AI can’t take the place of doctors yet, but healthcare workers who use AI algorithms might replace those who don’t. Learn how artificial intelligence can help healthcare. Find out how predictive AI, computer vision, and generative AI may benefit your organization.
In this article, we will discuss different applications of AI in healthcare and give you 10 real-life examples, showing how it can help your clinic operate more efficiently, assist your staff with tasks, give doctors helpful information, and improve patients’ experiences.
Let’s start!
Table of Contents
AI implementation in the healthcare industry
The AI-driven healthcare industry features sustainable and intensive growth with high chances for the investment’s annual growth. According to the report delivered by Mordor Intelligence, the value of AI in the global healthcare market is about to reach 36.79 billion USD by 2029. The research has also shown that the CAGR for the sector totals 25.83% (for the dedicated study period 2024-2029).
These pieces of data are just like those renowned rays of light falling intentionally on the right path for healthcare organizations to follow. Especially if we know the reasons behind such statistics. Improved efficiency, accurate diagnosis, personalized treatment plans, and drug discovery are just some of the long lists of AI benefits across the healthcare industry that pose considerable value from the entrepreneur’s perspective. Yet, looking at the whole world’s scale, AI dominance, chiefly in healthcare, seems to be inevitable.
What is the role of AI in healthcare projects?
Artificial Intelligence plays a transformative role in healthcare products, enhancing the efficiency and precision of medical processes. It aids in accurately diagnosing diseases by scrutinizing images like X-rays and MRIs with algorithms capable of identifying irregularities usually overlooked by the human eye. It can also optimize treatment plans by processing large amounts of data to recommend personalized therapies based on a patient’s unique genetic profile and history. Furthermore, AI contributes to operational improvements in healthcare facilities by streamlining administrative tasks, predicting patient admission rates, and managing resources effectively. This integration of AI not only leads to better patient outcomes but also reduces overall healthcare costs and improves the accessibility of quality care.
If that’s not impressive enough, the advent of General AI (Gen AI) significantly amplified the impact of AI on healthcare solutions, offering even broader capabilities and deeper insights. This versatility enables it to assist in complex clinical decision-making by synthesizing information from diverse sources, including patient records, professional literature, and real-time data.
Gen AI can also interact more naturally with patients and healthcare providers through advanced natural language processing, improving communication and personalized care. Furthermore, it has the potential to innovate in areas like drug discovery and epidemiological modeling by identifying patterns and predictions that are beyond the scope of traditional AI. With all that in mind, McKinsey estimates that Gen AI can bring $1 trillion of potential improvement to the industry.
Now, let’s break it down and take a look at some specific use cases of AI technologies in healthcare.
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Improving efficiency and accuracy of the medical diagnosis
The study conducted by BMJ Quality & Safety has shown that, in the United States, over 12 million adults are misdiagnosed each year. It means that 1 out of 20 adult patients receive the wrong diagnosis. Such a situation influences not only the course of a certain disease but also subsequent outcomes. It can also lead to other misdiagnoses, as even if you change care teams, doctors rely on your previous medical records (which can be inaccurate).
But one rarely can, in good conscience, blame the doctor or anyone else. Because no one is in a machine. Except for machine learning algorithms, which are, obviously, machines. AI products in healthcare provide benefits to the diagnostic process, thus increasing the efficiency of the whole treatment. It takes into account much more data than a human can, so it can provide correct disease diagnosis before evident symptoms take their toll. For instance, finding hints for diabetics retinopathy on the base of eye images (source: Nature, May 28, 2021).
>>> Read also: Solving healthcare problems with AI
Improving patient care through the use of AI solutions
AI is revolutionizing customer service in healthcare by enabling more personalized, efficient, and accessible interactions between patients and healthcare facilities. Traditional AI systems help automate routine inquiries, schedule appointments, and manage patient data or identify patients who need the most urgent assistance, significantly reducing wait times and human error and contributing to a smoother, more efficient healthcare experience for both patients and providers.
