Artificial Intelligence in the Healthcare Industry: A Statistical Analysis

Did you know that artificial intelligence in healthcare market was valued at $11 billion in 2021? The figure is projected to increase to $187 billion by 2030, suggesting that we are likely to see more changes in the healthcare sector via artificial intelligence.

Artificial intelligence has the potential to improve healthcare services and address the challenges set out above.

In simple terms, AI is capable of delivering better outcomes and improving the overall care delivery productivity. Likewise, AI simplifies healthcare practitioners' day-to-day tasks so they can spend more time looking after patients. Lastly, AI can help in bringing life-saving treatments to market faster.

Despite these benefits, questions have been raised about the impact of AI on health systems, patients, practitioners, its potential risks, and more.

Keep reading to understand how AI and the data that underpins it must be used.

What is Artificial Intelligence in Healthcare?

AI and ML in healthcare are robust technologies that are known to provide data-driven support to medical professionals. These technologies use data to successfully identify patterns and deliver insights, which helps with common applications like treatment design, managing medical records, and healthcare monitoring.

With advantages, AI brings in certain disadvantages in terms of financial and clinical implications. To explore both sides of the debate, we have put together a list of topics that will help you understand the impact of AI in the healthcare industry.

Artificial Intelligence in Healthcare Market Size

It is true to say that the use of AI in healthcare industry is gaining momentum. It can effectively bridge the gap between healthcare professionals and patients. This is why AI-based healthcare solutions have received some of the biggest investments in recent years.

Here’s a brief overview of the market size of AI in the healthcare sector:

Advantages of AI in the Healthcare Industry

Applications of AI in the healthcare industry can significantly change the way treatment is offered and can cause human errors. But there’s more to the story.

Scroll down to check some more benefits of AI in healthcare industry:

1. Improved Accessibility

Did you know that most developing countries that fail to keep up with global technological advancements don’t have access to standard healthcare systems? The inhabitants of such countries are at a higher risk of drying.

As per WHO, the 18.1-year gap in life expectancy is due to poor or zero healthcare accessibility. This gap has been recorded between the richest and poorest countries. Hopefully, with the emergence of AI in healthcare, this issue is getting resolved.

AI-integrated healthcare systems can facilitate better treatment and diagnosis. Healthcare professionals can use dedicated apps to simplify their work and render necessary assistance.

2. Easy Information Sharing

AI brings in an additional advantage of easy information sharing in healthcare. Algorithms and data can be used to fully unlock the potential of AI and precision medicine.

With AI in healthcare, practitioners can quickly locate particular patient data, which gives them more time to focus on treatments and medications.

Medical conditions that require immediate assistance can benefit from AI. For instance, people with diabetes can use a glucose broadcasting system to monitor their glucose levels in real time. They can also discuss and manage their health via support groups.

3. Early Diagnosis

AI-integrated tools use patient data to assess their past and present health issues. Healthcare professionals can use these tools to compare disease details and to diagnose more accurately.

What’s more?

Well, patient data in healthcare mobile apps can also successfully predict any potential health issue that an individual might face in the future. With such tools at their disposal, healthcare practitioners can take the right step now and mitigate possible future threats.

Companies with less budget should initially opt for one platform. Later, they can shift to multiple platforms.

4. Increase Speed, Reduce Cost

AI algorithms have made healthcare processes faster and better while reducing their cost. From detailed examination to successful diagnosis, AI is a true game changer.

For example, AI can be used to identify biomarkers that recognize disease in our bodies. AI-based data and algorithms have drastically reduced the manual work required to specify these biomarkers. It means that now healthcare professionals can act fast and save more lives.

Compared to traditional methods, AI algorithms are more cost-effective and provide prediction-based results. This has significantly increased AI implementation globally, with almost 88% of organizations making the transition.

5. Improved Care for Patients

Crowded and chaotic healthcare facilities can reduce the patient care experience. However, AI allows patients to quickly navigate through data and obtain reports. A survey has revealed that almost 83% of patients’ experience gets hampered due to poor communication. Not to mention, AI in healthcare has made care round-the-clock available for patients.

