Artificial intelligence (AI) has been around for decades, but the revolution of AI in healthcare is upon us and gaining serious momentum! In fact, were you aware that 86% of all healthcare organizations, life science, and technology companies were already using some sort of AI technology in 2019?!
According to the latest data, valued at a massive $11 billion in 2021, AI applications in healthcare are set to reach a whopping $180 billion by the year 2030 - a 16-fold increase in just nine years.
Artificial intelligence (AI) in healthcare market size worldwide from 2021 to 2030 (in billion U.S. dollars)
These projections signal a serious investment of AI applications in healthcare. Just like ChatGPT, CoPilot, or similar applications which are readily popping up on our personal devices, these solutions are becoming more and more integrated with our daily lives. Specifically, with the way we look after ourselves.
AI in healthcare is a rapidly, expanding phenomenon set to transform the industry in many ways not yet fully understood. As such, it will take a lot of unpacking to keep up with its advancement.
Given this, we thought we’d roll up our sleeves and dive into a series of posts about AI and healthcare conversations to break down what it all means, and what’s in store
…for our own health,
…for the wider medical community, and
…for all of us techies determined to leverage the right applications and solutions for your organization's needs!
The Fundamentals: What is AI in Healthcare?
In order to start tackling the healthcare AI juggernaut, let’s start from the ground up and answer a few fundamental questions:
…What exactly is AI in healthcare?
…What types of AI work in the industry?
…In what healthcare areas does AI function?
…How is AI used in healthcare right now?
Simply put, AI in healthcare is the application of machines which aim to mimic human cognition, to analyze and act on our health data.
At present, AI in healthcare is predominantly predictive and based on learned patterns. This is unlike ChatGPT or other similar applications, which are generative and able to create new things.
Predictive AI uses statistical analysis to identify patterns, anticipate behaviors, and forecast future health events based on vast amounts of data.
Essentially then, one of the primary goals of AI solutions in healthcare is to predict a health-related outcome based on a patient’s current and historical clinical data. Of course, as AI uses in healthcare expand, wider and more advanced applications and paradigms will develop, including generative technology.
We’ll come back to highlight some of these emerging developments a bit later. But for now, what’s important to highlight is that AI in healthcare is not just about one technology, it represents a whole range of them, such as
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machine learning (ML),
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natural language processing, and
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physical robots, to name a few.
Each of these AI types support various healthcare tasks and processes.
Here are the primary ones in operation:
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Machine Learning (ML)
One of the most common examples of AI uses in healthcare, machine learning is essentially a statistical technique for fitting models to data, to then learn from the data. It’s widely used in searching and processing large amounts of medical information and precision medicine. |
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Deep Learning
A form of Machine Learning, deep learning is separated out here due to its high complexity. Deep learning is a neural network model which sees problems as inputs, outputs and variables - it can process information in multiple ways to predict outcomes. Common uses include analyzing medical imaging, and speech recognition. |
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Natural Language Processing (NLP)
This is simply AI which makes sense of human language. Natural language processing has been used for decades and is widely in operation today to make sense of patients’ clinical notes, reporting and transcribing patient-clinician interactions. |
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Rule-based Expert Systems
Rule-based expert systems are a type of AI which, like NLP above, has been around for decades. They are essentially systems with ‘if-then’ rules. These systems are built standard into electronic health record systems (EHRs) and help support clinical decisions. |
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Robots
AI robots are a well-known form of AI tech used routinely in manual automated processes like lifting, assembling, and repositioning healthcare equipment. Robots are increasingly being fitted with additional AI capabilities to allow for human collaboration. An example of this is surgical robots which perform anything from neck and back surgery to tumor removal. |
All of these AI technologies are an active and essential part of our global healthcare infrastructure and being used in various healthcare areas.
Want to know how exactly they are used?
7 Different AI Applications in Healthcare
Check out this list of AI applications in healthcare being employed right now:
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1. Predictive Analysis
AI is being used to forecast health issues based on analyzing patterns in patient data. This ultimately leads to better preventative care and treatment. |
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2. Diagnosis & Treatment
Healthcare AI is pulling vast amounts of patient data from various sources, including medical records, labs, imaging results, and more, to help healthcare providers make better diagnoses and develop more effective treatment plans. |
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3. Enhanced Patient Care
Virtual healthcare agents are being added to a patient's care team and can offer first-line and round-the-clock support with personalized health advice, and appointment or medication reminders. |
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4. Remote Monitoring & Telemedicine
AI-powered devices and platforms are being used to monitor patients' health anywhere and intervene in real-time. Telemedicine provides much needed healthcare access for underserved and rural patients, personalized health advice, and appointment or medication reminders. |
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5. Drug Discovery & Development
AI algorithms are analyzing and identifying molecular structures for drug discovery, to better predict drug efficacy and side effects - leading to new and more robust therapies. |
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6. Clinical Trials
AI is being used in clinical trials to streamline the drug testing and approval process, including reducing sample sizes and improving recruitment and accessibility for underprivileged populations. |
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7. Healthcare Administration
AI is working alongside healthcare operations to optimize resources, predict demand for medical services, and manage inventory and supply chains. |
This list represents a small set of AI in healthcare examples and real world solutions currently being implemented. However, there is still so much to uncover about AI’s applications and what it means for the future of the healthcare and tech industries. Given this, let’s delve a bit deeper with the following key question:
What are some of AI’s biggest impacts on the healthcare industry?
