Year over year, artificial intelligence has been affecting more industries and areas of our private lives. Healthcare is no exception! With its soaring popularity, you might be wondering – is the hype around AI for medicine justified?

In this piece, we’ll begin by explaining the existing types of AI development services for medicine. Next, we’ll discuss the top benefits of AI in healthcare, mention the possible limitations, and how you can work around them. Finally, we’ll discuss the best way of getting started with AI for your healthcare project.

Before we proceed, let’s begin with answering the basic question:

What is artificial intelligence (AI)?

Broadly speaking, artificial intelligence is any task performed by a machine that would have previously been considered to require human intelligence, according to the fathers of the field, Minsky, and McCarthy, who came up with the term in the 1950s. 

It’s thought that a machine capable of human-like intelligence is yet to come into fruition, and the majority, if not all, cases of AI today are known as ‘narrow AI’. 

Narrow AI (Artificial Narrow Intelligence or ANI), sometimes referred to as ‘weak AI’, refers to any machine that can outperform humans in a defined and structured task. 

Respectively, General AI (Artificial General Intelligence or AGI) takes narrow applications to the next level and is where we are currently heading towards. While ANI is exceptional at running automated tasks, the objective of AGI is to create machines that can think in the context of humans, replicating the biological network of the brain. AGI can adapt to environments where ANI cannot. 

3 types of artificial intelligence: ani, agi, asi
Source: Mksaad 

Ultimately, the expectation is that one day we will reach artificial superintelligence (ASI) that can outperform humans in every field. That could take 10, 20, or 50 years, but AI experts are confident we will get there one day. 

Read also: What is the difference between artificial intelligence and machine learning?

How is artificial intelligence used in healthcare?

AI spending in healthcare is expected to be worth $36.1 billion by 2025, according to research by MarketsandMarkets. The huge potential for automation and efficiency across several end-users such as providers, hospitals, healthcare payers, pharmaceutical, and biotechnology companies makes the industry a prime investment opportunity. 

Improving models and algorithms, access to data, decreasing hardware costs, and better connectivity such as 5G opens the door to more ambitious AI solutions. The launch of 5G alone means machines can process vast amounts of data in real-time without the previous barrier of network reliability. 

In a life-critical industry like healthcare, such speed and reliability are pivotal to the future of AI. The COVID-19 pandemic has also highlighted how the healthcare industry needs to innovate, as incumbents struggle to handle the increased demand for its resources. 

Machine learning, computer vision, and natural language processing (all subsets of AI) can drive clinical decision-making for physicians and staff, as well as several other benefits. 

A professor and researcher at the University of Hawaii, John Shepherd, posted a paper in 2021 showing how deep learning AI technology can improve breast cancer risk prediction. The algorithms analyzed a dataset of 25,000 mammograms and were shown to improve the risk prediction for screening-detected breast cancer. AI algorithms can learn from far more extensive libraries than any radiologist, perhaps a million or more images, rather than relying on eight years of medical school training. Clinicians can focus on care rather than analyzing data.

When COVID-19 disrupted the world, AI was used as a tool to develop predictive models that can help minimize the spread of the pandemic. Immunologists used machine learning to make discoveries and create better vaccines. 

Startups such as Lark use conversational AI to help patients who are suffering from chronic diseases. The platform utilizes health data to monitor activity levels, sleep, and mindfulness, amongst other things.

Read also: Technology trends in business

AI in healthcare - how is artificial intelligence used in the medical fields?

The use of AI in healthcare – what are the main benefits?

Now that we’ve covered this brief introduction to AI for medicine above, let’s now take a look at its main benefits so that you can decide whether it’s something worth investing in.  

Increased efficiency of the diagnostic process

Increased diagnostic efficiency is one of the benefits of AI in healthcare. A lack of medical history and large caseloads can increase the chance of human errors in healthcare settings. AI algorithms can predict and diagnose diseases faster than clinicians with a minimal error threat in comparison to humans (assuming robust data quality, which we will talk about later). For example, a 2017 study shows that a deep learning AI model can diagnose breast cancer at a higher rate than 11 pathologists! 

PathAI improves patient outcomes through AI-Powered technology and partner collaboration to provide the most accurate diagnosis possible and efficient treatments.

A team at MIT developed a machine learning algorithm that can either make a decision or know when to defer to a human expert. With some conditions, such as cardiomegaly, they found a human-AI hybrid model performs 8% better than either could on their own. The research shows that AI cannot always necessarily replace humans at this stage, but it can augment processes to make them more efficient. 

Reduced overall costs of running the business

Using AI to make processes such as diagnosis more efficient can often be run at a fraction of the original cost. For example, if AI can analyze millions of images for signs of disease. It removes the costly manual work involved. Patients are treated faster and more effectively, reducing admissions, waiting times, and the need for beds. Healthcare IT News predicts significant cost savings in many different areas from AI automation. They rank the top five as below: 

  • Robot-assisted surgery – $40 billion
  • Virtual nursing assistants – $20 billion
  • Administrative workflow assistance – $18 billion 
  • Fraud detection – $17 billion
  • Dosage error reduction – $16 billion

As AI continues to learn, it will improve precision, accuracy, and efficiency, further driving down costs. 