6. Efficient Assistance in Surgery

Similar to ML’s implementation in surgery, AI has taken a huge leap in robotic applications. Dedicated AI Surgical Systems have been designed to execute possible movements accurately.

It means doctors can perform complex operations while reducing the risk of blood loss and pain without much hassle. Also, post-surgery recovery becomes faster.

Patients who have to undergo operations are subjected to Antibacterial Nanorobots to eliminate any infection in their blood. AI-backed information provides surgeons with patients’ present situation, which ends doubts, especially regarding surgery.

7. Enhanced Mental Support

Did you know that robots can assist patients alongside medical staff?

For instance, exoskeleton robots are known for helping paralyzed people regain their mobility with little to no help from caretakers. Similarly, AI-backed prostheses come with sensors, which serve as more helpful limbs.

It can be said that service robots from ML can effectively handle daily tasks and offer better care to patients. Besides the service-based robots, there are conversational robots as well that carry out required tests and checks.

Then some robots are designed and developed to help depressed patients. Their in-built capabilities help them correctly analyze patient’s moods and help them feel more positive.

Challenges Associated with AI Implementation in Healthcare

Here are certain disadvantages associated with AI implementation in healthcare:

1. Data Digitization and Consolidation

Without incorporating a massive amount of data into the AI systems, it becomes difficult to get accurate results. That’s why it is necessary to source high-quality healthcare data. However, the unauthorized and fragmented healthcare data across different systems creates an issue. It happens because patients frequently change their healthcare provider.

Digitalizing these data is even more challenging, especially in countries where siloed data systems are used. With updated digital record systems, better accuracy and efficiency can be achieved in the healthcare sector. This is the main reason healthcare stakeholders need to merge relevant data.

2. Inadequate Expertise

Indeed, all healthcare professionals do not possess AI expertise. However, they must gain a proper understanding to unlock the full potential of AI in healthcare. This issue is referred to as the “black box,” which needs to be resolved in healthcare.

Although AI systems receive and process input, medical practitioners need to know what occurs in the middle. To understand this thing, let’s check an example.

Suppose a doctor is using AI but they are not able to diagnose why it is recommending a particular treatment. In such a situation, patients' lives are put at risk. Medical professionals need to gain adequate expertise to mark up a particular well-being proposal and even oppose it.

3. Updating Regulations

The medical sector around the world has strict privacy laws, which need to be followed. These protection rules also state that sharing data among AI systems is a violation. However, in places where it is not illegal, patients can provide their consent to obtain medical data and use it for such purposes.

In this overall scenario, it is essential to address the logistical challenge that emerges in between. One such issue is the unavailability of flexible rules on medical data acquisition with identity protection.

Medical institutions can resolve this issue by ensuring compliance with such rules. They can also be accountable for patient data collection and utilization.

4. Human Interventions

Another challenge is human intervention, which emerges in the form of doubts about AI from medical professionals and patients.

In the current situation, patients are not ready for machines to handle their health concerns. Likewise, radiologists don’t want robots to take over their jobs.

5. Preparing Patients for New Models

In the last few years, video doctor appointments have become a common way of availing healthcare consultations. But for a new healthcare model to become popular and successful, patients need to be made aware of it.

For instance, patients should be prepared to get evaluated by doctors without having to see them in person. Also, when patients would get to know that robot-based surgery results in less scarring and quicker recovery, they might be open to trying it.

In simple words, patients will only trust AI and its applications if they become familiar with its benefits. Without it, building an AI-driven healthcare system won’t be possible. Remember that AI is designed to assist medical professionals in executing diagnoses.

Role of AI in Improving the Future of Medicine

From robot-assisted surgeries to faster and more accurate diagnoses, AI is playing a major role in improving the future of healthcare.

Below are a few examples, highlighting AI’s capabilities in medicine and healthcare.

1. AI in Medical Diagnosis

Did you know that each year, almost 400,000 hospitalized suffer some type of preventable harm, with almost 100,000 deaths?

