Artificial Intelligence (AI) has the potential to transform various fields and industries. AI applications in the healthcare industry are changing the approach to diagnosis, treatment planning, and patient care. Tasks such as billing, training, and staff management are also becoming more automated.
According to MarketsandMarkets, the value of the AI market in healthcare reached $2.1 billion in 2020 and is projected to exceed $31 billion by 2026. Meanwhile, nearly 5% of patients in the US receive incorrect diagnoses, with many of these errors involving severe medical conditions. This situation highlights one of the most compelling reasons to apply AI to data analysis, pattern recognition, and support for medical professionals.
In this article, we will explore the top real-world examples of AI in healthcare and the impact it has. Ultimately, we will gain insight into how AI is shaping the future of medicine and patient care.
Application of Artificial Intelligence in Healthcare
British NHS is an excellent example of the use of AI algorithms. They help improve healthcare services by assisting in the following:
- X-ray image analysis: This supports radiologists and allows them to spend more time with patients, reducing the time per patient and increasing the number of individual cases they can handle in a day.
- Home-care services: Remote monitoring applications collect and monitor vital data transferring it to the corresponding medical specialists. This enables people to receive quality care while staying at home.
- Quick brain scanning: These cases are usually related to severe conditions that require rapid action. Algorithms provide data-based insights that enable doctors to act quickly and provide better care.
In general, AI can play a significant role in various healthcare areas. Let’s examine them more closely.
Applying AI and ML to diagnosis allows medical professionals to focus more on data analysis, patterns, and patient communication. Annually, hospitals perform 3.6 billion imaging procedures, generating a massive amount of data. About 97% of these data are not useful. Machine Learning helps to structure and leverage this information for more precise diagnostics.
For example, Viz.ai has developed AI solutions identifying critical medical conditions like strokes. It empowers medical specialists to intervene in a timely manner reducing the chances of disability and mortality rates. However, while it’s one of the best applications of artificial intelligence in healthcare and incredibly helpful, it cannot be considered a substitute for skilled and trained medical specialists.
A personal approach is crucial in medicine. Each person can be considered as a unique dataset. AI makes work with this data more sophisticated and precise. Algorithms can accurately analyze genetic information and medical history to tailor separate and individual treatment plans. They can also recommend specific medications, minimizing adverse effects by accounting for patient-specific factors.
PathAI is an excellent example of a company that utilizes applications of artificial intelligence in healthcare diagnostics. Its algorithms predict patient outcomes with the highest accuracy.
Electronic Health Records (EHR) and Data Analysis
The routine of a healthcare specialist includes collecting, managing, and analyzing massive volumes of data. It is a daunting task, and there’s always a possibility of mistakes related to tiredness or distraction. AI-driven solutions come in handy in these situations.
Electronic Health Records (EHR) and other clinical data sources get thoroughly analyzed with the following data analysis and management. Healthcare specialists proceed with drawing conclusions, analyzing patterns, and using these insights in treatment planning.
AI employs robust encryption and authentication mechanisms to comply with The Health Insurance Portability and Accountability Act (HIPAA) regulations, ensuring data privacy and security.
Drug Discovery and Development
The usual drug discovery process is complicated and time-consuming. AI algorithms reduce the time needed to identify drug candidates and make predictions. AI analyzes data to identify promising molecules, expediting the drug development timeline.
AWS HealthScribe is a prominent example of Artificial Intelligence apps in healthcare. It utilizes generative AI to automatically create clinical documentation. This approach reduces administrative burdens and allows healthcare professionals to focus on patient care.
Virtual Assistants and Chatbots
AI-powered virtual assistants and chatbots transform patients communication with healthcare organizations. While using them, patients can ask general medical questions, schedule appointments, and receive straightforward medical guidance in real-time. These tools help to educate patients, respond to general questions, and reduce the workload on medical specialists and administrators.
Companies like Ada Health provide a virtual health assistant that uses AI to analyze symptoms. Based on this analysis, it offers insights and guides users toward relevant medical resources. It’s important to remember that while virtual assistants are easy to use and convenient, they can not replace in-person medical consultations for serious health concerns.
Remote Monitoring and Predictive Analytics
According to GMI, the wearable AI market in healthcare is projected to reach $180 billion by 2025. By analyzing patient data in real-time, AI can identify changes in health metrics, detect patterns, and predict potential health issues. This proactive approach improves patient care, reduces hospital readmissions, and helps prevent developing severe conditions.
In simple terms, it enhances overall patient outcomes and underscores the importance of predictive care. Numerous wearable devices with AI algorithms are available on the market today, such as smartwatches and fitness trackers. They can continuously monitor vital signs like heart rate, oxygen level, sleep cycles, and blood pressure. Devices monitoring blood sugar levels alert medical professionals if deviations from the norm occur.
Systems like these were invaluable during the COVID-19 pandemic.
Robotic Surgery Procedures
AI-powered robotic surgery becomes more sophisticated with AI implementation. AI algorithms bring a significant advancement in surgical precision. Surgeons can remotely control robotic arms equipped with AI algorithms, enhancing accuracy during complex and risky procedures, with vascular surgery being one of the best examples.
This technology opens up new possibilities for minimally invasive surgery, reduces the risk of errors, and shortens patients’ recovery time.
Intuitive Surgical’s da Vinci system integrates AI and robotics in surgical settings. Their surgeon’s console gives a doctor total control of the instrument on each arm. It allows surgeons to view the surgical field in 3DHD and is equipped with a built-in innovation — tremor filtration.
AI plays a crucial role in detecting fraudulent activities, such as insurance fraud. The algorithm analyzes financial and claims data, helping healthcare organizations maintain regulatory compliance and allocate resources more efficiently to benefit patient care.
