The growing complexity and volume of healthcare data make artificial intelligence (AI) an increasingly valuable tool. Various types of AI are already being utilized by healthcare payers, providers, and life sciences companies. Key application areas include diagnosis and treatment recommendations, patient engagement and adherence, and administrative tasks. While AI can often perform healthcare tasks as well or better than humans, implementation challenges will significantly hinder the widespread automation of healthcare professional jobs. Ethical considerations in applying AI to healthcare are also discussed.
What is Artificial intelligence?
Artificial intelligence (AI) and its associated technologies are rapidly becoming famous in business and society, and their application to healthcare is gaining momentum. These technologies can potentially revolutionize many aspects of patient care and administrative processes within healthcare providers, payers, and pharmaceutical organizations.
Numerous research studies have demonstrated that AI can perform or surpass human capabilities in critical healthcare tasks like disease diagnosis. For example, algorithms are now outperforming radiologists in detecting malignant tumours and guiding researchers in constructing effective cohorts for expensive clinical trials. However, despite these promising developments, we anticipate that it will be several years before AI can fully replace humans in broad medical process domains.
Types of AI in Healthcare
Artificial intelligence (AI) is not a singular technology but rather a suite of technologies. Many of these technologies have direct applications in healthcare, although the specific processes and tasks they support vary significantly. Below are some particularly noteworthy AI technologies within the healthcare domain
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Natural language processing (NLP)
Natural language processing (NLP) is a subfield of artificial intelligence (AI) that empowers computers to comprehend, generate, and manipulate human language. NLP allows for data interrogation using natural language text or voice, often called “language in.” Many consumers have unknowingly interacted with NLP. For example, NLP is the foundation of virtual assistants like Oracle Digital Assistant (ODA), Siri, Cortana, and Alexa. These assistants rely on NLP to understand user queries and respond in natural language. NLP applies to written text and speech and can be used with human language. Other NLP-powered tools include web search, email spam filtering, automatic translation, document summarization, sentiment analysis, and grammar/spell checking. For instance, some email programs suggest appropriate replies based on message content, utilizing NLP to read, analyze, and respond.
Natural language processing (NLP) tools and algorithms can unlock clinically relevant information hidden in piles of human-generated medical records and articles. In healthcare, NLP can help with two main tasks:
- Speech recognition. It saves clinicians from manually entering EHR notes
- Unstructured data processing. NLP algorithms help people interpret information by classifying data, extracting insights, and summarizing them.
In healthcare, NLP primarily focuses on generating, comprehending, and categorizing clinical documentation and published research. NLP systems can analyze unstructured clinical patient notes, draft reports (such as on radiology examinations), transcribe patient interactions, and engage in conversational AI.
2. Machine Learning
Machine learning is a statistical method for constructing models from data and learning through model training. It’s one of the most prevalent forms of AI; a 2018 Deloitte survey of 1,100 US managers found that 63% of organizations used machine learning in their businesses. This broad technique underpins many AI approaches and comes in various forms. In healthcare, traditional machine learning is primarily applied to precision medicine, predicting successful treatment protocols for individual patients based on their attributes and treatment context. Most machine learning and precision medicine applications require a training dataset with known outcome variables (e.g., disease onset), a method known as supervised learning.
3. Physical Robots
Physical robots are common, and over 200,000 industrial robots are installed annually worldwide. They perform predefined tasks such as lifting, repositioning, welding, or assembling objects in factories, warehouses, and hospitals. Recently, robots have become more collaborative with humans and are easier to train by guiding them through tasks. They are also becoming more intelligent as AI capabilities are integrated into their operating systems. Likely, future advancements in AI will further enhance the intelligence of physical robots.
4. Surgical Robots
Initially approved in the US in 2000, surgical robots provide surgeons with enhanced capabilities, improving their vision, precision, and ability to perform minimally invasive procedures. While human surgeons still make critical decisions, robotic surgery is commonly used for gynaecological, prostate, and head and neck procedures.
Treatment Application and Diagnosis
Disease diagnosis and treatment have been a focus of AI research since at least the 1970s, when MYCIN was developed at Stanford to diagnose blood-borne bacterial infections. While MYCIN and other early rule-based systems demonstrated potential for accurate diagnosis and treatment, they were not widely adopted in clinical practice. Their performance was inferior to that of human diagnosticians and poorly integrated with clinician workflows and medical record systems.
Medical Imaging
In medical imaging, AI can outshine human experts in identifying health conditions. Deep learning algorithms can analyze MR, CT, and mammogram scans against vast databases of similar cases, detecting diseases in their early stages. For example, researchers at Kaunas University of Technology in Lithuania developed a new AI algorithm that can predict the onset of Alzheimer’s disease with over 99% accuracy by analyzing MRI brain scans
As demonstrated, AI is generated to transform healthcare. Beyond accelerating processes, increased AI adoption is expected to generate significant cost savings for the industry. Accenture estimates that AI applications could reduce annual healthcare costs by up to $150 billion. AI can also enhance disease diagnosis, reducing errors, subjectivity, and variability in diagnostic methods. Additionally, widespread AI usage is likely to transform the role of healthcare providers by freeing them from mundane administrative tasks.
AI can effectively leverage the vast amount of healthcare data. However, data alone is not sufficient for successful AI projects. According to an IDC study, the primary obstacle to adoption is a shortage of qualified AI specialists. This is where we can assist. With years of experience in AI development, Postindustria offers support at all levels, from data preparation and algorithm training to developing custom AI/ML solutions.
