A I has recently experienced an era of explosive growth across many industries, and the healthcare industry is no exception. Studies across multiple medical specialties have employed AI to mimic the diagnostic abilities of physicians [1] [2] [3] [4] . The hope is that AI may augment the ability of humans to provide healthcare. However, although these technologies are rapidly advancing, their implementation into patient-care settings has not yet become widespread. Here we review some key issues surrounding implementation of AI-based technologies in healthcare. AI involves the development of computer algorithms to perform tasks typically associated with human intelligence 5 . AI is broadly used in both the technical and popular lexicon to encompass a spectrum of learning, including but not limited to machine learning, representation learning, deep learning, and natural language processing (see Box 1 for definitions). Regardless of the specific technique, the general aim of these technologies in medicine is to use computer algorithms to uncover relevant information from data and to assist clinical decision-making 6 . AI technologies can perform a wide array of functions, such as aiding in diagnosis generation and therapy selection, making risk predictions and stratifying disease, reducing medical errors, and improving productivity 7, 8 (Fig. 1 ). Personal health data may include demographics, healthcare provider notes, images, laboratory results, genetic testing data, and recordings from medical devices or wearable sensors. A multitude of technology platforms may be involved in the generation or collection of this data, including network servers, electronic health records (EHRs), genomic data, personal computers, smart phones and mobile apps, and wearable devices and sensors. With improved global connectivity via the Internet and technology with cloudbased capabilities, data access and distribution has become easier as well. Integration of big data concerning health and disease will provide unprecedented opportunities in the management of healthcare information at the interface of patients, physicians, hospitals and healthcare authorities, and regulatory bodies (Fig. 2) . Potential roles for AI in clinical settings. We envision several ways in which AI-based technologies could be implemented into clinical practice. The first is as a triage or screening tool. For example, AI could analyze radiology images and use the probability of disease as a basis to decide which images should be interpreted first by a human radiologist 9 , or it could examine retinal images to determine which patients have a vision-threatening condition and should be referred to an ophthalmologist 2 . Similarly, the Babylon app, an AI-based chatbot being piloted in the United Kingdom 10 , is essentially a triage tool used to differentiate patients who simply need reassurance from those who need referral for an in-person examination. AI-based triaging would theoretically decrease burden on the healthcare system and direct resources toward the patients most likely to have a real medical need. AI technologies may also function in a replacement scenario. While unlikely to replace human healthcare providers entirely, AI may perform certain tasks with greater consistency, speed, and reproducibility than humans. Examples include estimation of bone age on radiographic exams 9 , diagnosing treatable retinal diseases on optical coherence tomography 2, 11 , or quantifying vessel stenosis and other metrics on cardiac imaging 12 . By automating tasks which are not theoretically complex but can be incredibly labor-and timeintensive, healthcare providers may be freed to tackle more complex tasks, representing an improved use of human capital. Perhaps the most powerful role for AI will be as an add-on to or augmentation of human providers. Studies have demonstrated a synergistic effect when clinicians and AI work together, producing better results than either alone 13, 14 . AI-based technologies could also augment real-time clinical decision support, resulting in improved efforts toward precision medicine 15 . Engagement in the AI health space. The AI health space seems to be constantly expanding. The National Science and Technology Council's Committee on Technology estimated that the US government's investment in research and development in AI-related technologies was ~$1.1 billion in 2015 and that this will substantially increase over subsequent years has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China. PersPective | FOCUS
While these initial results are positive, substantial translation or implementation of these technologies into clinical use has not yet transpired REF .
The practical implementation of artificial intelligence technologies in medicine
Nature Medicine
Nature Medicine
Medicine