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AI for healthcare has been automating the work of healthcare professionals and improving patient outcomes sinc the 1970s. In 2023, this exciting market is on the brink of a new chapter — currently valued at over $20 billion, it’s expected to 10x by 2030.
Read on to find out how AI is changing the future for medical organizations and patients, and how you can innovate your work in healthcare.
AI for healthcare is a term that describes a wide range of tools powered by artificial intelligence and designed specifically for the healthcare industry. It includes expert systems as well as advanced software that utilizes machine learning (ML) and natural language processing (NLP) technologies.
AI solutions for healthcare automate data analysis, streamline research, and improve the accuracy of diagnostics and treatment planning. When implemented correctly, they also bring down admin costs for healthcare organizations and make healthcare more affordable for patients.
AI has a relatively short history — the term “artificial intelligence” was coined in 1955, after just a few decades of early experiments featuring robots, driverless vehicles, and computers designed to play chess.
The first use of AI technology in healthcare dates back to 1972 and the development of MYCIN, an expert system designed to diagnose and treat blood infections. At the time, the system was considered as competent as human blood infection specialists and more competent than most general practitioners.
Over the following decades, AI developed to include natural language processing (NLP), a technology that allows humans and computers to interact using “natural language,” i.e. common vocabulary and conversational syntax rather than code or expert commands. NLP algorithms can understand and interpret questions, and generate useful (and comprehensible) answers in real-time.
While NLP technology is still being developed and perfected in 2023 (play around with Chat GPT to see for yourself), the first breakthrough in the field came in the early 2010s with the development of IBM Watson. The system, originally designed to answer Jeopardy questions, was quickly adopted for commercial use. By 2013, it was used as Watson for Oncology in lung cancer treatment at the Memorial Sloan Kettering Cancer Center in New York, where it automated diagnostics and matched patients with the best treatment plans.
So what’s the current state of AI for healthcare? The industry has made significant strides over the last decade, and its market value is expected to reach nearly $188 billion by 2030.
Modern AI technology is used for medical research, diagnostics, treatment planning, and much more. Most importantly, it’s making a positive impact on the industry across the board, helping healthcare organizations optimize processes and workflows, and facilitating better health outcomes for patients.
Keep reading to find out more about how the healthcare industry is benefiting from AI — and what the near future holds for AI for healthcare.
In this section, we’ll explain the specific AI technologies that are used in healthcare. These are general definitions that will help you understand the mechanics that power most AI tools. For specific healthcare applications, skip to the next section.
Machine learning is a category of AI science focused on developing “machines” (e.g. computers, software, medical devices, etc.) that learn from data.
Let’s start with the basics: How can machines “learn?”
Machine learning systems use artificial neural networks (ANNs) and simulated neural networks (SNNs) that mimic the structure and workings of the human brain.
AI neural networks can be “trained” to build comprehension by processing examples and establishing connections between specific data points. Functioning ML algorithms recognize patterns in the datasets they analyze and can form predictions or make decisions based on the information available to them.
This is true even for large, complex, and unstructured datasets. While ML technologies could originally only process text, deep learning (a subcategory of machine learning) allows modern AI algorithms to process images and even sounds.
Why is this groundbreaking?
Traditional systems (e.g. the operating systems of personal computers) only do what they are specifically programmed to. For example, clicking on a certain icon opens an app, a sequence of commands restarts the system, etc.
AI systems using ML technology, on the other hand, can produce an infinite number of outcomes (decisions or predictions) that have not been programmed by a human — and they do so with minimal intervention.
Popular healthcare applications of machine learning systems include:
We go into more detail on specific use cases in the next section.
Natural language processing is an AI technology that allows humans and “machines” (systems, devices, etc.) to interact using “natural language,” i.e. common vocabulary and conversational syntax rather than code or complicated commands. It brings together artificial intelligence, linguistics, and computer science. NLP algorithms can understand and interpret text, from surface-level meanings to nuanced contextual cues.
Natural language processing dates back to the 1950s, when the first systems were developed to automatically translate text. At the time, NLP systems had to be fed a collection of rules to produce outcomes — in the case of translating systems, these were textbooks, dictionaries, manually written guides including questions and answers, etc. This early technology is now called “symbolic NLP.”
Modern-day NLP algorithms utilize machine learning technologies, and can interpret even nuanced and unstructured text, including language that would be difficult to interpret using traditional dictionaries or textbooks.
NLP helps clinicians and healthcare organizations quickly and efficiently organize and analyze electronic health records, including doctors’ notes and every bit of information added to a patient file. The technology is also used to put the findings of machine learning algorithms (used for research, predictive analysis, etc.) into words and communicate them to healthcare professionals.
