Chapter 1: The Current Landscape of Technology in Healthcare
This chapter will take you on a journey through the evolving landscape of healthcare technology. From the role that rudimentary technology has played to the explosive emergence of Artificial Intelligence, we will examine the transformative power of technology in the healthcare industry. Through real-life case studies, we'll understand the scope and limitations of the current systems and shed light on the promise that AI holds to revolutionize healthcare as we know it.
The Life-Saving Potential of Digital Resources
In 2017, Vipin Khadse found himself in a precarious situation. A medical student at a university in India, on his way to becoming a qualified doctor—he was on the train when a woman went into labor. The situation turned out to be very complicated; the baby was in a breech position, a potentially fatal complication if not managed correctly. With no other medical professionals around and limited resources, Khadse did what might have seemed unthinkable years ago—he turned to an instant messaging app (Whatsapp) for medical consultation. Following remote guidance from senior doctors, he successfully delivered the baby, turning what could have been a tragic event into a miraculous one. Technology became an invaluable resource, an extension of medical knowledge and expertise, and it probably saved two lives that day.
Switch continents and move forward a few years to a hospital room in Tokyo where a 60-year-old woman lay with what doctors believed was acute myeloid leukemia. The treatments they had administered were doing nothing to improve her condition, leaving them stumped and desperate. As a last-ditch effort, the team plugged her medical records into IBM's Watson, a medically trained AI capable of reading and analyzing over 20 million cancer research papers in less than 10 minutes. Watson analyzed her data and concluded that she had a rare form of leukemia that had been overlooked. A revised treatment plan, recommended by Watson and vetted by the doctors, was promptly initiated, which led to her improvement. Here again, technology—in the form of sophisticated artificial intelligence—stepped in to change the course of a patient’s life.
So, what do these two stories teach us about the state of technology in healthcare? In the first instance, even a simple messaging app, often taken for granted in our daily lives, became a crucial lifeline in a medical emergency. In the second, a far more complex technology—AI—was able to sift through many mountains of data to provide a diagnosis that human doctors had missed. These are not isolated incidents but rather indicators of a seismic shift in healthcare.
The question that naturally arises is: If this is what is possible now, what does the future hold? As we dig deeper into this chapter, we'll examine the various ways technology has been enhancing healthcare and how the rise of AI presents unprecedented possibilities—and challenges.
Bridging The Gap: From Traditional Tech to AI
These two gripping stories epitomize the dual narrative of healthcare technology today. In the first, we see a relatively rudimentary but incredibly useful form of technology—an instant messaging app—enabling remote consultations in emergency scenarios. In the second, we delve into the advanced capabilities of AI, a technology that promises to reshape the very foundations of healthcare, capable of complex diagnostics and personalized treatment plans.
But as you'll discover in this chapter, this duality represents not just the progress but also the challenges inherent in healthcare technology. We'll explore why some technologies, as practical as EHR systems, still grapple with issues like usability and interoperability while others, like telemedicine, must navigate ethical and regulatory minefields.
The advent of AI in healthcare introduces a fresh set of questions, ethical conundrums, and logistical challenges.
Yet it also offers unprecedented opportunities for enhancing patient outcomes, streamlining operations, and even tackling previously untreatable conditions.
As we proceed, this chapter will leave you with a comprehensive understanding of the state of healthcare technology. You will gain insights into its current capabilities, its limitations, and the manifold possibilities that the future holds—especially with the integration of AI.
So, are you ready to explore the digital lifelines of healthcare, the technologies that are saving lives today, and those that promise to revolutionize our tomorrow? Let's dive in.
Current Healthcare Technology
Medical experts utilize a range of technologies, such as X-rays, MRIs, and a plethora of lab testing devices. These tools are essential for making precise patient diagnoses.
Artificial intelligence assists in medical imaging by detecting minor fractures, identifying tumors, and pinpointing areas of bleeding, among other tasks. Trained medical professionals rely on such technology to diagnose and treat millions of patients each year. The pace of technological advancement has been particularly rapid in the last half-decade. Here are some examples:
● DNA genome research. Genomic research in DNA can flag potential diseases or conditions before any symptoms manifest in a patient. Early identification allows for more timely treatment. Looking ahead, there may be possibilities to either repair or inhibit faulty DNA.
● Ultra Sound. For years, medical applications of sound waves have ranged from imaging to targeting and eliminating certain cancerous tumors to enhancing brain function.
● Wearables. Technological advancements in wearables like fitness trackers and smart rings offer valuable tools for tracking medical conditions like hypertension, breathing issues, and irregular heartbeats. These devices are particularly useful for keeping an eye on at-risk populations, including infants, seniors, individuals with chronic illnesses, and those with specific disabilities.
