Artificial Intelligence

Applications in Healthcare Delivery

Sandeep Reddy

• Publisher: Productivity Press
• Year: 2020
• Topic: Business
• eBook ISBN:
  9781000216868
• Subtopic: Information Management
• Index: Business

The rediscovery of the potential of artificial intelligence (AI) to improve healthcare delivery and patient outcomes has led to an increasing application of AI techniques such as deep learning, computer vision, natural language processing, and robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in medicine. These trends will mean an expanded role for AI in the provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare. This book discusses the different types of AI applicable to healthcare and their application in medicine, population health, genomics, healthcare administration, and delivery.

Readers, especially healthcare professionals and managers, will find the book useful to understand the different types of AI and how they are relevant to healthcare delivery. The book provides examples of AI being applied in medicine, population health, genomics, healthcare administration, and delivery and how they can commence applying AI in their health services. Researchers and technology professionals will also find the book useful to note current trends in the application of AI in healthcare and initiate their own projects to enable the application of AI in healthcare/medical domains.

Chapter 1

Algorithmic Medicine

Sandeep Reddy

Deakin University

CONTENTS

1.1 Introduction

1.2 AI in Medicine – A History

1.3 AI Types and Applications

1.3.1 Computer Vision

1.3.2 Natural Language Processing

1.3.3 Robotics

1.4 Challenges and Solutions

1.5 The Future

References

1.1 Introduction

For long health services have faced several challenges, chief among them being rising expenditure and workforce shortages without clear solutions in sight (Topol, 2019). At the same time, there has been an unprecedented generation of medical data ranging from sources such as electronic health records, medical imaging and laboratory units (Sidey-Gibbons & Sidey-Gibbons, 2019). Clinicians have for long relied on computers to analyse such data as the analysis of such complex, and large datasets exceed their human capacity. In this context, the emergence of artificial intelligence (AI) with its ability to significantly enhance the data analysis process has presented an opportunity for clinicians and healthcare administrators to gain better insights (Reddy, 2018). An opportunity to optimise care delivery, reduce healthcare delivery costs and support a stretched workforce.

Of the various AI approaches, the most pertinent to analysing data is machine learning (ML), which comprises aspects of mathematics, statistics and computational science (Sidey-Gibbons & Sidey-Gibbons, 2019). ML is the core of changes occurring in medicine because of AI. Unlike non-AI methods and software, which rely mainly on traditional statistical approaches, ML software utilises pattern detection and probabilistic approaches to predict medical outcomes (Reddy, 2018). This utilisation of ML algorithms and other AI approaches to deliver medical care is what can be termed as algorithmic medicine. The ability to predict crucial medical outcomes through AI algorithms can make healthcare more precise and efficient. Beyond medical care, AI can also support healthcare administration, drug discovery, population health screening and social assistance (Reddy, 2018), thus expanding the scope of algorithmic medicine beyond the confines of clinical care, i.e. direct clinician to patient care. This ability and promise have ignited the interest of governments and other healthcare stakeholders to consider incorporation of AI in healthcare administration and delivery seriously. This chapter outlines what would be involved for this to occur and what the impact will be.

1.2 AI in Medicine – A History

Before we define AI and describe its techniques, it will be pertinent to review the history of AI in healthcare. The concept of intelligent machines is not new and in fact can be traced to Ramon Llull’s theory of reasoning machine in the 14th century (Reddy, 2018). However, modern AI can be tracked back to the past 70 years with the term originating from the workshop organised by John McCarthy at Dartmouth College in 1956 (AAIH, 2019). In the following decade, the availability of faster and cheaper computers allowed experimentation with AI models particularly in the areas of problem-solving and interpretation of spoken language (Anyoha, 2017). However, as work progressed in these areas, the lack of requisite computational power and the limitations of the then algorithmic models came to fore. In the 1980s, there was a revival of interest in AI particularly so in expert systems, which were modelled to mimic the decision-making process of a human expert (Figure 1.1). However, again these types of models fell short of expectations, and interest in AI in both academia and industry waned. Commencing in the mid-2000s, the availability of suitable technical hardware and emergence of neural networks, an advanced form of ML, coupled with their demonstrable performance in image and speech recognition once again brought AI back to the limelight. Since then, significant funding and interest has led to further advances in algorithms, hardware, infrastructure, and research.

Figure 1.1 History of AI and its use in medicine.

Paralleling the general history of AI, its use in medicine formally commenced with the DENDRAL project in the 1960s, which was an early expert system with an objective to define organic compound structures by investigating their mass spectra (AAIH, 2019). The development of this system required new theories and programming. This was followed by MYCIN in the 1970s, which was aimed at identifying infections and recommending appropriate treatment. The learning from MYCIN was extrapolated to develop the CADUSEUS system in the 1980s. This system was then hyped as the most knowledgeable medical expert system in existence. In line with the general history of expert system, the application of expert systems in medicine fell short of expectations. The sophistication of neural networks and availability of hardware to run these algorithms presented a new opportunity for the use of AI in medicine (Naylor, 2018; Reddy, 2018). Since then, increasing evidence has been detailed of what AI models can do in terms of medical imaging interpretation, support for clinical diagnosis, drug discovery and clinical natural language processing.

