Adapting to Artificial IntelligenceRadiologists and Pathologists as Information Specialists

這篇文章談論放射科/病理科醫師的職涯變化。在早期造影科技/免疫染色還沒有發達的時候,放射科/病理科醫師的任務比較像是從單一影像進行判讀。然而,今日大型全身造影技術/自動化切片使得放射科/病理科醫師必須面從大量影像中找出診斷所需資訊(For example, a radiologist typically views 4000 images in a CT scan of multiple body parts (“pan scan”) in patients with multiple trauma. The abundance of data has changed how radiologists interpret images; from pattern recognition, with clinical context, to searching for needles in haystacks; from inference to detection. The radiologist, once a maestro with a chest radiograph, is now often visually fatigued searching for an occult fracture in a pan scan.)

原文談論,隨着人工智慧的快速發展,放射科醫師應該重新思考職涯定位,要能接受某些任務逐步由CADx所取代。(To avoid being replaced by computers, radiologists must allow themselves to be displaced by computers. While some radiographic analyses can be automated, others cannot. Radiologists should identify cognitively simple tasks that could be addressed by artificial intelligence, such as screening for lung cancer on CT.)(文章說,美國放射科醫師浪費大量時間用 protable 爲 ICU 病患確認 endo 位置。這種事也得找放射科看喔…)

(未來的)放射科/病理科應該被整併爲一種新的專科「資訊專科」,換句話說,就是協助提升人工智慧診斷率的醫師。與此同時,將機器輸出的成果與臨牀情景結合,並機動的決定是否要加作額外的檢查。(The information specialist would not spend time inferring conditions between competing shadows on radiographs, scroll through hundreds of images looking for pulmonary embolus on CT, or examine slides for “orphan Annie”–shaped nuclei. Artificial intelligence could perform many such tasks. The information specialist would interpret the important data, advise on the added value of another diagnostic test, such as the need for additional imaging, anatomical pathology, or a laboratory test, and integrate information to guide clinicians. Radiologists and pathologists will still be the physician’s physician.)

潛在性的好處是,可以通過一個「information specialist」配上一群人工智慧機器,爲非洲一整個城鎮的居民進行篩檢。( A single information specialist, with the help of artificial intelligence, could potentially manage screening for an entire town in Africa.) (這讓我想到 Shang-Jui Tsai 大大的 #Cubix_Health_Tech)

文末提到了一些專科訓練改革的建議。我個人的看法是病理科的自動化可能會比較快到來。但是,gross pathology 涉及到在複雜的外科標本下切取病竈,尚未看到足堪負荷的任何機器。不過如果未來 automated microscopic pathology 足夠成熟,整組標本放下去切就好了 XD 還可以用雷射之類逐層切取。

該文延伸閱讀

  • 關於人工智慧取代放射科的可能性: Jha S. Will computers replace radiologists? Medscape. May 12, 2016. http://www.medscape.com/viewarticle/863127. Accessed November 15, 2016.
  • 使用 IBM Watson 診斷肺栓塞: McMillan R, Dwoskin E. IBM crafts a role for artificial intelligence in medicine. Wall Street Journal. August 11, 2015.
  • 使用人工智慧偵測肺小細胞癌病理切片: Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7(7):12474.