IBM Watson is an exemplary application of AI in healthcare fraud detection. Its machine learning techniques analyze structured and unstructured data, including medical records, billing codes, and claim history. IBM Watson recognizes patterns, detects anomalies, builds predictive models, and continuously monitors incoming claims and transactions in real-time.
Observation of Patient Health
Ongoing monitoring of patient health conditions is crucial for early intervention and predicting the development of complications. AI-driven solutions analyze data from health indicators in real-time and identify deviations from health metrics considered normal. This allows medical professionals to act quickly and prevent complications.
A good example is the Continuous Glucose Monitoring (CGM) system. These are small and convenient wearable devices monitor blood glucose levels throughout the day. AI assists in data collection and analysis, makes predictions, offers recommendations and personalized guidance, and shares data with healthcare providers and facilities. As a result, there are more accurate insights and better diabetes management.
Improving Gene Editing
Gene editing technologies greatly benefit from AI implementation. AI algorithms analyze genetic data to identify potential targets for gene therapies, enhancing the precision and effectiveness of treatments.This synergy of AI and gene editing is expected to persist and be used to address genetic disorders and advance personalized medicine.
For example, consider CRISPR-Cas9, a revolutionary gene editing technology. It employs AI to predict off-target effects, improve on-target efficiency, learn from experimental data, automate data analysis, and discover new gene editing techniques.
The Potential for AI to Reduce Healthcare Costs
Given all the above, it’s fair to conclude that AI and ML implementation in the healthcare industry have the potential to significantly reduce costs for all parties involved.
Hospitals that adopt AI and ML applications in healthcare for data analysis and personnel management can provide care to more patients, more efficiently. They also reduce the number of errors and attempts that can be costly. Improved staff management helps predict and prevent burnout, saving on staff retention and re-hiring costs.
From the patient’s perspective, the benefits come in the form of improved service and better claim management. Chatbots and AI-driven applications assist in managing health conditions, transmitting data to healthcare providers, and sending alerts about possible complications. They also handle simple questions and queries. All of this saves time and money, reducing the need for frequent hospital visits, tests, and so on.
Insurance companies have the opportunity to process a larger number of claims in a shorter time. The risk of costly errors decreases significantly, and billing processes become automated. It’s worth noting that AI and ML help track fraudulent transactions by identifying patterns that are often invisible to people.
Organizations like Bain, Salesforce, and Yahoo Finance emphasize the role of AI in transforming healthcare economics, creating more sustainable and accessible systems.
Top Examples of AI Applications in Healthcare
It is time to move to the best real-world examples of the application of AI algorithms in healthcare. We mentioned them before, but they all deserve more precise attention. The following examples demonstrate AI-enriched technologies’ capabilities, advantages, and potential limitations.
H2O.ai is transforming various industries, including the healthcare field. It impacts and sometimes defines the processes of decision-making and communication between parties.
H2O.ai’s AI-driven solutions have the potential to revolutionize patient care, diagnostics, and treatment. Wearable devices and health recording systems provide vast amounts of data that must be analyzed and managed daily. H2O.ai builds models to analyze data, identify patterns, and accurately predict patient outcomes.
Healthcare providers can develop customized patient treatment plans using machine learning algorithms. These algorithms are based on unique genetic information, medical history, activity levels, and habits, among other factors. This results in better and more personalized treatments. H2O.ai’s solutions can also assist in early disease detection, drug discovery, and optimizing hospital operations. This leads to a significant improvement in the overall efficiency of the healthcare system.
PathAI is a global leader in AI-powered pathology. Its AI-powered models improve diagnostics and achieve outstanding accuracy in analyzing data. As of today, they perform best in drug development for complex diseases.
PathExplore — the offspring of PathAI — offers exceptional tumor microenvironment (TME) resolution from H&E whole-slide images. It is currently available for the following types of cancer: breast, colorectal, gastric, non-small cell lung, pancreatic, prostate, renal cell carcinoma, and melanoma. More indications are set to launch later this year.
On August 29, 2023, PathAI announced that it had been selected to collaborate with FNIH as a technology partner. This partnership will bring AI-assisted precision to the study of mucosal healing.
Viz.ai was created as an AI-driven model for stroke diagnosis, treatment, and prevention. The primary goal was to accelerate care and minimize the risk of complications and disabilities. Later, it expanded its scope to include pulmonary embolisms and aortic dissections.
At the core of the Viz.ai system is an extensive library of scans. When a patient’s brain image is input into the system, the AI algorithm compares it with all the databases, identifies similarities and patterns, and provides insights. The objective is to detect early signs of strokes and alert doctors. Medical specialists can view these images on their AI-powered mobile apps within their healthcare profiles.
Viz.ai also has a significant impact on collaboration and communication within the healthcare system. The platform synchronizes the profiles of doctors and other personnel involved in each case. This ensures they receive timely updates and alarms, facilitating effective patient allocation and coordination among medical specialists.
The benefits of implementing AI in healthcare applications are indisputable. AI and ML algorithms accelerate the development of treatment plans and drug discovery, enabling faster life-changing decisions. Chatbots assume a significant part of communication responsibilities, while wearables monitor vital signs. Massive volumes of data can be compared and analyzed in mere minutes.
As a result, people spend less time and money in hospitals, and healthcare facilities can provide services to more people. AI is an ever-evolving field, with the potential to fundamentally transform and shape the future of medicine. While its full potential is still a subject of discussion, its undeniable impact on various specialties is evident. However, ethical concerns, the use of personal data, and AI-related risks are also important considerations that must be addressed.