1.3 AI Types and Applications

Before we discuss the different types of AI and its applications, it is important to define what AI is? There are numerous definitions of AI in the literature, but this one derived from computer science describes AI as “the study of intelligent agents and systems, exhibiting the ability to accomplish complex goals” (AAIH, 2019). However, this definition is oriented to an academic perspective. From an application and industry perspective, AI can be best described as “machines assuming intelligence”. Now that we have defined AI, it is pertinent to mention here two levels of AI: General and Narrow AI. General AI, also referred to as Artificial General Intelligence, is when AI exhibits “a full range of cognitive abilities or general intelligence actions by an intelligent agent or system” (AAIH, 2019), while Narrow AI, also referred to as Weak AI, is where AI is specified to address a singular or limited task.

The predominant approach of AI, currently, is ML (Figure 1.2). This approach involves performing tasks without explicit instructions relying mainly on patterns and relationships in the training data and environment (AAIH, 2019). To develop ML models, you will need to define the necessary features, i.e. dependent and independent or input and target variables, and develop datasets including the features. Further to this, you split up the dataset into training and test datasets to allow for internal validation. Following this, the datasets are trained or tested with relevant ML algorithms. If the training dataset contains the input data and the appropriate output/target variable, then it is termed supervised learning (El Morr & Ali-Hassan, 2019). However, if there is no known output and the algorithm is left to detect hidden patterns or structures within the dataset, then this is unsupervised learning. In recent years, a hybrid form where the training set has a mix of labelled and unlabelled data and the expectation is that a function predicting the target variable is arrived at, which is termed semi-supervised learning (El Morr & Ali-Hassan, 2019).

Figure 1.2 AI types, learning approaches and applications.

ML algorithm development does not necessarily have to adopt the training approach described above. Reinforcement learning, a relatively newer form of ML, involves a process of maximising reward function based on the actions taken by the agent (AAIH, 2019). A trial-and-error approach is adopted to eventually arrive at optimal decision-making by the agent. In generative learning, the model development involves creating new examples from the same distribution as the training set and in certain instances with a particular label. The evolutionary algorithm model builds on this approach where initially developed algorithms are tested for their fitness, similar to an evolutionary process, until peak performing algorithms are identified and no more progress in fitness of the group can be derived (AAIH, 2019).

While there are numerous ML algorithms in use, a couple of commonly used algorithms in medicine are linear regression, logistic regression, decision trees, random forest and support vector machines (SVMs). An advanced form of ML that excels at analysing complex patterns between variables in datasets is deep learning (DL) (Topol, 2019). This approach is inspired by the architecture and ability of human brains whereby learning and complex analysis is achieved through interconnected neurons and their synapses. This is computationally simulated through many layers of artificial neurons between the input and output variables. These artificial neurons through a hierarchical and interconnected process are programmed to detect complex features and the model depending on complexity of data adds necessary number of layers (auto-didactic quality) (Topol, 2019). Sandwiched between the input and output layers are the hidden layers (see Figure 1.3), which adds to the feature optimisation and model performance but also creates opacity about the decision-making process of the model.

Figure 1.3 Representation of the neural network architecture (Creative Commons License).

While there are myriad ways as to how neural networks and AI are in use in healthcare, three applications where they are mostly used or have most promise are profiled: computer vision, natural language processing and robotics.

1.3.1 Computer Vision

Computer Vision (CV) is where computers assist in image and video recognition and interpretation (Howarth & Jaokar, 2019). Increasingly DL has become central to the operation of CV. This is due to DL’s many layers useful for identifying and modelling the different aspects of an image. In particular, convolutional neural networks (CNNs), a form of DL, involve a series of convolutions and max-pooling layers (see Figure 1.4) as its underlying architecture has been found to be very useful in image classification (AAIH, 2019; Erickson, 2019). CNNs are credited for reviving interest in neural networks in recent years. The way the CNNs work is by commencing with low-level features in the image and progress to higher-level features that represent the more complex components of the image. For example, the first layers will identify points, lines and edges, and the latter layers will combine these to identify the target class. An early example of CNN was AlexNet, an image classification model (AAIH, 2019). More recent versions are CNNs with specialised layers including ResNet, ResNeXt and region-based CNN (Erickson, 2019).

Figure 1.4 Architecture of a CNN (Creative Commons License).

CNNs are increasingly ...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Foreword
• Editor
• Technical Reviewers
• Contributors
• 1 Algorithmic Medicine
• 2 Use of Artificial Intelligence in the Screening and Treatment of Chronic Diseases
• 3 AI and Drug Discovery
• 4 Mammographic Screening and Breast Cancer Management – Part 1
• 5 Mammographic Screening and Breast Cancer Management – Part 2
• 6 Deep Learning for Drawing Insights from Patient Data for Diagnosis and Treatment
• 7 A Simple and Replicable Framework for the Implementation of Clinical Data Science
• 8 Clinical Artificial Intelligence – Technology Application or Change Management?
• 9 Impacting Perioperative Quality and Patient Safety Using Artificial Intelligence
• 10 Application of an Intelligent Stochastic Optimization Nonlinear Model
• 11 Audit of Artificial Intelligence Algorithms and Its Impact in Relieving Shortage of Specialist Doctors
• 12 Knowledge Management in a Learning Health System
• 13 Transfer Learning to Enhance Amenorrhea Status Prediction in Cancer and Fertility Data with Missing Values
• 14 AMD Severity Prediction and Explainability Using Image Registration and Deep Embedded Clustering
• 15 Application of Artificial Intelligence in Thyroidology
• 16 Use of Artificial Intelligence in Sepsis Detection and Management
• 17 Transforming Clinical Trials with Artificial Intelligence
• 18 An Industry Review of Neuromorphic Chips
• 19 Artificial Empathy – An Artificial Intelligence Challenge
• Index