The History of AI in Healthcare: Pioneering the Future of Medicine

When the typical person thinks about artificial intelligence (AI), their mind often conjures up images of flying robots and brain implants. However, what they may not realize is that AI has already been deeply embedded in our lives for many years, especially in the healthcare system.

To discover how AI has been saving lives all the way back since the 1950s, join us as we venture through time and shed light on how indispensable AI has become.

What was healthcare like before AI?

AI wasn’t always a part of healthcare, and before its implementation, things looked a lot different from how they do now. Let’s look at the reality of what health systems and professionals battled before AI.

Paper-based systems

In the past, healthcare relied on physical paperwork to keep track of patient records, test results, and medical histories. As you can imagine, this brought many challenges with keeping things organized and in good condition.

Also, handwriting like this didn’t make the job any easier!

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It also caused accessibility problems as each manual record had to be physically located amid hundreds of files and boxes. 

You couldn’t just refine by name, illness, or date. This also made sharing of personal information difficult between healthcare providers and hospitals. It took lots of time, and it was common for some to get lost or damaged in the process.

Another element of using paper-based systems that posed problems for healthcare organizations was the physical space it demanded. Therefore, overall efficient access to patient data was seriously lacking compared to now.

Reactive approach

Before the age of AI, healthcare systems had to use a reactive approach, meaning that they focused on treating illnesses and diseases instead of preventing them. Thus ‘reacting’ to the situation. 

They still had a thorough understanding of how best to treat certain ailments, but they did not have the resources or knowledge required to be one step ahead.

As well as this, they didn’t see patients as individual cases. Instead, they expected conditions to affect every person in the same way. Despite having guidelines as a framework for diagnosing and treating conditions, they preferred to rely on their strict treatment guidelines instead of examining each case carefully to come up with the best way forward.

This reactive approach often led to conditions having delayed treatment or going unnoticed altogether. Unfortunately, this cost many lives. As a result, doctors and researchers soon realized that the human body is not as straightforward as once thought.

With this in mind, a proactive approach was soon adopted with the help of AI to leverage data and advanced algorithms to identify patterns, predict risks, and enable quick intervention before it was too late.

Lack of integration

In the past, healthcare facilities, specialists, and providers weren’t great at communicating and collaborating because it was just too hard. A disconnect between professionals made the transfer of important information and collaboration time-consuming, expensive, and overall difficult.

Without integrated systems, healthcare professionals could not make informed decisions and provide comprehensive patient care. They also couldn’t update their patient’s information efficiently and within an accessible database for other specialists to access.

Errors, misunderstandings, and delays were all caused by this disjointed approach and made it almost impossible for healthcare facilities to follow the same treatment practice and methodology. This resulted in inefficacies and inconsistencies, costing money, time, and lives. 

However, when AI came into the picture, electronic health records became the norm and enabled seamless sharing and input of patient information across all healthcare facilities and providers. As well as this, data could be updated in real-time, allowing time-sensitive cases to stay on track and facilitate teamwork and effective communication.

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All in all, by linking with integrated AI-powered systems, patient care is more coordinated, and transitions are much smoother between treatment methods and specialists. Data is safely stored and quickly sent to those who need it without clunky, slow technology slowing them down and putting someone’s life in danger.

Time-consuming research

Healthcare research was time-consuming and used a lot of resources for collecting, analyzing, and interpreting findings; this process was slow and inefficient.

Because of this, medical advancements and clinical practices had lackluster breakthroughs. Researchers had to dedicate lots of time and effort to gathering and processing data manually, taking them away from more pressing situations. 

The struggle didn’t stop there; once they collected the data, it was time to analyze it. Sure, they had computers to crunch the statistics, but they were slow and not optimized for the task. Also, those who ran the numbers through such informatics machines needed to be specialists as it was such a niche, advanced position. 

Understanding the data that the computer displayed was also another obstacle. It wasn’t as straightforward as it is now regarding readability and accessibility, leaving it to experienced researchers to interpret the findings.