Regardless of what you think about AI in healthcare, or any other industry for that matter, the reality is that it’s now an established and increasingly familiar technology that we must come to understand.
As AI gains momentum in environments where the central focus is on the safety and prosperity of human life, the benefits of AI in healthcare, as well as its limitations, must be carefully evaluated and balanced.
Let’s review the pros and cons of AI in healthcare. First, the advantages and benefits:
3 Key Benefits of AI in Healthcare
✅ AI allows for instantaneous, real-time and real-world patient data
Real-world data is an incredibly powerful tool for medical professionals to access, analyze and make critical clinical decisions about their patients’ healthcare needs. This lies at the heart of
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disease prevention,
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better diagnosis, and
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personalized treatment plans.
✅ AI saves time and money
The economic impact of AI in healthcare is already massive and set to save the industry billions of dollars! According to CMS.gov, nearly a quarter of all healthcare dollars spent in the US—which in 2022 was $4.5 trillion—was wasted on subpar deliveries and system inefficiencies.
Healthcare AI’s abilities to streamline and transform a wide range of tasks, including medical coding, reviewing insurance claims and medical records, and disease detection, will undoubtedly stem the precipitous rise in annual healthcare expenditures.
✅ AI improves patient access to healthcare
From telemedicine to remote monitoring to greater access to clinical research, AI is providing a vehicle to ensure the underprivileged, underserved, and underrepresented sections of our populations have access to healthcare.
Not only will this improve the health of these populations and, ultimately the world, it will vastly increase and improve the richness of patient data for better research, surveillance, and analysis of illness. In this way, the healthcare community is always geared towards prevention and staying ahead of the evolution of disease.
Next up on our list of AI impacts, are the limitations.
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The Limitations of AI in Healthcare
❌ AI may lead to biased and unequal outcomes for patients
Issues with accessing all relevant patient data, given data collection deficiencies or exchange issues between healthcare institutions, may result in suboptimal conditions for algorithm development. This has the potential to lead to a range of issues including biased or insufficient outcomes, and/or inaccurate predictions and recommendations in the clinical setting.
❌ AI may not be able to consider all of an individual’s healthcare needs
An individual’s health is impacted by more than just their physical condition, and includes social, economic and environmental variables. Given AI in healthcare is limited to patient health data, it may make recommendations on key tasks like diagnosis, treatment plans, and prescriptions without incorporating and considering these other factors.
❌ AI may increase disparities in the labor market
Despite helping to reduce healthcare costs and maximize efficiencies, there may be a negative economic impact of AI in healthcare which could contribute to mass layoffs.
The United Nations has certainly expressed its concerns about AI’s socio-economic impact. Without proper government policies and institutions in place, they note that AI’s disruptions as an advanced technology may have serious societal consequences, including large disparities in the human workforce.
The pros and cons of AI in healthcare extend well beyond this list, but these highlight key issues which will require a lot of investment in time and money to better understand them.
Microsoft’s five-year $20 million ‘AI for Good’ Initiative launched in 2020 is a prime example of these efforts to bring researchers and organizations together to analyze and evaluate AI’s impacts in healthcare, including its ethical implications and risks- two final topics we feel are important to touch upon.
The Ethics of AI in Healthcare
The ethics of AI in healthcare is a constantly evolving field that raises new issues for the industry to manage.
While data security and privacy concerns are always widely publicized and at the forefront of ethical debates, Khan and fellow researchers contend that accountability with AI is the real ethical issue that the industry and its stakeholders should be focused on.
Otherwise known as the ‘black box’ problem, it’s underpinned by a fundamental question -
Who is ultimately responsible for AI’s medical applications, especially if something goes wrong?
… Is it the treating physician despite the fact they had no input into the algorithms?
… Or is it the developer, even though they have no idea about the clinical setting?
