The healthcare industry is undergoing a transformative revolution thanks to the integration of artificial intelligence (AI). This technology, with its ability to analyze vast amounts of medical data and generate actionable insights, is redefining how healthcare providers diagnose, treat, and manage diseases.

According to a recent study by McKinsey, AI technologies have the potential to create $150 billion in annual savings for the U.S. healthcare sector by 2026. From predictive analytics in patient care to robotic-assisted surgeries and AI-driven drug discovery, the applications of AI in healthcare are both diverse and impactful.

In this article, we will explore some of the most innovative examples of AI in the healthcare industry. We will showcase how this technology is paving the way for a more efficient, effective, and patient-centric healthcare system.

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State of AI in Healthcare

The state of AI adoption in healthcare is progressing rapidly, with significant advancements and increasing integration across various healthcare functions. According to recent reports, the global market for AI in healthcare was valued at $16.3 billion in 2020. It is projected to grow at a compound annual growth rate (CAGR) of 40.2%, reaching $173.55 billion by 2029. This growth is driven by the widespread recognition of AI’s potential to enhance efficiency, accuracy, and patient care across the industry.

Despite this rapid growth, the adoption of AI in healthcare is not without its challenges. Many healthcare organizations face hurdles such as high initial investment costs, integration with existing systems, and concerns about data privacy and security. A McKinsey survey highlighted that only 21% of organizations have established policies governing the use of AI technologies. A mere 32% are actively mitigating the risks associated with AI inaccuracy.

However, the benefits of AI in clinical practice, such as automating administrative tasks, improving disease diagnosis accuracy, and enabling personalized treatment plans, are prompting more healthcare providers to invest in and adopt these technologies. High-performing healthcare organizations that have successfully integrated AI report significant advancements in operational efficiency and improved patient outcomes.

Examples of AI in the healthcare industry

Artificial intelligence has proven to be successful in a variety of industries and still sparks excitement among businesses. Even though there are numerous AI applications bringing benefits to many sectors, it seems like it’s still feared in healthcare. On one hand, there’s a fear of machine error, ethical concerns, data privacy concerns and other significant challenges of AI adoption. On the other – would patients be willing to see AI as an element of healthcare services? Of course, we’re not going extreme like with Disney’s Baymax. This cute inflatable robot would detect vital stats and treat any ailment. I can imagine not everyone would fancy being accompanied by a robot like that, but we’re not there yet. So, what are some of the ways to use artificial intelligence in healthcare?

AI systems for workflow automation for healthcare providers

AI systems for administrative workflow automation

Doctors, nurses, and other healthcare professionals all have to spend a lot of time doing paperwork and dealing with administrative tasks that burn a lot of their valuable time. AI can assist them in these arduous tasks. An example of such support may be computer-assisted documentation. A company called Nuance developed a solution that helps physicians to capture all necessary information about their patients. Their Computer-Assisted Physician Documentation provides real-time clinical documentation guidance and ensures consistent recommendations.

Solutions such as voice-to-text transcriptions can save a lot of doctors’ time to help order tests, prescribe medications or make notes.

What’s more, with the help of AI, physicians can access processed patient history. This means that they wouldn’t have to go through the entire history to make sense of it. Instead, they could access well-organized patient information whenever needed.

Read also: Examples of Responsible AI

Virtual nurses to support health care

Have you ever been stuck in a waiting room full of people trying to see your doctor? See, it’s not a secret that some hospital consultations are not emergency situations. People feel bad, they’re worried or scared, they want to know what’s going on. This is absolutely understandable but it also puts a lot of pressure on the doctors and nurses to help even more patients and solve problems even faster. Virtual nurses can reduce the number of unnecessary hospital visits, which is beneficial to both healthcare professionals and patients. This is a perfect example of how AI implementation can be leveraged in patient engagement solutions to increase patient satisfaction with medical services.

According to Syneos Health Communications, 64 percent of patients reported they would be comfortable with AI virtual nurse assistants. Some of the benefits listed include 24/7 access to answers and support, round-the-clock monitoring, and the ability to get quick answers to questions about medications. Since virtual assistants are available 24/7, they can help whenever they are needed and provide instant answers.

Real-life example: Babylon app

The Babylon app uses AI in an interactive symptoms checker to provide medical care. The system asks questions and analyzes the answers, it assesses known symptoms and risk factors to provide informed, up-to-date medical information.

Babylon app - an example of AI applications in healthcare

Robot-assisted surgery

AI-assisted robotic surgery has an estimated value of 40 billion dollars. No wonder. Actually, it is really impressive as technology. Robotic surgeries are considered minimally invasive as they allow replacing large incisions with a series of quarter-inch incisions and utilize miniaturized surgical instruments. In a robotic procedure, the pre-op medical records are integrated with real-time operating metrics to improve patient outcomes. This technique allows for better physician precision and can lead to a 21% reduction in the length of a patient’s post-operation hospital stay.

Real-life example: the da Vinci robot

The da Vinci is considered the world’s most advanced surgical robot. It lets healthcare professionals perform a variety of procedures with greater flexibility and control than conventional approaches can offer. It has robotic limbs with surgical instruments attached and provides a high-definition, magnified 3D view of the surgical site. The surgeon controls the machine from a computer console near the operating table.

Diagnosis aid

Even though AI diagnosis isn’t extremely advanced and needs to be developed further, there are some impressive cases already. In 2018, a story of artificial intelligence recognizing skin cancer based on medical images better than experienced healthcare professionals made the news. In a study published in a leading cancer journal, researchers from the USA, Germany, and France trained a deep learning convolutional neural network (CNN) to identify skin cancer. To achieve that, they fed the network with more than 100 thousand images of malignant melanoma, as well as benign moles. The researchers compared the performance of AI to that of 58 international dermatologists. They found that AI made fewer mistakes: it missed fewer melanomas and misdiagnosed moles as malignant less often.