Once the data was collected, analyzing it using conventional statistical methods was time-consuming, requiring specialized skills and extensive computations. Interpreting research findings also took considerable time, as researchers needed to analyze the data and draw meaningful conclusions carefully. 

This research process was long and arduous, creating delays in new interventions, treatments, and overall medical breakthroughs. 

However, with the introduction of our good friend AI, there is now the ability to speed up this undertaking. This is thanks to automated data collection that was otherwise done manually and the reduction of required resources. 

Machine learning accelerated this possibility by quickly analyzing big datasets to find patterns and correlations that humans would have normally missed. 

1956: AI’s first step into healthcare

AI’s journey in healthcare began in 1956. Once healthcare researchers and visionaries saw the potential, the rest was history. Prior to this, the thought of incorporating such advanced, abstract technology into healthcare was an aspiration more than anything else.

What got the ball rolling was Allen Newell and Herbert A. Simon, two pioneers in AI who proved it could be done with the concept of ‘problem-solving’ machines. 

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They stated these systems could mimic human intelligence in ways that computers couldn’t before, capturing the attention of healthcare board members and giving them an opportunity they couldn’t refuse.

During this period, AI pioneers like Allen Newell and Herbert A. Simon laid the foundation for what would become a transformative field. 

1966: Early applications of AI in medical diagnosis

In 1966, AI made another breakthrough in healthcare by helping doctors with disease diagnosis more efficiently and accurately. It didn’t take rocket science, just a simple model including big data of thousands of diseases, symptoms, and cases. 

This means that when an individual’s medical data is inputted into the computer system, it can instantly analyze and compare it to previous records, thus providing a potential diagnosis. By applying this data science, a lot of pressure was taken off of medical professionals, enabling them to get started with the right treatment plan.

Compared to modern AI systems, the ones in 1966 were far from perfect, but at the time created a huge impact in demonstrating how this technology can help analyze data and produce accurate results. Without this foundation, it’s hard to imagine how the journey of AI in healthcare would’ve changed.

1980: Expert systems and decision support

As well as sweatbands, 1980 was the year of expert systems and decision-support tools, a huge breakthrough in healthcare AI.

Computer programs such as rule-based systems that replicated human knowledge, expertise, and decision-making were created to make important information accessible to all that needed it. This technology also lessened the hesitation that came from hypothetical diagnoses. 

Essentially, expert systems are made up of powerful software and databases full of expert knowledge on any given subject, such as treatment, diseases, and other health-related research. This information is stored digitally and helps other professionals know what to do in certain circumstances. 

Expert systems save lots of time when it comes to problem-solving, as all the information you could need to come to a conclusion is right there from the best minds in the industry.

1997: Deep Blue and AI in medical imaging

In 1997, IBM released a supercomputer known as Deep Blue, which changed medical imaging forever – by playing a game of chess.

Deep Blue was pitted against chess champion Garry Kasparov and made headlines after defeating him. It proved that you didn’t have to be human to be great at chess and that AI had incredible pattern recognition abilities.

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This breakthrough enthused researchers and healthcare innovators, and began applying the same deep learning to medical imaging. With the added help of AI algorithms, radiology practices such as X-rays, MRIs, and CT scans have become more accurate. 

As well as producing more accurate diagnoses, medical imaging improved by processing large amounts of data by identifying patterns and anomalies humans would not have seen. To this day, AI algorithms such as this are invaluable to radiologists in diagnosing conditions and establishing effective treatment initiatives.

2005: Machine learning and predictive analytics

It wasn’t until 2005, eight years after the previous breakthrough, where AI proved its capabilities once again. This was the dawn of machine learning and predictive analytics, an area still evolving today.

Machine learning allows computers to learn from data given to them or collected by themselves to make predictions and decisions without close supervision. This is done with the help of artificial neural networks, a subset of AI.

