Eapen Bell R
Information Systems, McMaster University, Hamilton, ON, Canada.
Indian Dermatol Online J. 2020 Nov 8;11(6):881-889. doi: 10.4103/idoj.IDOJ_388_20. eCollection 2020 Nov-Dec.
Artificial Intelligence (AI) has surpassed dermatologists in skin cancer detection, but dermatology still lags behind radiology in its broader adoption. Building and using AI applications are becoming increasingly accessible. However, complex use cases may still require specialized expertise for design and deployment. AI has many applications in dermatology ranging from fundamental research, diagnostics, therapeutics, and cosmetic dermatology. The lack of standardization of images and privacy concerns are the foremost challenges stifling AI adoption. Dermatologists have a significant role to play in standardized data collection, curating data for machine learning, clinically validating AI solutions, and ultimately adopting this paradigm shift that is changing the way we practice.
人工智能(AI)在皮肤癌检测方面已经超越了皮肤科医生,但在更广泛的应用方面,皮肤科仍落后于放射科。构建和使用人工智能应用程序正变得越来越容易。然而,复杂的用例可能仍需要专业知识来进行设计和部署。人工智能在皮肤科有许多应用,包括基础研究、诊断、治疗和美容皮肤科。图像缺乏标准化和隐私问题是阻碍人工智能应用的首要挑战。皮肤科医生在标准化数据收集、整理用于机器学习的数据、对人工智能解决方案进行临床验证以及最终采用这种正在改变我们执业方式的范式转变方面发挥着重要作用。