Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology, and cardiology. We then review in more detail the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with a discussion about pioneer AI systems, such as IBM Watson, and hurdles for the real-life deployment of AI.
Overview of the medical artificial intelligence (AI) research
Recently AI techniques have sent vast waves across healthcare, even fuelling an active discussion of whether AI doctors will eventually replace human physicians in the future. We believe that human physicians will not be replaced by machines in the foreseeable future, but AI can definitely assist physicians to make better clinical decisions or even replace human judgment in certain functional areas of healthcare (eg, radiology). The increasing availability of healthcare data and the rapid development of big data analytic methods have made possible the recent successful applications of AI in healthcare. Guided by relevant clinical questions, powerful AI techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making.1–3 In this article, we survey the current status of AI in healthcare, as well as discuss its future. We first briefly review four relevant aspects from medical investigators’ perspectives:
1. motivations of applying AI in healthcare
2. data types that have been analyzed by AI systems
3. mechanisms that enable AI systems to generate clinical meaningful results
4. disease types that the AI communities are currently tackling.
Conclusion and discussion
We reviewed the motivation of using AI in healthcare, presented the various healthcare data that AI has analysed and surveyed the major disease types that AI has been deployed. We then discussed in detail the two major categories of AI devices: ML and NLP. For ML, we focused on the two most popular classical techniques: SVM and neural network, as well as the modern deep learning technique. We then surveyed the three major categories of AI applications in stroke care.
A successful AI system must possess the ML component for handling structured data (images, EP data, genetic data) and the NLP component for mining unstructured texts. The sophisticated algorithms then need to be trained through healthcare data before the system can assist physicians with disease diagnosis and treatment suggestions.
The IBM Watson system is a pioneer in this field. The system includes both ML and NLP modules and has made promising progress in oncology. For example, in cancer research, 99% of the treatment recommendations from Watson are coherent with the physician’s decisions.66 Furthermore, Watson collaborated with Quest Diagnostics to offer the AI Genetic Diagnostic Analysis.66 In addition, the system started to make an impact on actual clinical practices. For example, through analysing genetic data, Watson successfully identified the rare secondary leukemia caused by myelodysplastic syndromes in Japan.
The cloud-based CC-Cruiser in24 can be one prototype to connect an AI system with the front-end data input and the back-end clinical actions. More specifically, when patients come, with their permission, their demographic information and clinical data (images, EP results, genetic results, blood pressure, medical notes, and so on) are collected into the AI system. The AI system then uses the patients’ data to come up with clinical suggestions. These suggestions are sent to physicians to assist with their clinical decision making. Feedback about the suggestions (correct or wrong) will also be collected and fed back into the AI system so that it can keep improving accuracy.
Stroke is a chronic disease with acute events. Stroke management is a rather complicated process with a series of clinical decision points. Traditionally clinical research solely focused on a single or very limited clinical questions, while ignoring the continuous nature of stroke management. Taking the advantage of large amounts of data with rich information, AI is expected to help with studying much more complicated yet much closer to real-life clinical questions, which then leads to better decision making in stroke management. Recently, researchers have started endeavors in this direction and obtained promising initial results.
Although AI technologies are attracting substantial attention in medical research, the real-life implementation is still facing obstacles. The first hurdle comes from the regulations. Current regulations lack standards to assess the safety and efficacy of AI systems. To overcome the difficulty, the US FDA made the first attempt to provide guidance for assessing AI systems.68 The first guidance classifies AI systems to be the ‘general wellness products’, which are loosely regulated as long as the devices intended for only general wellness and present low risk to users. The second guidance justifies the use of real-world evidence to access the performance of AI systems. Lastly, the guidance clarifies the rules for the adaptive design in clinical trials, which would be widely used in assessing the operating characteristics of AI systems. Not long after the disclosure of these guidances, Arterys’ medical imaging platform became the first FDA-approved deep learning clinical platform that can help cardiologists to diagnose cardiac diseases.
The second hurdle is data exchange. In order to work well, AI systems need to be trained (continuously) by data from clinical studies. However, once an AI system gets deployed after initial training with historical data, the continuation of the data supply becomes a crucial issue for further development and improvement of the system. The current healthcare environment does not provide incentives for sharing data on the system. Nevertheless, a healthcare revolution is underway to stimulate data sharing in the USA.69 The reform starts with changing the health service payment scheme. Many payers, mostly insurance companies, have shifted from rewarding the physicians by shifting the treatment volume to the treatment outcome. Furthermore, the payers also reimburse for a medication or a treatment procedure by its efficiency. Under this new environment, all the parties in the healthcare system, the physicians, the pharmaceutical companies, and the patients, have greater incentives to compile and exchange information. Similar approaches are being explored in China.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
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Artificial intelligence in healthcare: past, present and future
Artificial intelligence in healthcare: past, present, and future
Received 12 June 2017 Accepted 14 June 2017 Published Online First 22 June 2017, Volume 2, Issue 4
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