In recent years, there has been an amplified focus on the use of artificial intelligence (AI) in various domains to resolve complex issues. Likewise, the adoption of artificial intelligence (AI) in healthcare is growing while radically changing the face of healthcare delivery. AI is being employed in a myriad of settings including hospitals, clinical laboratories, and research facilities. AI approaches employing machines to sense and comprehend data like humans has opened up previously unavailable or unrecognised opportunities for clinical practitioners and health service organisations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. Yet, there is an incomplete understanding of AI and even confusion as to what it is? Also, it is not completely clear what the implications are in using AI generally and in particular for clinicians? This chapter aims to cover these topics and also introduce the reader to the concept of AI, the theories behind AI programming and the various applications of AI in the medical domain.
- artificial intelligence
- healthcare delivery
- machine learning
- deep learning
- intelligent agent and neural networks
There has been an immense amount of discussion in recent years about the advent of artificial intelligence (AI) and the implication of its application in various domains. However, the concept of AI is not new and can be traced back to Ramon Llull’s theory of a reasoning machine in 1300 CE and even Aristotle’s syllogisms in 300 BC [1, 2]. However, it is only since the 1950s, clearer definitions and practical applications have been formulated [3, 4]. While there was a lull in the development of AI in the 70s and 80s because of loss of interest and funding, there has been in the most recent period a dramatic revival in the research and development of AI programs. Countries like China have prioritised AI development by investing billions of dollars into AI industrial hubs . Other nations and global corporations have also invested into AI programming and creation of innovative AI applications [6, 7, 8]. Building on this trend, institutions are now increasingly paying attention to application of AI in healthcare. AI is being used to improve the efficiency in delivery of healthcare and address previously intractable health problems [1, 9, 10]. The hundreds of AI-based healthcare applications being introduced into the market in recent years is a testimonial to this focus. Commentators have discussed how application of AI in healthcare is at the early stages and there is yet more to come [1, 4, 6]. However, is AI just hype and are entities investing into a bubble? To get an answer, we first need to understand what AI is and its approaches and tools. This chapter covers these issues and how they specifically apply to healthcare and what is next for the use of AI in healthcare?
2. Development and application of AI
So, what is AI? Because of the complexity involved in developing synthetic intelligence that is comparable to human intelligence, there are varying interpretations of what AI is and what goes into developing AI. Some authors even frown upon the term ‘AI’ and prefer the term ‘Computational Intelligence’ . However, if we consider what is the objective of AI and what resources go into achieving the objective, an acceptable definition encompassing these components can be fashioned. The end objective of AI is to create systems that think and act rationally like humans [2, 4, 12]. These systems can also be termed as ‘intelligent agents’ [2, 4]. If the goal of the system is to demonstrate intelligence and developing these systems requires computer programming, a formal definition of AI would read as ‘
2.2. Intelligent agent
AI theory can be best understood through the
2.3. What makes up AI?
In the past, researchers aimed for AI to replicate human intelligence . This approach is called ‘Classical AI’. However, this was a limiting approach as it assumed human intelligence is the only form of intelligence. This approach also assumes human intelligence is the most intelligence can be. Intelligence mainly comprises of learning and reasoning [3, 13]. Constructing intelligence does not have to be defined by the limitations human intelligence poses. An apt analogy to discuss here is flight. While bird flight may be a source of inspiration for constructing aeroplanes, the aeroplane structure is not replicating the anatomic structure of a bird. So in constructing AI, it is more important to incorporate the vital characteristics of intelligence than merely replicate human intelligence.
Learning is an essential characteristic of intelligence [2, 4, 11]. Learning involves acquiring new knowledge, developing new skills through instruction or practise and knowledge representation and experimentation. If AI comprises learning, it has to demonstrate all the aforementioned features. A very common process through which AI systems achieve learning objectives is by
Reasoning and knowledge representation are the other aspects of AI . In AI, reasoning involves manipulation of data to produce actions. Unlike traditional programming, the emphasis in AI is on what is to be computed rather than how it is to be computed? Structuring of this computation happens through design-time reasoning, offline computation and online computation. Earlier forms of AI involved algorithms based on the step-by-step reasoning model used to address predicated problems . However, these models were not useful for uncertain situations or when there was incomplete information. AI reasoning models have now evolved to respond to these situations by drawing upon concepts from probability and economic theories. To resolve problems-certain or uncertain, AI systems require widespread knowledge about the relevant environment and then be able to represent this knowledge in a computable form . For this to occur, AI uses a
2.4. AI tools
AI systems employ several tools to automate problem-solving tasks. These tools are based on AI principles, some of which were discussed in the previous sections. The tools are used to create AI applications to resolves issues across various disciplines and industries. Some commonly utilised tools are discussed in this section.