It is where AI’s promise to improve the diagnostic process appears as one of the most exciting healthcare applications. Large caseloads and incomplete medical histories can indeed result in serious human errors. But with AI, these errors can be easily fixed. Also, AI can quickly and more effectively diagnose disease.

Artificial intelligence in the diagnostics market size is expected to grow at a CAGR of 24.60% from 2023 to 2030. With the healthcare industry in the USA rapidly adopting AI technology, it is possible to see more AI-powered solutions in various verticals to achieve better clinical outcomes.

2. AI in Drug Discovery

The drug development industry is not making any significant progress due to the high development costs and research, which require massive amounts of human hours.

Each drug that is being tested through clinical trials costs an average of $1.3 billion, and only 10% of them are successfully brought into the market. Thanks to the advanced AI technologies that have helped biopharmaceutical companies to perform drug discovery with more efficiency and accuracy.

The market size of AI in drug discovery in the USA is expected to grow at a CAGR of 29.6% from 2023 to 2030. The main reason for this growth is the rise in demand for the discovery and development of novel drug therapies and increasing manufacturing capacities.

3. AI Transforming Patient Experience

Time plays an essential role in the healthcare market, which is why hospitals and clinics need to offer seamless medical care to patients. Also, hospitals that can offer a positive experience to their patients have higher profits than hospitals with negative reviews.

4. AI for Healthcare Data Management

Healthcare has emerged as one of the next data frontiers to tame because sensitive and high-value data sometimes gets lost. This makes connecting to important data at the right time impossible, which further slows down the new drug development process.

That’s why many healthcare practitioners have started using AI for better healthcare data management. Advanced AI technology breaks down the silos and connects in minutes, which usually takes years to process.

5. AI in Robotic Surgery

With the popularity of robot-assisted surgery skyrocketing, hospitals have started using them for everything. It has even been revealed that robots can help doctors perform even the most complex surgery with great precision.

Robots provide doctors with a 3D and magnified view of the surgical site. Surgeons use this information to lead other team members who closely work with the robot throughout the operation.

As robot-led surgeries result in less scarring, fewer complications, minimal pain, and quicker recovery, the AI-based surgical robot market in the USA has grown tremendously. The market is expected to expand at a CAGR of 18.9% from 2023 to 2030.

6. AI Supporting Medical Imaging Analysis

AI helps in medical imaging analysis and scans, which allows radiologists to identify and prioritize critical cases. It further helps avoid potential errors and establish a more precise diagnosis.

Ideally, a clinical study requires checking huge amounts of data and images. Powerful AI algorithms can successfully analyze datasets at a tremendous speed. It can also efficiently compare them to identify patterns, which helps medical professionals track crucial information more quickly.

7. AI Decreasing Medicine Development Costs

Supercomputers have been used to forecast which possible medications would and would not be effective for treating different ailments based on databases of molecular structures.

By leveraging convolutional neural networks, AtomNet was able to predict the binding of tiny chemicals to proteins. The system was further used to analyze and interpret clues from thousands of protein structures and millions of experimental observations.

Also, convolutional neural networks used this method to find a safe and promising drug candidate from the database that was examined, which decreased the expense of creating new medications.

8. AI Forecast Kidney Disease

While acute kidney injury (AKI) might be hard for doctors to diagnose, it is known to worsen patients' conditions and even endanger their lives. Early diagnosis and treatment of AKI can significantly lower the need for lifelong care and the cost of kidney dialysis. However, doctors fail to detect and treat patients at the right time, which causes 11% of hospital deaths.

For this reason, medical units need to implement ML tools to assist physicians in making treatment decisions that can enhance the quality of life for veterans with AKI.

Three Phases of Scaling AI in Healthcare

Indeed, our knowledge of AI and its full potential in the healthcare industry is quite limited. Especially when it comes to the role of artificial intelligence in healthcare personalization.

However, as per studies, it is estimated that there will be three phases of scaling AI in healthcare:

First Phase

To optimize healthcare operations and boost adoption, solutions need to focus on the low-hanging fruits such as repetitive, routine, and administrative procedures that take up a lot of time for doctors. This stage also includes AI imaging-based technologies, which are now in use in fields like radiology, pathology, and ophthalmology.