Keep reading to learn more about how natural language processing is used in healthcare.
Expert systems are AI-powered computer programs that mimic the decision-making abilities of an expert in a given field. MYCIN, the original AI system designed specifically for healthcare in the 1970s, is an example of an expert system. Elizabeth Holmes’ infamous Theranos Edison device, had it worked, would also be considered an expert system.
Expert systems need three key components to function:
With these inputs, expert systems can solve complex problems in real-time.
Popular healthcare applications of expert systems include:
Read on for more specific examples of expert systems in modern-day healthcare.
AI is already helping both healthcare professionals and patients — and its impact will only continue to grow as the technology advances.
Here are the ways that AI is currently used in healthcare.
We already mentioned that machine learning algorithms can access, analyze, and interpret EHRs at scale in real-time. Here’s how they can also streamline administrative processes related to data management:
One of the most important ways that AI is impacting patient care is the technology’s ability to improve the accuracy and timeliness of diagnoses. Here are specific examples of how AI models are used in diagnostics:
The benefits of AI in healthcare don’t only apply to individual patients — modern AI technologies also have a positive impact on public health.
During widespread epidemics, AI can help improve population health by empowering regulatory bodies to make informed decisions regarding public safety faster and speeding up the discovery of new treatments.
We all saw these processes at work during the COVID-19 pandemic. AI helped officials and scientists:
By automating the work of healthcare professionals and minimizing inefficiencies, AI can make healthcare more accessible and affordable.
The time savings brought about by AI automations can help get more people into doctors’ offices, which is particularly important in underserved areas — and simplified diagnostics result in lower average out-of-pocket costs for payers.
These benefits are not a futuristic dream. 12% of US healthcare executives surveyed by eMarketer in 2022 believe that the implementation of AI and ML technologies in healthcare is already “very effective” at improving financial outcomes, and 35% believe that it’s “often effective.”
AI’s ability to compute large datasets and automatically extract key insights helps scientists interpret raw data at scale. Artificial intelligence can also be used to model data and visualize research findings.
Streamlining processes and automating tasks typically performed by human healthcare workers is not just convenient — it can also save healthcare organizations a lot of money.
Research from Harvard’s School of Public Health estimates that at-scale adoption of AI technologies in healthcare could save the industry over $150 billion by 2025.
A significant chunk of these savings has to do with automating the work of professionals. According to a 2020 McKinsey report, 15% of all work hours across the healthcare industry are expected to be automated in the near future, even though only 35% of hours worked by healthcare professionals can be automated. The report names over 30 healthcare occupations that can expect significant time savings due to automation by 2030.
AI technology can streamline and accelerate drug discovery by processing large amounts of data from laboratory experiments and clinical trials, and formulating actionable insights faster than teams of scientists would be able to.
Across industries, AI can be used to improve cybersecurity by means of:
In regulated industries like healthcare, cybersecurity is particularly important. It helps protect sensitive patient information, but it also makes it easier for organizations to comply with data handling laws and regulations.
A future in which robots take over healthcare is unlikely. Today, even the most advanced AI models can’t replace a qualified clinician’s observations, instincts, and judgments. This means that the popularization of AI in healthcare won’t be an overhaul — it’s better to think about it as a revolution in how effective and precise (human) healthcare professionals can be in their jobs.
Start thinking about how you can use AI to improve your healthcare organization today. Becoming an early adopter will help you stay on top of industry trends and market demands, and become a leader in your niche.
AI will revolutionize healthcare. Make sure you work with data science experts to innovate your work. Contact us to find out how our team can help you implement AI in your processes.
Both patients and healthcare providers will benefit from AI technologies. Patients can expect faster and more reliable diagnoses, and easier, more affordable access to some healthcare services. Doctors and medical professionals will be able to automate repetitive tasks and get faster access to patient data and actionable insights.
The use of AI technologies in healthcare will, most importantly, result in streamlined and more reliable healthcare at a lower cost for patients — and easier data analysis for healthcare professionals.
There are potential threats to using AI in healthcare. The implementation of imperfect AI healthcare technologies could result in data analysis and treatment recommendation errors causing patient harm. Imperfect systems could also be vulnerable to hacking and data leaks leading to patient privacy violations.
When implementing AI solutions in your healthcare organization, work with professionals to avoid common risks and create the best experience for your staff and patients. At Tribe AI, we help companies apply machine learning to their business by connecting them with leading AI engineers and data scientists. Take risk out of the equation — book a free consultation today.
AI can be used in healthcare in many ways. The most popular uses include the consolidation and analysis of medical records, automated diagnostics, health risk analytics based on a patient’s medical history, and infection rate forecasting (used for seasonal illnesses like the flu and widespread epidemics like COVID-19).