This inevitably leads us to the next critical issue, the risks of AI in healthcare.
What are the Risks of AI in healthcare?
The ‘black box’ problem and other ethical issues raised by AI reminds us that there are real risks associated with this technology, and we’ve outlined a few here:
⚠️ AI technology may provide inaccurate and/or unreliable outcomes
With any new technology, there are very real and genuine concerns about its ability to accurately and reliably perform. AI optimally operates on large and complete datasets. Therefore, any instances where very little information exists on illness, treatment population, or environmental factors may lead to issues in diagnosis, prescribing, and treatment.
⚠️ AI may threaten data privacy and security
According to a study run by Forrester Consulting, 80% of cybersecurity decision-makers feel that organizations require advanced cybersecurity defences to combat the real-world threats of offensive AI.
AI technology relies on data systems which we all understand are subject to security risks, and data breaches. According to Forbes, the unauthorized access and misuse of patient health data can have serious personal, ethical and legal consequences is a big area of concern across the industry.
⚠️ AI may adversely affect humans
There is a real concern that AI and healthcare may lead to actual patient injury and harm, through misdiagnosis or recommending the wrong medication or even failing to pick up a tumor on a scan. Of course, this risk exists now without AI, but there is an inherent fear that harm resulting from a software malfunction can quickly become a widespread, systemic and uncontrollable problem.
While these risks present the potential for disruption and harm to patients and medical professionals, they are also presenting countless opportunities for technology companies to find the right AI solutions in healthcare and implement them effectively.
The Future of AI in Healthcare
As we’ve presented, AI in healthcare is an extensive topic that demands constant exploration and discussion. As a rapidly evolving technology, AI will continue to transform healthcare and all industries that support it!
It is vital, then, that we continue to ask questions, reflect, and help craft the best solutions going forward.
Before we wrap up this first installment of AI in healthcare series, we want to present a glimpse of the future of AI in healthcare - where the technology is heading and what applications we could see.
While the healthcare industry currently relies on predictive models, generative ai in healthcare is presenting new possibilities to transform areas such as
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natural language generation,
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translation, and
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handling unstructured, and unlabeled data.
According to Deloitte, 75% of leading healthcare companies are already experimenting with or planning to scale Generative AI across the enterprise.
Google is already doing this, after recently announcing the launch of new generative search capabilities, which will help clinicians pull healthcare data from various sources.
Generative AI in healthcare and other more sophisticated algorithms are predicted to be the main advancement in the next 5-10 years, according to Bajwa.
These solutions will effectively manage unlabelled data and combine structured and unstructured data from various sources.
In addition, healthcare organizations will evolve to become co-innovators with technology partners like Alpha Solutions to develop their own novel AI systems.
In the longer term, smarter, and more precise AI technologies will become more advanced and prevalent despite being in operation in some form today.
We see AI being used in combination with augmented reality as a prime example of this. Already, this application is allowing medical professionals to see inside the human body while being able to process vast amounts of data and allow for real-time interpretation and diagnosis. Imagine where this technology will be in the next 10 or 15 years!
Another advanced and mind-blowing use of AI is explained by STAT, which highlights the development of AI algorithms and computer simulations poised to replace human
patients with virtual ones.
This application could result in a massive paradigm shift for healthcare as we know it. New therapies and interventions could be virtually trialled and perfected without ever exposing human patients to any risk.
In Conclusion
As we can see, the revolution of AI in healthcare is underway and presenting a wide range of questions, challenges, and opportunities for creative and innovative solutions.
Alpha Solutions can help you navigate several of these key AI and healthcare areas:
Defining AI in Healthcare - making sense of the key AI types and how these technologies are changing the healthcare and digital tech industries.
AI Applications in Healthcare Types - assessing the best AI applications in healthcare for your organization’s needs.
Benefits and Limitations of AI in Healthcare - selecting the best AI strategy for your project while minimizing risk and maximizing the benefits.
Ethics of AI in Healthcare - managing the ever-changing AI landscape to ensure your systems are compliant and used responsibly and ethically.
Future of AI in Healthcare - staying on top of the latest trends and developments of AI technologies and solutions used in healthcare.
AI in healthcare comes with advantages and, in turn, ethical risks. However, the field is rapidly developing, and we are prepared to help you navigate these critical issues and progress AI’s implementation in your environment.
While healthcare currently relies on predictive AI, there is a vast opportunity for generative AI and other applications to advance the industry and patient experience.
Join us as we delve into this new AI world and prepare for and deliver the best
technologies and strategies to support it.
Curious about how to implement the best AI solutions in healthcare for your institution or organization? Let’s have a chat about it.