Read also: What are the most important benefits of AI in healthcare

AI systems using machine learning and other AI algorithms to analyze patient data

Electronic health records analysis

Wearable health and fitness monitors are more and more popular, and there’s also a whole variety of apps tracking our daily activities. I’m sure you’ve heard of Google Health or Apple Health. Right now, these solutions provide us with insight into how much we move, how many kilometers we walk, how fast we run, how many calories we burn, and what our heart rate is. All this medical data is extremely valuable! It can be shared with the doctor to provide alerts in case of health issues or up-to-date information about the patient’s habits. Monitoring your health using solutions that gather and analyze patient data is much easier than trying to figure out how many minutes a day, on average, you move and when you last went for a jog.

Drug discovery process

The drug discovery process is undergoing a significant transformation with the advent of artificial intelligence. Traditionally, drug discovery is a time-consuming and costly endeavor, often taking over a decade and billions of dollars to bring a new drug to market. AI is accelerating this process by enabling researchers to analyze vast datasets and identify potential drug candidates more efficiently. Machine learning and AI algorithms can sift through chemical libraries, predict the efficacy and toxicity of compounds, and even propose new molecular structures, significantly shortening the discovery phase and reducing costs.

Identifying drug targets

One notable application of machine learning in drug discovery is in the identification of novel drug targets. Medical AI systems can analyze genomic, proteomic, and clinical data to uncover previously unknown pathways and mechanisms of diseases, opening up new avenues for therapeutic intervention.

Furthermore, AI-driven simulations and modeling can predict how drugs interact with their targets at a molecular level. This allows for the optimization of drug design before moving into costly clinical trials. This combination of big data analysis, predictive modeling, and molecular design surpasses human expertise and is revolutionizing the drug discovery process, making it faster, more accurate, and more cost-effective.

Clinical decision support for treatment planning

Another example of an AI system that revolutionizes the way healthcare providers make diagnostic and treatment decisions is clinical decision support systems (CDSS). Utilizing AI technologies, these systems analyze vast amounts of patient data, including medical history, lab results, and imaging studies, to provide evidence-based recommendations. AI-driven CDSS can detect patterns and correlations that might be missed by human clinicians, thereby enhancing diagnostic accuracy and identifying optimal treatment pathways. This not only improves patient outcomes but also reduces the likelihood of medical errors. By integrating seamlessly into electronic health record systems, CDSS can assist healthcare professionals in delivering personalized, high-quality care efficiently and effectively.

Machine learning systems support healthcare organizations in many ways.

Is artificial intelligence in healthcare going to gain popularity?

For sure! With the rapid development of new technologies, it’s certain that new solutions will emerge while the existing ones will improve. As you can see, AI in healthcare is more than just analyzing medical records. It also presents opportunities across many areas, including diagnostics, virtual assistants, wellness management, and wearables. Artificial intelligence can “sense,” understand and perform actions. Therefore, it can support medical professionals in both administrative tasks and clinical functions.

Apple heading toward the healthcare industry

The importance of consumer health is an issue that huge companies like Apple are more and more interested in. In an interview with CNBC, Tim Cook, the CEO of Apple, said that Apple has been investing in health and wellness in recent years. With solutions like the electrocardiogram-equipped Apple Watch, they aim to empower individuals to manage their health. While Apple is not associated with the healthcare sector now, Tim Cook says:

If you zoom out into the future, and you look back, and you ask the question, ‘What was Apple’s greatest contribution to mankind?’ It will be about health.

With the introduction of Apple Watch and Apple Health Records, the company has officially entered the healthcare industry. These solutions allow users to track their health data and aggregate and visualize their health records. Health and wellness solutions are constantly developing, and they offer more benefits to patients and institutions. We’ll surely witness changes in health care in the upcoming years. The introduction of artificial intelligence may bring some astounding results.

Medical challenges that AI in healthcare can solve – key takeaways

The integration of AI into the healthcare sector is addressing some of the most pressing medical challenges of our time.

By automating administrative workflows, AI is freeing up valuable time for healthcare providers, allowing them to focus more on patient care. Virtual nurses offer round-the-clock support, enhancing patient engagement and ensuring timely medical advice. The precision of robot-assisted surgeries minimizes risks and improves surgical outcomes, while AI’s diagnostic capabilities enhance accuracy and speed in identifying medical conditions.

Furthermore, the analysis of electronic health records through AI algorithms streamlines data management, ensuring better patient monitoring and personalized care. In drug discovery, AI accelerates the identification of new therapeutic compounds, bringing life-saving drugs to market faster. Clinical decision support systems leverage AI to provide healthcare leaders with evidence-based treatment recommendations, optimizing patient care plans and reducing the likelihood of human errors. As AI continues to evolve, its potential to revolutionize healthcare and improve patient outcomes is immense, promising a future where medical challenges are met with innovative, data-driven solutions.

What’s the future of AI in healthcare?

The future of AI in healthcare looks promising as more healthcare providers and technology companies collaborate to overcome existing challenges and further develop AI technologies. Efforts are being made to improve the quality of healthcare data, ensure regulatory compliance, and provide adequate training for healthcare professionals to use AI tools effectively. As AI continues to evolve, its adoption in healthcare is expected to expand, leading to a more efficient, effective, and patient-centered healthcare system.

artificial intelligence in healthcare sector