● Robotics. Robotics in the operating room is becoming the standard of care to assist surgeons in using smaller incision lines, causing less damage to normal tissue. Precision robotics provide improved outcomes for brain, spine, and thoracic surgeries.
● MRNA technology. mRNA technology may have gained popularity during the Covid-19 pandemic, but the technology has been improving over the past decade. It uses messenger ribonucleic acid (mRNA) to transport lab-generated genetic data into human cells.
● Neurotech. Devices designed to observe and evaluate brain activity can also deliver external stimuli to influence neural functions. One such device under development is the Neuralink brain implant.
Those are but a few of the innovations made in the last five years, and many more are being researched. The large-scale adoption and implementation of these technologies is justifiably slow in the medical field, yet it is progressing regardless.
A significant emphasis in the expansion and advancement of medical technology is geared toward providing patients with access to their health data, as actionable medical information can guide individuals in making lifestyle adjustments.
The Rise of AI in Healthcare
What Exactly Is AI?
Before we embark on discussing how AI is shaping healthcare, it's crucial to understand what AI means. According to McKinsey (2023), AI refers to "software or hardware that is designed to mimic human capabilities of sensing, comprehending, acting, and learning." Within AI, there are more specific categories, such as Applied AI, which pertains to systems that are designed to handle specific tasks (like diagnosing diseases), and Machine Learning, a subset of AI where the system learns from data rather than following pre-programmed rules.
Artificial Intelligence, as defined by McCarthy (2007), is “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable” (IBM, 2023).
Machine learning falls under the umbrella of artificial intelligence and computer science, with a focus on leveraging data and algorithms to replicate human learning processes and incrementally enhance accuracy. The algorithms analyze data and then create their own set of criteria through which that data is identifiable and manipulable. (IBM, 2023b)
AI Technology Accessibility
AI isn't a new concept; its roots trace back to the mid-20th century and to the work of British Mathematician, computer scientist, and logician Alna Turning. However, what's changed dramatically in recent years is the accessibility and awareness of AI technologies. In the past, AI was largely the domain of specialized research institutions and corporate giants with immense resources. Today, owing to advancements in affordable consumer hardware and the prevalence of cloud computing, AI tools and platforms have become available to the public, thereby equalizing access to this potent technology.
Though it's well-recognized that machines surpass humans in computational abilities, the degree of advancement in machine capabilities has been staggering. Several computers have now surpassed the exascale threshold, signifying their ability to execute quintillion computations every second. To put it in perspective, such a machine could accomplish more calculations in a single second than a highly intelligent human could in 31.5 billion years.
Real-World Examples of Accessible AI
To highlight the broad accessibility of AI, let's look at a few examples of free or widely available programs that are transforming different sectors:
● ChatGPT was designed by OpenAI with GPT (Generative Pre-trained Transformer) architecture. It was designed to be a conversational service. The current version, as of the last training data in 2022, is built on GPT-4, a large-scale machine learning model trained to predict the next word in a sequence of words. GPT-4 possesses 175 billion machine learning parameters, enabling it to produce text that resembles human writing, based on its training data. The success of ChatGPT is rooted in the fact that the generated text has a human-esque tone, closely mimicking real human speech and conversation (Sedinkina, 2023).
● ChatGPT is engineered to help with various tasks such as responding to queries, offering clarifications, creating text, and participating in discussions. It has applications in customer service, tutoring, content creation, and more. While the technology is advanced and can perform many tasks at a level that seems human-like, it's important to note that it lacks true understanding, consciousness, or emotions.
The model is trained on a diverse range of internet text but doesn't have the ability to access real-time information, browse the internet, or understand context beyond the text input it receives. It is not a specialized expert but a generalist model capable of discussing a wide array of topics.
OpenAI has successfully released versions of GPT, each with improvements in scale and performance, and the technology continues to be a subject of active research and development. Chat GPT has garnered attention for its versatility and user-friendliness.
● Midjourney is an advanced generative artificial intelligence program and service, developed and maintained by the independent research lab Midjourney, Inc., based in San Francisco. It generates images from natural language descriptions, called prompts, like OpenAI’s DALL-E and Stability AI’s Stable Diffusion. The tool is currently in open beta, which it entered on July 12, 2021.