Sure, computers could make predictions before this, but only with close guidance and training. So, when it was discovered that computers could do it themselves, it blew minds.

Machine learning algorithms can also anticipate risks, predict outcomes, and guide decision-making processes. Paving the way for a more personalized, proactive healthcare experience. 

2011: IBM Watson’s Jeopardy!

If you aren’t aware, ‘Jeopardy!’ is a popular American quiz show that was a platform for another huge AI milestone. IBM’s AI system called Watson was featured on the show and demonstrated incredible human language comprehension and efficient processing of large datasets.

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Watson was up against two other contestants, both very intelligent humans and up for the challenge. What blew people away was that Watson wasn’t answering with copy-and-paste replies stored in his database; he actually understood what was being said in real-time.

Sure, a trivia show is not quite the same as a hospital. However, Watson still did something revolutionary: giving AI innovators and pioneers confidence that the same technology could be used in healthcare.

Data-heavy documents such as patient data and medical research papers could now be analyzed and understood quickly due to their skills in understanding human language. This was an area that AI was behind in compared to its other strengths. 

Watson’s claim to fame on ‘Jeopardy!’ was a sure sign that AI could now process and understand natural human language and was only the start of where this ability would take us, in and outside of healthcare.

2016: Breakthroughs in natural language processing (NLP)

Five years after Watson’s breakthrough, AI got a firmer grip on the human language, and thus, a new branch was born – natural language processing (NLP). This is how systems can fully understand human language, including semantics, syntax, and intent.

This created a refreshing interaction method with computers as before, we had to talk in their language, but now the tables had turned. But what did this mean for healthcare? 

Here are some more specific ways in which NLP was involved.

Analyze medical documents

Due to medical documents using natural human language, it became much easier to pick out specific segments of information in just seconds. NLP also allowed AI to fully understand and analyze clinical notes, research papers, and patient records.

With an AI assistant on your side who understood where you were coming from, finding information, patterns, and insights was a lot easier.

Medical record organization

NLP also streamlined the medical documentation process by automatically transcribing and summarizing conversations and notes, taking a lot of pressure off busy healthcare providers and allowing them to spend more time with patients.

AI systems could now recognize key medical terms, which created a well-organized web of information. So now, whatever specific record you needed could be found instantly, as well as other relevant information that may have been overlooked beforehand.

As well as this NLP allowed for electronic medical records to be converted into a standardized format. This meant that whatever system a facility was using would understand and display the information uniformly, thus mitigating errors and delays.

Virtual assistants and chatbots

It was also becoming normalized for patients to interact with virtual assistants as they became increasingly more in tune with unique needs and appropriate responses. These AI-driven conversational agents were used to provide helpful information, understand patient questions, and offer basic medical advice.

This lifted a lot of burden from healthcare receptionists, nurses, and doctors as they were no longer bogged down with simple questions a bot can now answer instantly.

It also greatly improved personal care by reassuring patients their concerns were being heard no matter the time of day. These virtual assistants led to further innovation of conversational AI and have now become naturally integrated with our everyday lives.

2018: AI in precision medicine and genomics

As we head into 2018, healthcare saw big strides in AI through precision medicine and genomics.

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Precision medicine focuses on making medical treatments and interventions fit individual patients’ needs more precisely than ever before. It does this by considering their lifestyle, genetic makeup, and environmental factors to give them the best chance at recovering or managing their condition.

In terms of genomics, diseases and health conditions were traced back to an individual’s genes and their function to better understand what caused the illness. 

By looking into this data, AI systems can identify genetic elements associated with specific conditions to help diagnose and prevent them in other patients. It even opened the door for personalized medicine to customize treatments for each medical case. 

DNA analysis

Genetics and DNA are complex areas with more questions than answers that took a long time to get to the bottom of. However, with genomics, DNA sequences could now be researched and analyzed. Old, traditional methods were greedy with time and resources and prone to uncover lackluster results. 