Safer surgeries

AI is finding its place in healthcare robotics by providing efficient and unique assistance in surgery. Surgeons get an increased level of dexterity to operate in small spaces that might otherwise require open surgery. Robots can be more precise around sensitive organs and tissues, reduce blood loss, risk of infection, and post-surgery pain. Robotic surgery patients also report less scarring and shorter recovery times due to smaller incisions required. 

ai in healthcare applications example: robotic surgery
Source: Materprivate 

In 2017, the Maastricht University Medical Center in the Netherlands used an AI-assisted robot to suture small blood vessels, some no larger than .03 millimeters. The robot is controlled by a surgeon whose hand movements are converted into more precise actions that are performed by robot hands. AI can stabilize any tremors to ensure the robot moves correctly during the surgery. 

Enhanced patient care

Healthcare facilities are typically crowded and chaotic, making for a poor patient experience. In fact, a recent study shows that 83% of patients describe poor communication as the worst part of the patient experience. Leveraging AI can help rapidly scan through data, get reports, and direct patients where to go and who to see quickly, avoiding the usual confusion in healthcare environments. AI tech for patients also has another unbeatable advantage – it’s available 24/7.  

A great example of AI for medicine that helps improve the patient’s experience is Babylon, an app that functions as an interactive symptoms checker. The system asks questions, analyzes the answers, and assesses known symptoms and risk factors to provide informed up-to-date medical information.

Easy information sharing

Another benefit of AI in healthcare worth mentioning is easy information sharing. AI can track specific patient data more efficiently than traditional care, allowing more time for doctors to focus on treatments. The ability of algorithms to analyze vast quantities of information quickly is the key to fulfilling the potential of AI and precision medicine. 

According to the Centers for Disease Control and Prevention, 10.5% of the US population has diabetes. There is a desperate need to treat and manage the condition, and AI can help providers understand the disease through data. The FreeStyle Libre glucose monitoring system, for instance, allows diabetes sufferers to track glucose levels in real-time, and access reports to manage and review their progress with doctors or support teams. 

AI-powered FreeStyle glucose monitoring system
Source: Freestyle Abbott

Information from wearable devices can be an indicator of the probability of getting a specific illness or disease. As the industry leverages AI to collect, store, and analyze data, it could create a treasure chest of revolutionary information for healthcare.  

Better prevention care

AI and machine learning can assist with infectious disease prevention and management. The ability to handle vast amounts of data such as medical information, behavior patterns and environmental conditions means AI can be invaluable in preventing outbreaks such as COVID-19. 

The outbreak intelligence platform, Blue Dot, analyzed airline ticketing and flight paths to accurately predict the path of COVID-19 from Wuhan to Bangkok, Seoul, and Taipei. Similar AI-enabled systems can help doctors detect the spread of disease when patients enter a facility with a rapid diagnosis to enable effective isolation and quarantine procedures. 

Furthermore, AI can analyze billions of compounds for drug testing, condensing research that would typically take years into only a few weeks. Researchers can review the virus genomes alongside AI to develop vaccines quickly and prevent disease.  

While discussing illness prevention, it’s also worth mentioning how AI-powered wearables can help detect non-infectious diseases. Based on the user’s vitals, the device can detect the tell-tale signs of a serious health event. Then, it can motivate the user to see a specialist.

Read also: 5 medical challenges that can be solved with AI in healthcare

4 major challenges companies face while implementing AI for medicine

While there are indisputable benefits that speak in favor of the use of AI in healthcare, it’s important to recognize that these solutions also come with several challenges. See what they are! 

Ensuring patient data quality

We briefly touched on the importance of data quality for effective AI solutions earlier in this article. It’s a challenge that comes towards the top of the list in most industries. But is arguably more critical in healthcare where it is highly personal information and lives could be at risk. 

key hurdles to AI adoption
Source: Business Process Incubator 

For example, AI is being used for disease diagnosis and operates much faster than humans. However, the accuracy of results relies on the quality of training data. AI processes millions of data points to make a decision, but if the data it uses comes from unreliable or biased sources, the outcomes will be flawed. 96% of organizations say they are hindered by data-related issues when trying to drive AI success. 

In one example, an AI algorithm used healthcare costs as a proxy for health needs. The problem is that less money is spent on black patients with the same level of need under normal circumstances, and the algorithm concluded black patients were healthier than they were in reality. Such decisions could result in catastrophic decisions. 

The healthcare industry must ensure that AI data is collected from trusted sources and is diverse enough to reduce the impact of bias. Without doing so, that is a risk that AI could exacerbate inequality rather than promote efficiency. 