Generative AI goes a step further by engaging patients with human-like conversations, providing detailed explanations of procedures, and offering tailored health advice based on individual patient profiles. This technology can also generate realistic simulations and visualizations to help patients understand their conditions and treatment options better. By improving communication and support, AI and Gen AI not only enhance the patient experience but also free up healthcare professionals to focus on critical care, fostering a more patient-centric healthcare environment.
>>> Read also: Predictive analytics in healthcare – how to improve the efficiency of a clinic?
Speeding up medicine development
Although the vision of developing a bespoke diagnosis & personalized medical approach is tempting, it would be roughly helpful if we still have just one or two courses to choose from. Unfortunately, research has shown that a challenging part of circa 2.6 billion dollars assigned to drug development is lost irrevocably on behalf of testing, errors, and regulations.
Wind of change hustles loudly also in the area of newly developed drugs. AI analyzes massive bytes of data to accelerate medicine development. So, it happens that enormous pharmaceutical companies hire smaller AI-driven startups focused on finding new ways, paths, and gaps in drug development. Why?
Every innovation involves the time and costs of departments dedicated to research and development. This rule is being applied to whatever we think of innovation in batteries, education systems, or healthcare. AI use cases in the healthcare industry can be applied to every stage of drug development to:
- Reduce the costs of the research.
- Avoid human mistakes and check if calculations are correct.
- Identify significant areas for improvement and needed intervention.
- Discovering candidates and stimulating connections finding,
- Speeding up tests and, as a result,
- Classify biomarkers to identify, for instance, risk factors for developing a disease.
Providing personalized medical approaches and services
With predictive algorithms, it is possible to anticipate a patient’s specific response to a considered drug or therapy. Thanks to the cross-transfer of referenced data of patients bringing similar complaints, the system compares the conditions, treatments, and the flow of courses. It results in a personalized approach that offers plenty of treatment options considering specific factors.
Developing new AI tools for medical research
Applications powered by AI find their niche in healthcare as well, especially in genome-based diagnostics. In genome sequencing and annotation, machine learning technologies try to recognize common patterns to develop solutions to improve the damaged parts of genes. But, for some companies, there is no need to edit genes to have an AI-driven attitude in their veins.
Another use case is using natural language processing (NLP) to speed up clinical trials. Thanks to sharing patients’ histories available from various sources, we can structure high-dimensional graphs that allow patient identification and highly advanced patient-to-disease matching.
Read more about the benefits and challenges of using AI in healthcare.
Medical fields where AI can help
AI in medicine uses mainly algorithms that read numerical or image-based data and (including, for instance, information about heart rate or MRI scans) prepare required classification. Areas which turn out to be incredibly responsive to AI-handled skills are:
- surgery,
- nursing,
- administration,
- others (such as drug development, radiology research, or risk management).
Types of artificial intelligence in healthcare
Various types of AI in the healthcare industry enhance patient care and operational efficiency:
- Machine learning improves diagnostic accuracy by training models on vast datasets of images and patient records. It detects patterns and anomalies that human clinicians may miss.
- Deep Learning utilizes neural networks with many layers to process large datasets. It enhances medical imaging to detect abnormalities, analyzes genomic data to identify genetic markers, and processes speech and text to extract information from clinical notes.
- Natural Language Processing (NLP) extracts meaningful information from unstructured data such as notes and research papers. It improves clinical decision-making and patient record-keeping.
- Computer vision enables computers to interpret and make decisions based on visual data. In healthcare, it serves image analysis in radiology, pathology, and dermatology, assisting in the detection and diagnosis of common diseases such as cancer and diabetic retinopathy.
- Predictive Analytics tools use historical data and statistical models to forecast patient outcomes and disease outbreaks. They enable proactive interventions and better resource allocation.
- Generative AI, finally, can simulate complex biological processes, assist in drug discovery, generate patient education materials, or serve as patient virtual assistants.