AI also played a crucial role in genomics research by accelerating the analysis of DNA sequences. This took research to new levels and led to discoveries about genetic diseases that would not have been possible, especially when we compare it to the conventional methods that ate up limited time and resources.

2020: AI and COVID-19 response in healthcare

Every aspect of the healthcare system was challenged when the COVID-19 pandemic struck in 2020. However, without AI, we would likely not have come out the other side as strong as we did.

AI technology played a critical role in various aspects of the pandemic response worldwide, including:

• Diagnosis
• Treatment
• Monitoring
• Drug discovery
• Public health management

Here we will look at the areas in which AI helped us through COVID.

Efficient, accurate COVID-19 diagnosis

One of the most important ways that AI helped us was the quick, accurate diagnosis of COVID cases. It was important to catch the virus so that isolation could begin before it spread to others. Since COVID is a respiratory condition, medical imaging was at the forefront of detecting COVID-19-related abnormalities in the lungs and brain.

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COVID-19 virus management

Once COVID was diagnosed, it needed to be managed as no treatment was available due to it being such a new illness. Machine learning helped healthcare professionals monitor and predict the spread of the virus by analyzing case numbers, demographics, and mobility patterns.

Using AI to identify high-risk areas and predict future outbreaks allowed emergency services to stay one step ahead and allocate resources effectively. A recent study found that machine learning could prevent COVID from coming back ever again.

COVID-19 treatment discovery

AI algorithms analyzed existing drug databases to predict their effectiveness against COVID. This cut down a lot of time on the search for new treatments and enabled researchers to repurpose existing drugs and save countless lives.

Specialized COVID-19 chatbots

Finally, since the COVID-19 pandemic was such an unsettling time for the public, AI-powered chatbots were deployed to provide up-to-date, accurate information about what was going on. 

They also had the power to answer COVID-related questions, provide guidance on symptoms, and recommend where to seek medical help. This helped the public feel informed and supported and took the burden off the already thinly stretched healthcare professionals and hotlines.

2022: Robotics and automation in surgical procedures

2022 created something we thought only belonged in science fiction, robotic surgical procedures. This advancement in technology made space for robots in the operating room, making the surgery more efficient and precise as every movement was carefully calculated. 

Robotic-assisted surgery

Robots were created to be an extension of the surgeon’s hands so that each and every movement could be scaled to the exact precision. 

It doesn’t matter how many years of experience you have as a surgeon; your hands will never be as steady as a robot’s. These inventions minimize human error, leading to better surgery results and shorter recovery times.

The ‘ da Vinci surgical system ‘ is a prime example of these specialized robots. It has four arms, each equipped with a high-definition camera and surgical instruments, all controlled by the surgeon from a console in the same room.

Not just anyone can control these robots, though; extensive, specialized training is required beforehand. Remember, a tool is only as effective as the hands that wield it.

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Surgical automation

At this time, AI automation made surgical procedures more streamlined. Suturing, stapling, and tissue manipulation were among the features that became automated, which increased efficiency, accuracy, and consistency.

Safer surgeries

As robots were more precise and smaller than human hands, surgeries became much less invasive and, therefore, safer. Surgeons could perform complex procedures even through the tiniest incision, resulting in minimal scars, less pain, and faster recovery rates.

2023: AI-powered virtual assistants in healthcare

As 2023 came around, we saw more and more patients and clinicians use virtual assistants to access medical information. Powered by AI, these chatbots provide personalized support and reliable medical advice.

Even though it was released in 2021, a great example of a specialized health bot is Microsoft’s ‘Azure’, which supports integration with other platforms for users to access their health records and test results easily. It also had service feedback and conversational features.

These chatbots are especially prevalent in mental health services to give patients somewhere to work through problems and receive relevant, helpful replies. Using NLP, these virtual assistants can manage symptoms of mental health issues such as anxiety and depression. 

Professional, experienced psychologists develop bots such as these to ensure that only accurate information is used. Of course, this doesn’t replace the real thing, but it helps those in a crisis who need a quick helping hand. 

