Artificial Intelligence in Medicine – AIM 2020

Artificial Intelligence in Medicine (AIM) came back into the limelight in 2018 when the Royal College of Physicians, London invited Babylon’s Team (Babylon a healthcare chatbot) to make a presentation at its 2018 conference. The NHS-UK decided to use Babylon to triage patients, hopefully shortening the waiting time for patients to consult their GPs. This is normally done by a triaging nurse and GP when a patient calls to get an appointment out of hours. Babylon published a paper about the Chatbot and Enrico Coeria has commented on why Babylon may not yet be ready to triage real-patients.

What is Artificial Intelligence?

What is AIM?

Medicine is defined as the science and art dealing with the maintenance of health and the prevention, alleviation, or cure of disease. Merriam Webster Dictionary [1]: It defines Artificial Intelligence as a branch of computer science dealing with the simulation of intelligent behavior in computers; the capability of a machine to imitate intelligent human behavior. The philosophical question “Is medicine science or art?” has been simply skipped in the Merriam Webster’s definition by taking both science and art in the considered definition.

From the time computers came into use in the 1950’s scientists were aiming to create programs that were intelligent (thinking and reasoning) like human beings. In 1956 John McCarthy who coined the term ‘Artificial Intelligence’ (AI) (Ref) proposed at the Dartmouth Conference that  “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

This summer has extended into more than half a century. Physicians were also captivated by the potential AI could have in medicine. With computer power to store vast amounts of data and processing power, it was thought that computers would become ‘doctors in a box’ assisting and surpassing clinicians with tasks like diagnosis (Ref). With this background computer scientists and healthcare professionals mainly from USA working together formed the new discipline of  ‘Artificial Intelligence in Medicine’ (AIM).

In reviewing the emerging field of AI in medicine, Clancey and Shortliffe in 1984 provided the following definition: ‘Medical artificial intelligence is primarily concerned with the construction of AI programs that perform diagnosis and make therapy recommendations. Unlike medical applications based on other programming methods, such as purely statistical and probabilistic methods, medical AI programs are based on symbolic models of disease entities and their relationship to patient factors and clinical manifestations.’ (Ref)

The field of AI had two schools of thought. Proponents of so-called ‘strong’ AI were interested in creating computer systems whose behaviour is at some level indistinguishable from that of humans (Interlude Box IC2.1 – Ref Turin Test). Success in strong AI would result in computer minds that could reside in autonomous physical beings such as robots or perhaps live in ‘virtual’ worlds such as the information space created by something like the Internet. (Ref – Coiera, Enrico. Guide to Health Informatics, Third Edition, 3rd Edition. CRC Press.) The ‘weak’ AI  looked at human cognition and decide how it can be supported for complex or difficult tasks. For example, a fighter pilot may need the help of intelligent systems to assist in flying an aircraft that is too complex for humans to operate on their own. These ‘weak’ AI systems are not intended to have an independent existence, but instead are a form of ‘cognitive prosthesis’ that supports a human in a variety of tasks.———————–The Turin TestHave this in a BOXYouTube clip that is less than 2 minutes————————The progress of the strong AIThe progress of weak AI

What is Machine Learning? AI is a branch of computer science that tried to make computers more intelligent. A basic requirement for intelligent behaviour in learning. Most experts believe that without learning there can be no intelligence. Machine learning is a major branch of AI and a rapidly developing subfields of AI (Ref). (This is a key paper to understand ML and the three branches – Baysean classifier, Neural Networks and Decision Trees) From the very beginning, three major branches of machine learning emerged. Classical work in symbolic learning is described by Hunt et al. [5], in statistical methods by Nilsson [6], and in neural networks by Rosenblatt [7]. Bayesian classifier example and explanation – link Jordan ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines (see below) to design algorithms that process data, make predictions and help make decisions (MJ Berkley). ML would soon power not only Amazon but essentially any company in which decisions could be tied to large-scale data. New business models would emerge. The phrase “Data Science” began to be used to refer to this phenomenon, reflecting the need of ML algorithms experts to partner with database and distributed-systems experts to build scalable, robust ML systems, and reflecting the larger social and environmental scope of the resulting systems. This confluence of ideas and technology trends has been rebranded as “AI” over the past few years. This rebranding is worthy of some scrutiny.

ChatBot‘Chatbots can be defined as software agents that converse through a chat interface. Now, what that means is that they’re software programs that are able to have a conversation, which provides some kind of value to the end user. The user can interact with the chatbot by typing in their end of the conversation, or simply by using their voice, depending on the type of chatbot provided. Virtual assistants like Apple Siri or Amazon Alexa are two examples of popular chatbots interacting via voice rather than text. Typically, the chatbot will greet the user and then invite them to ask some kind of question. When the user replies, the chatbot will parse the input and figure out what’s the intention of the user’s question. Finally, it will respond in some form of consequential or logical manner, either providing information or asking for further details before ultimately answering the question.’ (Ref)

Reviews re AIM – historical order Computer Programs to support clinical decision making –  1987 Shortlife Coming of Age in AI -2008 Patel Shortliffe Thirty years of AIM review of research themes – 2015 – AIM Peek Artificial intelligence in medicine – 2017 Hamet Artificial Intelligence in Medical Practice: The Question to the Answer? – AJM – 2018 Miller Topol The Medscape Editor Eric Topol’s articles about AIM The image below has all papers Toplo think is methodologicaly good for thned