Second Phase

In the second phase, we can expect more AI-based solutions to facilitate the transition from hospital-based to home-based care, such as virtual assistants, AI-powered alerting systems, and remote monitoring.

In addition, this phase is likely to see greater applications of NLP solutions in the home and hospital environments. Likewise, the application of AI in oncology, cardiology, and neurology, will increase. It will result in advanced AI integration into clinical procedures through active participation from healthcare providers.

Technological developments such as those in deep learning, NLP, and connectivity along with organizational culture shifts and competence building will drive this increased use of AI.

Third Phase

Based on data from clinical trials, we can see more AI solutions in clinical practice. Focus on improving enhanced and scaled clinical decision-support (CDS) tools will also increase.

In the end, we can expect artificial intelligence to play a crucial role in the healthcare value chain, influencing everything from how we learn, research & provide care to how we enhance population health.

Here are some preconditions for AI to deliver its full potential in European Healthcare:

How Can AI Change the Healthcare Workforce?

MGI has researched the impact of AI and automation on employment in the future. The study suggests that most jobs across all industries will be impacted by automation to varying degrees.

Healthcare is one of the industries with the least amount of potential for automation. Only 35% of the time is automatable and it varies depending on the type of healthcare job. The possibility of acceptance differs from the potential for automation.

This study used a midpoint where it was estimated that 15% of current healthcare labor hours can be automated in the future. It is also estimated that the percentage of hours worked in certain European countries can be reduced by automation by 2030 for a wide range of healthcare jobs. It also eliminates the possibility of additional disruption from other elements, such as customization.

Below are the areas of impact for AI in healthcare:

Ethical Issues for AI in Healthcare

While artificial intelligence is ethically neutral, the very nature of AI algorithms and the fact that they are man-made result in biases. It can have a negative impact on patient care.

One of the most important factors to take into account when comparing the benefits and drawbacks of AI is ethics. By continuously evaluating how well AI is performing in healthcare, ethical issues can be resolved. Also, giving regular feedback can help AI to learn and adapt to new data.

Common AI Mistakes in Healthcare

Typical AI errors in the healthcare industry are no different from the issues resulting from human error.

For instance, AI-driven medical equipment with limited comprehension of speech recognition for various languages and accents can wrongly diagnose a patient or fail to identify a tumor.

Remember that AI is a creation of humans. And if you want to maximize its potential in the healthcare industry, you must constantly supervise it and provide it feedback.

This is also related to the discussion surrounding social prescribing, which is growing in popularity within the NHS. Social prescribing is the process by which medical professionals direct their patients requiring general health assistance to community-based support programs.

Social prescribing leads to positive outcomes. However, if done incorrectly, it can have several negative effects. That’s why healthcare practitioners should use caution when attempting to integrate AI with social prescribing. This strategy demands soft skills to help connect with the patient in the best way and to assist them with non-medical resources.

How Can Protonshub Technologies Help You in Your AI in Healthcare Journey?

The application of AI in medicine faces several challenges in terms of ensuring high-quality data and building internal AI expertise. However, the benefits of integrating AI in the healthcare industry are substantial. The healthcare sector can be positively impacted by AI in several ways.

So, the question “whether using artificial intelligence in medicine is worthwhile” is not relevant. That’s because it is clear that AI is the way of the future for healthcare. The issue is whether you can afford to implement it in your healthcare app.

If you can, Protonshub Technologies is here to help. Our company works with a team of skilled developers who take pride in delivering custom AI development solutions that fit your budget and meet your goals.

We provide custom healthcare software development, patient record management solutions, and health tracking & monitoring apps. If you are interested in working with certified developers and receiving high-tech security solutions, you must contact Protonshub Technologies.

To know more about how we can help you in your healthcare app development project, reach out to us today!

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Ekta Singh

"Senior Content Writer"

Ekta Singh is a rookie food experimentalist and k-drama fan with 4+ years of experience in writing. Bridging gaps between companies and customers through conversion-driven content is her forte. She believes in writing stories for a living and living life for exploring places.