診斷，將 AI 更深入地整合到正規醫學課程中，能讓未來的醫師為 AI 在醫療照護中日益增長的角色的做好準備。

Some universities, like Duke and Stanford, have already introduced courses to help medical students and residents learn to use AI to solve healthcare issues.

有些大學，像是 Duke 和 Stanford，已經開設課程，協助醫學生和住院醫師學習如何運用 AI 來解決醫療照護問題。

The Mayo Clinic and Stanford offer courses that teach physicians how AI is currently influencing medicine, as well as how they can use it to their advantage in their practice.

Mayo Clinic 和 Stanford 提供課程，教導醫師 AI 如何影響現今的醫療，以及如何在他們的執業中善用 AI。

AI is even influencing the medical school application process, which we made a separate video about.

AI 甚至影響了醫學院的申請流程，這部分我們有另外製作一支影片。

Link in the Description AI has huge potential to revolutionize many different facets of medicine, but

連結在說明欄。AI 有巨大的潛力能徹底改變醫療的各個面向，但

with any change, we should be cognizant of any drawbacks or possible biases we could be introducing.

就像任何改變一樣，我們應該意識到可能帶來的任何缺點或潛在偏見。

AI, after all, is designed and programmed by humans, so it's susceptible to the same biases we are.

畢竟 AI 是由人類設計和編寫的，所以它也容易受到我們同樣的偏見影響。

As AI technology rapidly evolves in all areas of our lives, from medicine to personal wellness to writing.

隨著 AI 技術在我們生活的所有領域，從醫療到個人健康再到寫作，都在快速發展。

We'll continue to cover emerging topics here on our YouTube channel and the Med School Insiders blog.

我們會在我們的 YouTube 頻道和 Med School Insiders 部落格持續關注新興議題。

Check out the Artificial Intelligence Playlist for more videos.

看看「人工智慧」播放清單，裡面有更多影片。

If you haven't already, sign up for the Med School Insiders newsletter, which sends our latest videos, guides, study strategies, job opportunities and music recommendations straight to your inbox Once a week, visit medschoolinsiders.com newsletter or visit the link in the description.

如果你還沒訂閱，請註冊 Med School Insiders 電子報，它會將我們最新的影片、指南、學習策略、工作機會和音樂推薦直接寄到你的收件匣。每週一次，請瀏覽 medschoolinsiders.com 電子報或點擊說明欄中的連結。

If you enjoyed this video, check out the top 10 most stressful jobs in Healthcare.

如果你喜歡這支影片，可以看看「醫療業十大壓力最大的工作」。