AI in Healthcare: Five Use Cases & Types

The expansion of digital information about patient health and the demand for cost reductions are driving the adoption of artificial intelligence (AI) technologies in healthcare. Its subsets, such as machine learning and deep learning, predictive analytics, and natural language processing (NLP), hold great promise for the healthcare industry. Especially when they’re used to help healthcare professionals with administrative tasks, provide insights, and improve health outcomes.

In 2021, 85% of healthcare leaders had an AI strategy, and 41% were using AI at a fully functional level. This isn’t surprising since artificial intelligence can help address staff shortages and hospital readmissions, improve information flow, and make good use of terabytes of structured and unstructured data.

Having delivered dozens of AI healthcare projects, Unicsoft knows what opportunities and challenges this technology can bring, and we’re here to share the insights. But first, let’s look into how AI affects the healthcare industry overall.

Impact of AI on healthcare

The global market size of artificial intelligence in healthcare was valued at $15.4 billion last year and is expected to grow at a whopping 37.5% annually from 2023 to 2030. As you can see in the graph below, the application of artificial intelligence in healthcare has already become an important part of many healthcare systems.

The use of AI can save $200-360 billion annually, or 5-10% of total healthcare spending, if implemented over the next five years with existing technology. How is this possible, you ask? Let’s see.

The shortage of physicians in primary and specialty care is forcing hospitals to raise wages to retain their workforce. This causes financial problems and difficulty in finding medical professionals due to high labor costs. AI technology in healthcare can alleviate this problem by taking over some of the administrative, triage, and primary diagnostic tasks.

Here’s one more example. The use of AI-powered imaging, wearables, virtual health assistance, and robotics can save the healthcare sector about 1.8 billion hours of work annually. And that’s just the beginning.

Preventing medical errors is another area where AI can revolutionize healthcare. Studies show that preventable medical errors affect up to 7 million patients each year and cost over $20 billion. Generative AI can minimize these numbers by helping doctors make accurate and informed diagnoses. By analyzing gigabytes of patient records, AI can identify potential health problems or misdiagnoses and suggest further testing or treatment options.
Now that we have explored the impact of AI on healthcare let’s look at the key use cases where AI is significantly changing the healthcare industry.

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Top 5 use cases of AI in healthcare

One of the most popular applications of AI in healthcare is managing staffing shortages. A recent survey shows that 47.5% of healthcare organizations have AI solutions to fill gaps and improve productivity. Meanwhile 42% of respondents seek ways to improve patient care and administrative efficiency with AI. We identified five areas in the medical field where AI can make an impact.

Cancer prognosis

Conventional methods of cancer prognosis often rely only on histologic or genomic data, potentially omitting important information that could affect the accuracy of predictions.

The application of AI can remedy this by combining multiple data types into a single entity. Deep learning is constantly improving the accuracy of predictive models and cancer prognosis, leading to more effective, personalized treatment, better patient outcomes, and higher survival rates.
A real-life example from the Mahmood Lab at Brigham and Women’s Hospital shows the successful implementation of this approach. They developed a proof-of-concept predictive model that integrates different types of data and used a deep learning algorithm to extract meaningful insights. After rigorous evaluation of different cancer types, the model demonstrated improved prognostic accuracy, lowering cancer treatment costs and improving patient quality of life as a result.

Early symptom prediction

Effective and timely diagnosis is critical, but it can be challenging for clinicians to detect subtle changes or patterns that indicate the onset or progression of disease. That’s where AI comes in. Machine learning algorithms can analyze vast amounts of structured and unstructured patient data to identify relationships and patterns that may not be apparent to human clinicians.