Search in AI system mirrors real-life problem solving but draws upon computing power to resolve the problems . Search problems are classified based on the amount of information that is available to the search process. This information may relate to the whole of the problem area or a specific component of the problem. AI through an independent search planning process analyses multiple options and identifies an optimal solution. AI adopts a faster and better process to search and optimisation than conventional techniques [17, 18]. The search process that separates AI from conventional techniques is its process remembers past results, learns and refines its performance in relation to past searches, plans its path forward and answers search queries akin to human intelligence. One such example of AI search and optimisation tool is
In their quest to replicate biological intelligence, AI researchers inspired by the biological nervous system have developed
Logic is important to reasoning, which in turn is a key component of intelligence. Classical logic is based on the assumption that only two truth-values (false and true) exist . This assumption is called
Another important AI technique is
3. AI in healthcare
AI lends itself to healthcare delivery very well. In fact, in the recent years there has been an exponential increase in the use of AI in clinical environments [1, 6, 21, 22, 23, 24]. With modern Medicine facing a significant challenge of acquiring, analysing and applying structured and unstructured data to treat or manage diseases, AI systems with their data-mining and pattern recognition capabilities come in handy. Medical AI is mainly concerned with the development of AI programs that help with the prediction, diagnosis and treatment or management of diseases. In contrast to non-AI medical software application, which relies on pure statistical analysis and probabilistic approaches, medical AI applications utilise symbolic models of diseases and analyse their relationship to patient signs and symptoms [1, 25, 26, 27]. For example, diagnostic AI applications gather and synthesise clinical data and compare information with predefined categories such as diseases to help with diagnosis and treatment. Medical AI applications have not just been used to support diagnosis but also treatment protocol development, drug development and patient monitoring too .
3.1. History of use of AI in healthcare
Discussion of the use of AI in medicine coincides with the advent AI in the modern era. This is not surprising as AI systems initially intend to replicate the functioning of the human brain . In 1970, William B Schwartz, a physician interested in the use of computing science in medicine, published an influential paper in the
3.2. Application of AI techniques in healthcare
The wide acceptance of AI in healthcare relates to the complexities of modern medicine, which involves acquisition and analysis of the copious amount of information and the limitation of clinicians to address these needs with just human intelligence. Medical AI applications with their advanced computing ability are overcoming this limitation and are using several techniques to assist clinicians in medical care.
AI is being used for all the three classical medical tasks: diagnosis, prognosis and therapy but mostly in the area of medical diagnosis [9, 32]. Generally, the medical diagnosis cycle (Figure 3) involves observation and examination of the patient, collection of patient data, interpretation of the data using the clinician’s knowledge and experience and then formulation of a diagnosis and a therapeutic plan by the physician. If we can compare the medical diagnostic cycle (Figure 3) to the concept of an intelligent agent system, the physician is the intelligent agent, the patient data is the input and the diagnosis is the output. There are several methods, through which AI systems can replicate this diagnostic cycle and assist clinicians with medical diagnosis. One such approach is the use of
Commonly, the application of the expert system approach in medical software programming is seen in
Unlike experts systems where a serial or sequential data processing approach is utilised, ANN processing utilises a parallel form of data processing analogous to the brain . In ANNs, the processing elements, otherwise called as neurons, process data simultaneously while communicating with each other. The processing elements are arranged in layers and the layers, in turn, are connected to each other. The links between the processing elements are associated with a numerical weight. The memory and adaptation of ANNs are adjusted by changing the weights, which leads to the amplification of the effects of afferent connection to each processing element. As a result of this architecture, ANNs can be trained to learn from experience, analyse non-linear data and manage inexact information. These abilities have led to ANN techniques being one of the most popularly utilised AI techniques in medicine . ANNs in addition to medical diagnosis have been used for radiology and histopathology analysis. In radiology, gamma camera, CT, ultrasound and MRI all create digital images, which can be manipulated by ANNs and used as inputs. The digitised inputs are then transmitted through the hidden and output layers to produce desired outputs (see Figure 2). Using the Backpropagation approach, a learning algorithm, ANNs have successfully identified orthopaedic trauma from radiographs . When ANNs and radiologists interpret the same radiological images separately, research has identified good diagnostic agreement [1, 36]. ANNs have also been used for analysis of cytological and histological specimens too [1, 25]. For example, ANNs has been used to screen abnormal cells from slide images for haematology and cervical cytology. Further, ANNs have also been used to interpret ECGs and EEGs through waveform analysis. For this to occur, a multi-layered neural network is trained with waveform data from both people with the disease and without . Evaluation of the waveform interpretations by ANNs has identified excellent pattern approximation and classification abilities and comparable in interpretation to clinicians.