● Currently, Midjourney is exclusively accessible through a Discord bot on its official Discord server. You can interact with it by directly messaging the bot or inviting the bot to a third-party server. Users “create” artwork with Midjourney using Discord bot commands . To generate images, users use the /imagine command and type in a prompt; the bot then returns a set of four images. From there, the images can be enhanced or modified. Further tweaks can be made to the prompt to help refine the produced images. David Holz is leading the team responsible for Midjourney. He is a co-founder of Leap Motion. In an August 2022 interview with The Register, Holz disclosed that the company had already achieved profitability. (Claburn, 2022)
● Visily is an AI-powered wireframe tool capable of swiftly converting screenshots, templates, or text prompts into modifiable wireframes and prototypes. Tailored for non-designers, it garners trust from a vast array of product managers, founders, developers, and business analysts. Visily boasts an intuitive interface, a comprehensive UI library, and enhanced AI functionalities, which collectively have expedited the discovery process for numerous companies substantially. It has helped distinguish good ideas from bad ones and simplified communication for teams. Visily is also capable of auto-populating labels, names, numbers, and images to save time. The tool is free to sign up for and does not require a credit card. Visily is loved by many companies such as Rewst, Swept Janitorial Software, PayBIX, interviewing.io, and SenseHawk. (Visily, 2023)
● Bing, a web search engine, is owned and managed by Microsoft. It offers an array of search services such as web, video, image, and map search functionalities. It is developed using ASP.NET. Microsoft has also integrated artificial intelligence into Bing to enhance its capabilities. Some of the features and capabilities are
Bing Chat: A conversational agent that can answer questions, provide explanations, generate text, and engage in conversation. It can also create images from natural language descriptions, called prompts.
Bing Summary: A feature that can summarize the content of web pages, books, articles, and other documents. It can also generate key takeaways and compare different sources of information.
Bing Translate: A feature capable of converting text and speech among various languages. It can also detect the language of the input and provide alternative translations.
○ Bing Content: A feature that can help users create and improve their own content, such as poems, stories, emails, tweets, and more. It can also add humor, brevity, formality, and other styles to the text.
○ Bing Context: A feature that allows users to access Bing Chat from the Edge Sidebar and use the current web page as a source of information. It can also keep the context of previous questions and provide more relevant answers.
The Influence and Future of AI
The rise of artificial intelligence is at a critical turning point in the annals of human invention, exerting an irreversible influence on myriad sectors—from healthcare and education to transportation and commerce. This groundbreaking technology carries with it an almost incomprehensible potential for catalyzing positive societal and economic transformations. For instance, in the realm of healthcare, AI can assist in data management and diagnostic accuracy, develop personalized treatment plans, and even predict the likelihood of certain diseases long before symptoms manifest. Similarly, in transportation, the advent of autonomous vehicles promises to enhance safety while reducing environmental impact. However, technology is not without its complexities and ethical implications.
It is imperative that we approach AI with both cautious optimism and critical awareness, particularly in domains where human lives are directly affected. Take healthcare as a pertinent example: AI-powered diagnostics and treatment options can revolutionize medicine. However, improper or irresponsible use could lead to catastrophic errors, highlighting the need for rigorous testing, measured and controlled deployments, and the imposition of necessary ethical boundaries.
Ultimately, as we stand on the cusp of a future increasingly influenced by AI, technology presents us with both unparalleled opportunities and serious ethical dilemmas. Balancing these two facets—optimizing and successfully exploiting the enormous potential for social good while diligently mitigating risks—is essential for the responsible evolution and deployment of Artificial Intelligence.
Implications of AI for Healthcare
Artificial intelligence has the potential to revolutionize various aspects of healthcare, offering both opportunities and challenges. Below are some of the key implications.
● Improved Diagnostics. AI algorithms can analyze medical images, genomic sequences, and other data to help identify diseases more quickly and accurately than human professionals.
● Personalized Treatment. AI can evaluate a patient's medical history, genetic composition, and additional factors to suggest treatment plans that are highly customized.
● Predictive Analytics. AI can predict outbreaks of infectious diseases, patient admission rates, and other important phenomena, allowing for better resource allocation.
● Telemedicine and Remote Monitoring. AI-powered systems can facilitate remote healthcare delivery, which is especially useful in rural or underserved areas.
● Drug Discovery. AI algorithms are capable of scrutinizing extensive datasets to foresee the interactions between various drugs and their targets, considerably accelerating the drug discovery process.
● Efficiency and Cost-Effectiveness. One of the immediate impacts of AI is the automation of repetitive tasks. Mundane but essential duties like data entry or appointment scheduling could be fully automated, freeing healthcare professionals to focus on more complex tasks that require human judgment.
● Enhanced Surgical Procedures. Robotics powered by AI can assist surgeons and even carry out specific tasks autonomously during surgeries.
● Affordability and Accessibility. By automating various processes, healthcare could become more cost-effective, which in turn could make it more accessible to wider populations.
● Quality of Care. Far from rendering human expertise obsolete, AI acts as a tool that can help health professionals perform better at their jobs, enhancing diagnosis accuracy and personalized treatment plans, thus improving outcomes for patients.
● Expanding the Scope. AI's current applications include but are not limited to disease diagnostics, personalized treatments, and even gene editing. These topics will be explored more thoroughly later in the book.