Seamless integration

AI-powered virtual assistants became readily available on various devices, including smartphones and smart speakers such as Amazon’s Alexa, offering convenient and immediate access to healthcare information. 

Simply ask questions or describe your symptoms; the virtual assistant will provide relevant information and advice on what you should do next. This allowed people to address health concerns and make informed clinical decisions quickly.

Remote healthcare monitoring

As well as providing valuable information, AI-powered virtual assistants also play an important part in remote monitoring and healthcare management. They can collect and analyze personal health data by connecting with wearable devices like fitness trackers or smartwatches. 

Depending on the device, vital signs, activity levels, and sleep patterns are monitored and analyzed to offer insights into the user’s health status. From here, recommendations on how to lead a healthier lifestyle are given. 

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Keeping the public in tip-top shape prevents many health conditions like diabetes and heart disease, which lead to emergency care visits and lots of healthcare resources.

Scheduling and task management

With the implementation of virtual assistants, making appointments is more straightforward than ever. Streamlined administrative tasks such as these allowed healthcare staff to focus on other areas. 

As well as scheduling appointments, medical devices can send reminders, request prescription refills, and open communication opportunities between clinicians and their patients.

Future: AI’s next healthcare breakthroughs

Now that we have looked at the history of AI in healthcare, what can we learn from it to help predict where it will go next? 

We know that AI is set to transform healthcare even further and lead to even better efficiency, accuracy, and predictive abilities. 

Let’s look at what AI technology seems to be emerging in the healthcare industry as we speak.

Driverless ambulances

We have driverless cars, so it makes sense that we will soon see ambulances with no one at the wheel. By applying what we know about the nature of AI, driverless ambulances would be faster at responding to emergencies while navigating safely through traffic using the most efficient routes. 

Arriving at the scene promptly is paramount to ambulance services, and AI is a surefire way to achieve it. 

As well as this, driverless ambulances would be able to collect and transmit patient information in real-time, allowing remote medical professionals to monitor the crisis and provide insight throughout the process.

If this were to become a reality, health services would be a lot less strained, and those who call for an ambulance can have peace of mind they won’t have to wait long.

In 2021, Volkswagen showcased its autonomous ambulance prototype with a whole interior made of recycled materials such as fishing nets and industrial waste. 

AI-enabled prosthetics

Prosthetics have been around for thousands of years, but ones that utilize AI are a pretty new concept. It would improve mobility and functionality with AI algorithms enhancing specific controls in a natural, human way. 

One of the best things about AI-enabled prosthetics would be harnessing sensory feedback mechanisms, which is already established technology but not yet fully optimized for prosthetics.

Sensory feedback means that if someone were to pick up a ball, they would feel the weight and texture as if they were using their real hand. This not only improves engagement with the world around you but also improves tactile movement. 

Faster drug development

Finally, we can predict that drug development will become much more efficient and save companies up to 70% of their pharma costs. 

Vast amounts of biomedical data, such as genetic information and patient outcomes from clinical trials, would be analyzed with AI algorithms to enable early drug effectiveness prediction.

As well as this, drug development can be sped up by identifying what existing drugs can be repurposed for other treatments. This could be done through several ways with AI, but most likely with virtual screening, which would involve certain drug interactions being simulated, leading to better health outcomes. 

This mitigates a lot of the risk involved with medicine prescription and the time and money that goes into researching new drugs. 

AI’s impact on healthcare

From our journey in this article, you can see that the revolution of AI in healthcare is nothing short of groundbreaking. The industry began with old-fashioned paper systems and reactive approaches, but now we are in the dawn of driverless ambulances and surgical robots.

Yes, we’ve made progress, but there’s still much more to discover. AI has proven its ability to change the way we live our lives forever, and the more open we are to it, the stronger our healthcare system will become.

Were you aware of how far back AI went in healthcare? To learn more about how AI shapes our world, check out our Top Apps blog.