Each of these AI technologies contributes uniquely to advancing healthcare, from enhancing diagnostic capabilities to streamlining administrative functions.
10 common examples of AI in healthcare
Automating administrative tasks for healthcare providers
AI makes life simple, allowing us to get rid of mundane, often frustrating tasks. For instance, single calls when a client is being informed about basic procedures. That is why the most down-to-earth healthcare applications of AI regard fulfilling administrative responsibilities. With the help of AI chatbots providing assistance, patients communicate with AI-driven software to gain the information they need instead of directing their emails straight to reception. In addition, automation gives a deserved relief for healthcare organizations’ staff, which improves their job efficiency, performance, and endorsement but speeds up all the processes.
Check other ideas for successful AI adoption in your business.
ML-based radiology results analysis
Radiologists working in small groups struggle to obtain the most valuable information. Unfortunately, as long as the analysis is conducted by, let’s say, 5 and often exhausted people, the outcome will always be subjective.
Machine learning, on the contrary, can analyze images such as computed tomography (CT) or magnetic resonance imaging (MRI) to track hidden connections and provide crucial data for researchers, hospitals, and their clients. As a result, radiologists use AI to help automate daily administrative tasks, improve diagnostic accuracy, eliminate the risks of human errors, and let researchers focus on complex cases.
Shortening diagnostic process with deep learning
Do you know the character of Dr. House from a popular TV series? He is famous for his natural-born talent for diagnosing diseases and sarcastically distinguished sense of humor. Although AI does not have enough life trauma to throw decadent jokes, it surpasses House’s diagnostic thinking.
Among examples of artificial intelligence in healthcare, deep learning is a promising technique for abnormality detection. It is widely used to augment the diagnostic process. Running deep learning experiments is, in total, much cheaper than hiring researchers for that purpose. AI is also less likely to make mistakes and provide inaccurate diagnoses, and even so, it is easy to check at what step the system commits an error. AI in healthcare supports doctors in decision-making, thus speeding up and improving the whole diagnostic process.
Developing electronic health devices for medical tasks
Most medical researchers’ base is, foremost, tissue samples. However, experts’ predictions indicate that thanks to intelligent algorithms, an opportunity to invent new tools arises. Their purpose is to screen chest X-rays in search of tuberculosis or cancer. This idea could be developed further as an app that would give healthcare providers a helpful hand while testing a niche lacking high-quality massive libraries.
Predicting health risks
The world is dynamic. More often, risk management (of an activity) is an essential field to measure rather than the activity itself. It stems from this mentioned dynamic itself as the number of factors to consider increases every year. That is why AI tools in healthcare are often used to predict risks, such as the probability of getting resistant to antibiotics, identifying the suitable layers of risk stratification, or just possibilities for infection.
We live in a world where most people can travel abroad to different climate zones or use diets and exercises that are not exactly meant for their health condition. And so, a variety of risks arises due to the fact that there are more and more factors influencing our health. The rumor has it that humans can no longer analyze this health data efficiently. But then they meet machine learning, and you know what it says? “You have my sword.” Well, at least, this is what we heard.
Real-time vitals monitoring and analysis
Where else can we find AI? In crucial health metrics monitoring and real-time analysis. To obtain a comparison, let’s say that you have 5 researchers in your hospital hired. All of their studies are static. It means they are prepared on a scheduled date concerning previously determined factors, with the prediction for, let’s say, 5 years. Analyzes prepared with the help of algorithms are dynamic and real-time.
If you need to consider two more factors, researchers have to add them to the system and monitor how results are calculated. If you want to span the number of years included in the prediction, you can do it with a few clicks. Of course, the system spots that the accuracy for each added year decreases. But the clue is that the analysis, once settled, can be easily adjusted to specific needs.
Improving business decision-making
Artificial intelligence is used in healthcare not only in the scientific side of developing new tools and drugs and accelerating diagnosis processes but also in business-related areas such as decision-making. Doctors can gain access to unheard-of insights to obtain a more profound sense of treatment variabilities. This stems from the transparency of the AI line of thinking.