Transforming the Healthcare Landscape
The most significant promise that AI holds for healthcare is a transformative shift in clinical workflows. As outlined by Insider Intelligence (2023), AI can add immense value by automating or augmenting most of the tasks performed by clinicians and staff, thereby making healthcare delivery faster, more accurate, and more efficient. The scope of its application is diverse and far-reaching, covering both visual and physical domains.
● Visual AI: This includes tasks such as managing electronic medical records, streamlining outpatient appointments, and tracking patient health through various applications.
● Physical AI: Here, the impact is even more tactile, involving robotic surgeries, automated drug dispensing systems, and other mechanical tasks traditionally performed by human hands. (Shuaib et al., 2020).
Deep learning
A form of Machine Learning capable of processing not just plain text but also more complex data types like images and videos, often with minimal human intervention.
The foundation for deep learning lies in the human brain's neural network, which serves as the model for arranging the neural networks in deep learning programs. As these machine-based neural networks consume more data, they are designed to progressively learn and identify increasingly complex aspects of the data they process.
As it processes data, the model can form hypotheses about the information it's analyzing and then refine its understanding based on the accuracy of these hypotheses. This recurrent learning becomes another dataset that can help the model make fresh observations about new data or inform other models for future predictions. Different kinds of deep learning neural networks include:
● Feed-Forward Neural Networks: These represent the most basic type of neural networks, where data travels in one direction only, moving continuously from the input layer directly to the output layer without looping back to previous data. The network is "trained" to analyze specific datasets so that it can later apply that knowledge to new, unfamiliar datasets. These types of neural networks are most employed in the banking industry for identifying fraudulent transactions. The initial training involves feeding the AI a dataset where fraudulent transactions are tagged while legitimate ones are not. The AI then leverages this foundational understanding to spot patterns in new transactions, aiding in the detection of fraud.
● Traditional Neural Networks (CNR). Modeled after the animal visual cortex, these networks excel at perceptual tasks similar to those their biological counterparts handle. They are particularly effective at processing images and recognizing elements within those pictures. Applications range from identifying brand logos in photos to recognizing animal species from images or even analyzing medical scans for diagnoses. Initially, the network is trained with images containing a specific subject, allowing it to identify the unique traits of that subject—like the distinctive wing pattern, beak length, or foot type of a bird. Once trained, the network can then identify those traits in new images by seeking those specific characteristics.
● Recurrent Neural Networks (RNN): Contrary to feed-forward networks, these networks permit data to circulate in both forward and backward directions. They reintroduce old data alongside new inputs, providing the system with a sense of temporal context. This is particularly useful for processing text and images. While they can perform tasks like those of feed-forward networks, they do so in a more nuanced manner due to their capacity to recognize specific patterns.
In a banking context, both feed-forward and RNN systems can detect fraudulent activities. However, RNNs can also learn from an individual's general financial behaviors as though they have a memory. When new transactions occur, RNNs compare them to the individual's past financial history. For example, if client A has never made a transaction exceeding $1,000 in a five-year span but suddenly makes two such transactions within three days, the system can flag this as potentially fraudulent due to the significant deviation from typical activity.
Deep Learning is one of the more important AI and Machine Learning subtypes that will be used in healthcare. A customized, medical version of it will be the basis of the analytical and data processing systems used for AI diagnostic assistance and personalized treatment recommendations.
The Future Awaits: An AI-Integrated Healthcare System
Imagine a healthcare system where AI plays a pivotal role in every aspect:
● A primary care appointment where an AI-powered virtual assistant initially gathers patient data, medical history, and current symptoms, enabling the doctor to spend more time on diagnosis and treatment.
● In hospitals, robotic arms assist surgeons with high-precision tasks, minimizing human error and increasing the overall safety of difficult procedures. The added surgical precision creates less tissue damage, and patients experience shorter recovery times.
● AI algorithms sift through all available and pertinent medical literature to suggest the most current and effective treatments for various diseases, personalized to each patient's genetic makeup. Achieved through complex simulation of each potential treatment's interaction with the unique biology/genetics of each patient to find the optimal result.
By incorporating AI into these aspects and more, healthcare could transform into a system that is not just faster and more efficient but also more precise and personalized.
AI has the potential to redefine healthcare by automating mundane tasks, improving the quality of care, and even expanding the horizons of what's medically possible. The use of AI in healthcare is poised for exponential growth, bringing forth opportunities and challenges that we can scarcely imagine.
What Lies Ahead
Now that we've taken a comprehensive look at the dynamic landscape of current healthcare technology, it's crucial to examine the other side of the coin. The next chapter will investigate the potential that AI has to enhance the utility of existing medical technology and personnel. This critical exploration will help us better understand the immeasurable value and urgent necessity of AI integration in healthcare while also granting an appreciation for the justified caution needed in this endeavor. Are you ready to bridge the gap between what is and what could be?