There are an estimated 5 billion mobile phone subscriptions in the world . Many mobile phones now have memories and processing power equivalent to the capacity of mini-computers . So it is natural to see mobile communication devices being harnessed to deliver healthcare. The use of wireless communication devices to support delivery of healthcare is called
In addition to the examples discussed above, AI techniques have been successfully used in other areas of medicine. Genetic algorithm techniques have been used to predict outcomes in acutely ill and cancer patients, to analyse mammograms and MRI images and fuzzy logic techniques have been used in diagnosing various cancers, characterise ultrasound and CT scan images and predict survival in cancer patients and administer medication and anaesthetics [1, 6].
Of all the AI applications that have been developed over the past many decades, IBM’s Watson is one of the well-recognised applications. IBM Watson is a cognitive computing technology that groups together the competencies of reading, reasoning and learning to reply to questions or investigate original connections . IBM Watson aggregates huge volumes of structured and unstructured data from multiple sources into a single repository called Watson corpus. IBM incorporates machine learning and NLP techniques to process and analyse data to undertake problem solving. The technology of IBM Watson has been extended to the medical domain to assist medical scientists and clinicians in improving patient care [31, 51, 52, 53]. Some of the published examples of the use of IBM Watson in health care include automated problem list generation from electronic medical records, drug target identification and drug repurposing, interpretation of genetic testing results, oncological decision making support, and to support the roll-out of government healthcare programs.
3.3. Future trends and application of AI in healthcare
As more AI research is undertaken and AI systems become more trained and consequently intelligent, it is foreseeable that these agents replace some of, if not all, the human elements of clinical care . While leaving the communication of serious matters and final decision making to human clinicians, AI systems can take responsibility for routine and less risky diagnostic and treatment processes. The intention here is not to replace human clinicians but enable a streamlined high-quality healthcare delivery process.
Of all the promising medical AI novelties that are being explored, robotics driven by AI will have an important role in the medical automation process. Robots embody AI and give it a form, while AI algorithms/programming provide intelligence to the robots . Robotic assistants have already been employed to conduct surgeries, deliver medication and monitor hospital patients but the most promising area for their use is in elderly care . Mobile robotic assistants are already being used to assist the elderly people in their day-to-day activities either in their home or in aged care settings . The robotic assistants mainly undertake tasks that remind them of their routine activities including medication intake or guidance in their environments. With advances in AI and robotics, the employment of robotic assistants in elderly care is only bound to grow.
While the conventional thinking is that robots act as a vessel for a silicon-based artificial brain, there is emergence of a school of thought that imagines the use of biological brains in robots . With advances in science now allowing the culture of biological neurons, the potential use of a biological brain in a robotic frame through which it can sense the world and move around is not inconceivable. This
While the application of AI in delivery of healthcare has very promising potential, challenges-both technical and ethical exist. AI research is largely led and driven by computer scientists without medical training and it has been commented that this has led to a very technologically focused and problem oriented approach in the application of AI in healthcare delivery . Contemporary healthcare delivery models are very dependent on human reasoning, patient-clinician communication and establishing professional relationships with patients to ensure compliance. These aspects are something AI cannot replace easily. Use of robotic assistants in healthcare has raised issues about the mechanisation of care in vulnerable situations where human interaction and intervention is probably more appealing . There is also the reluctance of clinicians in adopting AI technologies that they envisage will eventually replace them. Yet there is no qualm in them using technologies that automate and speed up laboratory diagnostic process . This has led to some suggesting a model of co-habitation . This is a model that accommodates both the AI and human elements in healthcare delivery and anticipates the inevitable automatisation of significant components of medical processes while preserving the human aspects of clinical care like communication, procedures and decision-making.
Healthcare delivery has over years become complex and challenging. A large part of the complexity in delivering healthcare is because of the voluminous data that is generated in the process of healthcare, which has to be interpreted in an intelligent fashion. AI systems with their problem solving approach can address this need. Their intelligent architecture, which incorporates learning and reasoning and ability to act autonomously without requiring constant human attention, is alluring. Thus the medical domain has provided a fertile ground for AI researchers to test their techniques and in many instances; AI applications have successfully solved problems with outcomes comparable to that of human clinicians. As healthcare delivery becomes more expensive, stakeholders will increasingly look to solutions that can replace the expensive elements in patient care and AI solutions will be sought after in these situations. However, cold technology cannot totally replace the human elements in patient care and a model that incorporates both technological innovations and human care has to be investigated.
The chapter was submitted to a double blind review and it is in line with COPE Ethical Guidelines.
- In computer science, an algorithm is an explicit description of how to solve a class of problems? [2, 3, 4].