The collaboration between Montefiore Medical Center and Intel’s Healthcare AI team is one of the successful examples of AI in healthcare. They created a Patient-centered Analytical Learning Machine (PALM) using ML algorithms to identify complex relationships and patterns between symptoms and treatment responses.
By continuously monitoring things like cholesterol levels, blood sugar levels, heart rate, oxygen levels, etc., PALM can use AI to predict when respiratory arrest, sepsis, or other dire outcomes are likely. This facilitates treatment and can save lives, money, and time for the patient, medical providers, and the healthcare system.

Hospital stay forecast

Long-term hospitalizations pose challenges to healthcare organizations worldwide. These include increased resource allocation, potential adverse patient outcomes, and strain on overall hospital capacity. Machine learning algorithms such as Generative Adversarial Networks (GANs) can address this problem by making accurate predictions about the risks of long-term stays based on the analysis of patient data.

For example, the AI Skunkworks team at the NHS AI Lab has developed an AI proof-of-concept for predicting long-term hospital stays. Using GANs, the team trained the algorithm on a diverse dataset of patient data that included demographic information, medical histories, and admission details.
The AI tool achieved an impressive 85% accuracy in predicting the risk of long-term hospitalization. Thanks to its use, hospitals were able to reduce the average length of stay by two days, resulting in estimated cost savings of £1 million per year.

Personalized medications and care

Traditional medical practices often ignore patients’ unique genetic predispositions, lifestyle factors, and overall health. This may lead to ineffective treatments, lengthy recovery processes, and unnecessary healthcare expenditures. But AI-based systems trained on extensive patient data can predict which medications and interventions are most likely to be effective for patients.
One of the examples of AI in the medical field is GNS Healthcare, which uses machine learning to match patients with the most effective treatments. The system has already shown promising results, including increased patient satisfaction, reduced costs, and avoidance of unnecessary hospitalizations.

Real-time prioritization and triage

The biggest challenge in triage is to ensure that the most critical cases are treated immediately and that all patients are assigned to the correct physician. To achieve this, medical staff must analyze patients’ medical records and lab results along with the patient’s current condition, which leaves room for human error.

An AI-based patient triage system based on NLP, ML, and computer vision can automate patient prioritization and route cases to the appropriate healthcare providers. Enlitic’s Curie software is an example of this approach. It scans cases for clinical findings, prioritizes them, and matches patients with appropriate physicians. Results from a study of over 10,000 patients demonstrate:

• 25% reduction in time to diagnosis
• 15% improvement in patient satisfaction
• 10% decrease in unnecessary referrals

AI-based triage systems relieve healthcare workers of administrative tasks, allowing them to prioritize patient care and save time and costs.
Despite all the progress AI has made in the medical field, its implementation comes with a number of challenges.

Difficulties with using AI in healthcare

Let’s look at the difficulties medical organizations may face when adopting AI technology in healthcare.

• Data quality. AI models rely on large amounts of high-quality data to produce accurate results. However, there is little well-labeled and representative data for particular uses of AI in healthcare. This makes it complicated to integrate data from different sources and ensure its accuracy and completeness.

• Algorithmic bias. WHO’s new report cautions that AI systems trained predominantly on data obtained from individuals in high-income countries may not effectively function for people from other settings. AI systems must be carefully designed and trained on a dataset fully representative of the population. Otherwise, they can produce false or even harmful results. 

• Privacy and security concerns. Healthcare data contain sensitive and personal information, making privacy and security crucial to prevent data from unethical use.

• Integration and compliance. AI technologies must be seamlessly integrated to complement existing practices without causing disruption or increasing workload. Besides, a solution must comply with HIPAA, GDPR, or any other local laws that dictate the rules for data protection, patient consent, clinical validation, and liability.

• Lack of talent for healthcare AI. The McKinsey State of AI in 2022 Survey shows that it’s very difficult to hire AI data scientists. 46% of respondents said it’s extremely difficult to fill this position. Similarly, positions such as data engineers (49%), architects (47%), and machine engineers (42%) were also in high demand and difficult to fill.

Despite all these challenges, the current AI use cases for healthcare promise the technology a bright future in the industry.