Lack of proper experts

Clinicians don’t tend to be AI experts. An AI system is designed to replicate the human brain, and it’s difficult, if not impossible for the standard user to understand how it arrives at a conclusion. This challenge is referred to as the “black box” problem. Namely, an input goes into the system, the output is received, but we don’t know what happens in the middle. 

In healthcare, a solution is needed to the black box problem. For example, if a doctor cannot explain why AI recommends a specific treatment or has come up with a particular diagnosis, it can put lives at risk. Therefore, with the current state of AI, it’s essential that these solutions are always used by experts, who can straight up notice a peculiar health recommendation and challenge it. The bottom line here is that, though immensely helpful, AI isn’t perfect – at least, not yet – and it still requires a human expert at the end of the process.

Another challenge is related to successful AI adoption in your business itself. Here, you also need proper experts who have experience in building AI-powered solutions and who have understanding of the healthcare industry. Data science is still a niche specialization.

Training users

A lack of staff and patient education in AI tools and how they can solve fundamental industry problems is a significant barrier to success. Let’s take predictive modelling, for one. Nowadays, AI can be used to forecast the probability of hundreds of outcomes – for example, the chance of severe COVID-19 symptoms among diabetes and obese patients. If your solution targets clinicians, then you can expect a softer learning curve than for users who aren’t accustomed to using software in their day-to-day work, such as medical staff or patients. 

Preparing patients for new procedures

When you look back to early-2020, when the pandemic hit, video doctor visits were met with some uncertainty. Patients didn’t understand how a doctor can take blood pressure or evaluate conditions efficiently if they were not face-to-face. However, in a recent survey, around 50% of Americans say they prefer healthcare professionals who offer phone or web-based consultations.  

Once patients understand that robotic surgery means a shorter hospital stay, less scarring, lower levels of blood loss, and a faster recovery, they might be more open to AI. As patients become accustomed to AI and the benefits, trust will come. 

Read also: Artificial intelligence implementation in 5 steps

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Potential cons of using AI in the healthcare industry

For all the benefits of the use of AI in healthcare, there are some potential disadvantages of its application. 

Losing the personal approach

AI will change the way that patients interact with healthcare providers. Physicians, nurses, and other clinicians care deeply about their patients. There is a concern that AI implementation will interfere with face-to-face time. There are already limited appointments that stop clinicians from picking up on their patients’ body and verbal cues. 

In an Accenture survey, 29% of patients who don’t want to use AI or virtual doctors say it is because they prefer to visit. 

There is a risk that AI could strip empathy out of the healthcare system. 

On the other hand though, if AI were to handle the diagnosis, this could leave doctors with more time to focus on interacting with patients rather than sift through medical documentation.

Wrong diagnosis

We have discussed the need for high-quality data in this article already. But if AI systems are not trained with enough data from diverse backgrounds, there is a significant risk of defective diagnosis. Unless AI is explainable, doctors are not experienced enough in AI to recognize a mistake. If there is an incorrect diagnosis, questions are then raised around accountability. For example, is the doctor who took the decision from AI culpable for the error? Deploying AI could raise different types of ethical conundrums for healthcare. 

In a 2017 HIMSS poll, a third of participants said they were reluctant to adopt AI due to its immaturity and inability to support reliable use across their organizations. 

However, it’s essential to understand that diagnoses provided by doctors and AI both come with a margin of error. According to a global study on primary care errors, 5% of all outpatients are given a wrong diagnosis by a professional. A third of all misdiagnoses among severely-ill patients leading to harm.

The threat of data loss

AI is dependent on data networks, and with that, systems are susceptible to security risks. Healthcare services will need to invest in cyber security to ensure the technology is safe and sustainable. The amount of personal data stored within healthcare systems makes it very enticing for cyber attacks. Moving gigabytes of data between disparate systems is new territory for healthcare organizations and takes substantial financial backing and planning. That’s why data security must be the highest priority in all AI development projects in the healthcare industry.

How can you start with AI?

Many businesses are contemplating the right time to move to AI-based solutions. A successful implementation starts from implementing the right strategy and tackling various challenges in implementing AI that we have discussed in this article. 

To adopt artificial intelligence successfully, you need to start smart. Namely, instead of taking on a large-scale, complex project, begin with a single-use case. Develop a proof of concept by using available data, and monitor and iterate your solution continuously. 

Read also: When to move to AI-based solutions?

Benefits of AI in healthcare – summary

While AI for medicine comes with a few challenges, such as ensuring good data quality and gaining AI expertise by staff, it creates huge potential for the industry. 

The use of AI in healthcare can provide tremendous benefits, from increased diagnosis efficiency, all the way to enhanced information sharing and better prevention care. The question isn’t whether it’s worth using AI in medicine, as it’s undoubtedly the future of healthcare. It’s whether you can afford to wait with implementing it.