We can also assume that the language of artificial intelligence is becoming more renowned across different departments. It enables fluent communication between scientists and business professionals. This compliance supports decision-making as both researchers and the management use similar words and points of view drawn up from the AI.
Read more about AI business applications
Earlier cancer detection
If you ask anyone about the most desired drug to be developed, most people would probably pick a medicine for cancer treatment. No wonder! At Tulane University, a group of researchers used AI to analyze tissue scans to accelerate the process of a colorectal cancer diagnosis. The research involved gathering 13.000 images of colorectal cancer from more than 8.000 subjects from independent medical centers located in Asia, Europe, and the United States. They developed a machine learning model that was more accurate in diagnosis than the human doctors.
That’s correct. AI is able to diagnose cancer more accurately than experienced radiologists do, which brings huge value to the treatment process. Moreover, they can grade the aggressiveness of a rare cancer twice as accurately as a biopsy. As reported by Euronews, Researchers from The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research developed an AI model that is able to accurately predict how aggressive a tumor was likely to be for 82% of them, while only 44% of tumors were correctly graded using a biopsy.
Leveraging neural network for clinical trials
Examples of artificial intelligence in healthcare also cover neural networks, such as AtomNet. The systems are used to identify patterns in bioactivity. Screening dozens of millions of genetic sequences daily, artificial neural networks provide insights faster and more efficiently than traditional researchers do. ANNS constitute animal brains and, with the AI models devised for this purpose, imitate the possible behaviors of human neural networks.
Improving communication between healthcare professionals and patients
There are always more ill people than doctors to take care of them. The biggest challenge for healthcare is the lack of time. If we run out of time even for the diagnosis processes, we can’t even think of soft skills and manners such as well-thought-out communication. The studies have shown that the main complaint among patients is the low standard of client service.
To deal with this challenge, AI in medicine is concerned with different ways to solve the problem of poor communication. Health systems struggle to reduce complex paperwork that stems from the existence of a variety of insurance coverage covering a variety of ailments and a variety of conditions based on which a service can or cannot be provided.
Examples meant to improve communication leverage AI for creating platforms for automotive appointment systems, real-time health status monitoring (handy for chronic diseases such as diabetes), or the development of patient engagement solutions.
Examples of Generative AI in healthcare
Even though the article was written before the outbreak of the GPT in 2022, it would be unacceptable to leave it without presenting any examples of Gen AI.
One of the simplest and most common applications of large language models (LLMs) in the healthcare industry is advanced chatbots and virtual health assistants. These aren’t your run-of-the-mill bots; they can engage in human-like conversations, offering personalized health advice, symptom checks, and even mental health support. Imagine having a friendly, knowledgeable health assistant available 24/7—no appointment necessary.
In research, on the other hand, Gen AI helps researchers use to develop and test new treatments by generating synthetic patient data. This allows for innovation without the worry of patient data privacy issues. It’s like having a never-ending, privacy-compliant data source at their disposal.
And let’s not forget about drug discovery. Gen AI can simulate how new drugs will interact with the human body, predicting their effectiveness and potential side effects. This accelerates the development process, bringing new, life-saving medications to market faster.
In essence, Gen AI is transforming healthcare by making it smarter, safer, and more efficient. It’s not just a buzzword; it’s a revolutionary tool that’s reshaping the future of medicine.
Read also: What are the costs of healthcare app development?
What are the use cases of AI in healthcare? Summary
AI applications in healthcare are concerned with speeding up and improving the efficiency of critical fields such as diagnosis, disease detection, health risk assessment, drug development, administration, and communication. It seems that if we combine the magnificent talent of researchers and doctors working by vocation with the power of AI, predictive analysis, predictive modeling, and calculation skills of various machine learning algorithms, living in a disease-free world is not a dream anymore. What seemed to be a distant future use has finally come into humanity’s view.