The future of AI in healthcare

AI has already made its way into healthcare processes, promising to make them cheaper, more effective, and personalized. Most large healthcare organizations, such as IBM Watson Health, Oncora Medical, and Babylon Health, are already using some form of AI, and 96% of healthcare executives believe AI is critical to achieving health equality goals. Where will it go from here?

The Health Management Academy survey shows conversational AI growing in popularity among healthcare providers, with 27.5% already using it and 72.5% considering its use. For instance, AI chatbots can help therapists treat mental health problems and provide an initial diagnosis. This opens up many business and cost-saving opportunities in this healthcare sector.

Preventive medicine is among the most desired use cases of AI in healthcare. It will allow patients to manage their health indicators without the help of a doctor, which can significantly reduce the burden on the medical system.

However, to function properly, AI needs human control. The speed of data processing allows AI to study thousands of medical records and build a more comprehensive picture of patients’ health.
To reap the benefits of the technology, your organization needs an experienced software development vendor. And that’s where Unicsoft can be of great help to you.

Unicsoft will help you use AI effectively

With 17 years of experience, our team knows how using the latest technologies can give companies better results and a competitive edge. Unicsoft is constantly researching the market to provide our customers with the best use of AI technology in healthcare.

Our recent projects demonstrate our expertise in using AI in healthcare better than words.

AI solution for early skin disease detection

Unicsoft helped a European startup build an AI-powered early skin disorder detection prototype. Our R&D team selected the optimal AI algorithm to detect four types of skin diseases. We used the open-source skin disease library Dermnet.com to teach the algorithm to find problem areas in the photos and classify diseases.

The solution we developed can process thousands of images within seconds and provide primary diagnostics and recommendations.

Wellskin automated melanoma detection solution

Our AI experts developed an iOS application that allows users to diagnose and monitor their moles and submit their skin images to clinics for analysis. By integrating advanced image analysis algorithms, the application can make accurate diagnoses and detect skin cancer at early stages.

Predictive remote patient monitoring system

Unicsoft collaborated with Wanda’s ML engineers to build an AI-powered cloud patient monitoring system that facilitates communication between patients, doctors, and caregivers. Unicsoft experts improved the back-end structure and integrated video, message, and IVR communication options for coordinated care.
Using ML algorithms, prediction, and risk analysis, the system was able to foresee and prevent adverse events seven days in advance. This allows the care team to take preventive measures and foster patient well-being.

Conclusion

The growing number of AI use cases in healthcare and its ability to improve the quality of medical services show how important the technology is for the healthcare industry. The use of AI can help overcome physician shortages, reduce staff burnout, and make healthcare more affordable, accurate, and personalized.

However, algorithmic biases, patient safety concerns, cybersecurity, and the unethical use of medical data can hinder the effective use of AI technology in healthcare. Your organization needs a reliable software development partner to navigate these waters safely. Fortunately, Unicsoft has your back.
Contact us, and we’ll consult you on this topic and help your healthcare organization take advantage of AI.

FAQ

What are the most popular AI use cases in healthcare?

Popular use cases of AI in healthcare include early symptom detection, cancer prognosis using medical imaging, predictive analytics for disease diagnosis and treatment, automated triage and administration.

What are the benefits of using AI in healthcare?

The use of AI improves diagnostic accuracy, personalized treatment plans, efficient data analysis, patient monitoring, and cost savings.

How to overcome challenges when adopting AI in healthcare?

Partnering with a trusted software vendor helps you address data privacy concerns and ensure regulatory compatibility and proper implementation. You can also invest in a robust cloud infrastructure and train your staff to work with AI-powered tools.

What is Unicsoft experience using AI in healthcare?

Unicsoft experts are constantly educating themselves and attending AI conferences to ensure the effective implementation of AI technology in healthcare. Check out our portfolio to learn more about the latest applications of AI in healthcare our company has developed.