Unlocking the Potential of Artificial Intelligence in the Healthcare Sector
Artificial intelligence (AI) is the method of theorizing and developing computer systems to think like human minds and consequently perform tasks requiring human intelligence, such as speech recognition, decision-making, comprehending human prompts, and translating between languages. AI uses real-time data to replicate human intelligence with the help of algorithms, robots, and computers. However, the scope of applications for artificial intelligence is wider than just these basic functions. With rapid technological advancements in this field, the scope of AI and its use cases have expanded exponentially. AI is used in most industries and sectors to benefit from the multiple features of artificial intelligence for industry growth.
Artificial Intelligence use cases in healthcare.
AI finds numerous uses in the healthcare and medical device industries. Artificial intelligence is used for early diagnosis of diseases, medicine compatibility tests, research and development for vaccination creation and disease prevention studies, and so on. Artificial intelligence can also be used to analyze medical data at a large scale to establish patterns, determine symptoms of diseases, study success rates and side effects of cures, and personalize health treatment needs for individuals.
- Machine Learning (ML) is commonly regarded as one of the most common forms of AI. Machine learning technology has significantly impacted the healthcare sector by enabling the use of artificial intelligence for medical diagnosis and treatments. These systems can process large amounts of raw data relating to clinical documentation and analyze the data to identify any patterns found within the data and predict future medical outcomes with improved accuracy. Machine learning can also be used for disease diagnosis, development and discovery of therapeutic drugs, etc., to help in improved diagnosis and treatment of illnesses. The most basic function of machine learning in healthcare is precision medicine, which predicts success rates of treatment procedures on patients based on data relating to the patient’s physiological attributes and the treatment framework.
- Natural Language Processing – This form of artificial intelligence enables the interpretation and usage of human intelligence for computer systems. It is widely used in the healthcare sector and many other industries too. Natural language processing is used in healthcare to improve diagnosis accuracy, streamline clinical processes, etc., to improve patient care. This technology can accurately diagnose illnesses through medical records by extracting usable information from a vast data library. It can also help predict potential medical risks based on historical patient data and identify treatments and medications that are expected to have better effectiveness depending on individual patient data. This form of artificial intelligence is widening in its scope of applications due to technological advancements in the field and is quickly becoming an indispensable part of modern healthcare.
- Rule-based Expert Systems – These systems can be dated back to the 1980s when variations of “if-then” rule sets were used in healthcare. Rule-based expert systems are still in use in healthcare for clinical decision support. These systems usually require human engineers to define an extensive array of rules in an area of interest. However, these systems can pose complex problems as the number of defined rules increases. Therefore, it is seen that rule-based expert systems are being slowly substituted by other forms of artificial intelligence, such as machine learning, etc.
- Physical Robots – These robots can be programmed to perform pre-defined tasks. Recent technological developments have made these robots more collaborative and can be trained more easily. The operating systems for these robots can be based on artificial intelligence technologies, which make them smarter than simple task robots. Surgical robots are prevalent in the healthcare industry. The first surgical robot, the da Vinci Surgical System, was approved by the Food and Drug Administration in 2000. These surgical robots enhance the capabilities of surgeons by helping them in stitching wounds, creating precise incisions, etc. However, these robots are not developed enough to make important surgical decisions independently, as surgeons still make these. Surgical robots are commonly used in prostrate surgeries, gynecologic surgeries, etc.
- Robotic process automation – This technology performs administrative-based digital tasks, such as information systems. This is a comparatively cheaper form of artificial intelligence and can be easily programmed to carry out the desired functions. It is based on server-based computer programs and acts like a semi-intelligent user of information systems through a combination of workflow, business rules, and presentation layer information. These processes are most commonly used for repetitive and routine tasks such as updating medical records, billing, authorizations, etc. These systems can also be used for more complex tasks, such as extracting data by combining them with other technologies.
Even though these different forms of artificial intelligence technology used in healthcare are exclusive, they are being increasingly integrated to form more intelligent and composite artificial intelligence solutions due to technological developments. Thus, these combined technologies are used for many applications, such as diagnosis and treatment applications, patient engagement and adherence applications, and other administrative applications. The use of artificial intelligence technologies such as machine learning, natural language processing, robotic process automation, etc., together enables the expansion of the scope of artificial intelligence applications in the healthcare sector.
Companies in the Healthcare Market are leveraging AI.
Several global companies are increasingly investing resources in artificial intelligence applications in the healthcare sector, which is a testament to the ingenuity and potential of the technology. Global leaders in technology, such as Google, etc., are prioritizing artificial intelligence development to become industry leaders in this emerging sector.
- Google Health/Deep Mind – DeepMind’s health team and Google Health merged in 2019 to build artificial intelligence products to support care teams and improve patient outcomes. The teams are tapping this technology to assist in screening breast cancer, predicting patient outcomes, helping with vision loss, etc. Google Health, in collaboration with DeepMind, has developed a solution for identifying breast cancer with the help of artificial intelligence technologies, and it is reported, as per its studies, that this technology performs better than human radiologists by over 10%.
- Augmedix – This company specializes in medical documentation solutions. It has developed solutions that help extract data from doctor-patient conversations and simultaneously process it as medical notes. This data can then be transferred to healthcare provider’s electronic health record systems for future use. The company aims to help healthcare units, and workers save time and resources by automating these administrative tasks. Automation of these routine tasks also helps in reducing medical personnel burnout.
- Arterys – The company aims to revolutionize medical imagery with the help of artificial intelligence and cloud computing. The FDA clears its artificial intelligence-based platform, and it helps in performing faster radiology image examinations, reduces the rate of missed detections, and automates routine administrative tasks, allowing physicians more time to be involved in patient care. It also allows its users to access medical data anytime, anywhere, due to its cloud accessibility features. The company was acquired in October 2022 by Tempus Labs, which was regarded as one of the biggest acquisitions in the artificial intelligence-based medical imaging sector. This represents the growth and potential of artificial intelligence in radiology and other imaging use cases.
In conclusion, The scope of artificial intelligence in healthcare is expanding rapidly, and its applications are being improved constantly due to rapid developments in artificial intelligence technology. In the coming years, the prevalence of AI in carrying out routine or common tasks is also expected to increase. However, there are certain barriers to the adoption of AI, such as lack of expertise, high costs of certain AI tools, data complexity, lack of platforms for model development, etc. These are certain factors that are hindering the growth potential of artificial intelligence.
