Artificial intelligence (AI) and its associated technologies are increasingly being utilized in healthcare, offering the potential to transform patient care and administrative processes. Studies suggest that AI can outperform humans in tasks like diagnosing diseases, such as spotting tumors on radiographs and guiding clinical trial cohort formation.
However, widespread adoption of AI in healthcare may take time due to various reasons. Despite its potential, there are barriers that need to be addressed for rapid implementation, and this article titled “Unleashing the Power of Artificial Intelligence in Healthcare” explores both the advantages and challenges of integrating AI into healthcare systems.
Understanding the Role of AI in Healthcare
Artificial intelligence encompasses a diverse set of technologies rather than a singular entity. While these technologies hold significant relevance to the healthcare industry, their applications and functions can vary greatly. In the context of healthcare, there are several specific AI technologies that are particularly crucial and deserving of attention. The following section provides definitions and descriptions of these noteworthy AI technologies in healthcare.
Machine Learning – Focus on Neural Networks and Deep Learning
Machine learning is widely used in making predictions by training models with data. In healthcare, it plays a significant role in precision medicine, where treatment plans are based on patient attributes and contextual factors. Supervised learning, a type of machine learning, relies on a training dataset with known outcomes. Neural networks, a more complex form of machine learning, have been extensively used in healthcare research for tasks like predicting the likelihood of a patient developing a specific ailment.
Natural language processing
Natural Language Processing (NLP) is an AI field focused on comprehending human language, encompassing speech recognition, text analysis, and translation. In healthcare, NLP plays a vital role in tasks such as analyzing clinical documentation, generating reports, transcribing patient interactions, and facilitating conversational AI. Statistical NLP, powered by machine learning and deep learning neural networks, has significantly enhanced language recognition accuracy, necessitating ample language data for effective training.
Rule-based expert systems
Expert systems, which rely on collections of ‘if-then’ rules, were prevalent in AI during the 1980s and continued to be widely used commercially, including in healthcare for clinical decision support. These systems involve human experts and knowledge engineers constructing rule-based frameworks within specific domains.
While effective and easy to comprehend, expert systems can become cumbersome when dealing with a large number of conflicting rules, and adapting them to changing knowledge domains can be challenging and time-consuming. Consequently, data-driven approaches and machine learning algorithms are gradually replacing expert systems in healthcare.
Physical robots have gained widespread familiarity, with industrial robots alone reaching an installation rate of over 200,000 units annually worldwide. These robots are deployed in various settings such as factories, warehouses, and hospitals to perform predefined tasks such as lifting, welding, assembly, and supply delivery.
Recent advancements have made robots more collaborative, trainable, and intelligent, as they incorporate additional AI capabilities into their operating systems. Surgical robots, first approved in the USA in 2000, enhance surgeons’ capabilities in performing precise and minimally invasive procedures across various specialties like gynecology, prostate, and head and neck surgeries. While surgical robots provide remarkable assistance, crucial decisions are still made by human surgeons.
Robotic process automation
Robotic process automation (RPA) is a cost-effective and easily programmable technology that performs structured digital tasks resembling human actions within information systems. Despite the term “robotic,” RPA solely relies on computer programs on servers, utilizing workflows, business rules, and integration with information systems to mimic intelligent user behavior.
In healthcare, RPA is applied to repetitive administrative tasks like prior authorization, patient record updates, and billing. It can also be combined with image recognition for data extraction from faxed images and input into transactional systems.
These technologies are increasingly integrated and combined, with robots incorporating AI-based capabilities and image recognition merging with RPA. This integration may pave the way for future composite solutions where these technologies intertwine seamlessly.
Innovations in Diagnosis and Treatment
The use of AI in disease diagnosis and treatment has a long history, but early rule-based systems faced challenges in integration and improvement over human diagnosticians. IBM’s Watson also encountered difficulties in specific cancer types and care processes. Implementation issues persist, including maintaining rule-based systems and handling genomic data.
However, ongoing research explores AI applications in radiological image analysis and precision medicine. Startups and tech giants develop prediction models and algorithms, but integrating AI into clinical workflows and electronic health record systems remains a significant barrier. Limited AI functions are being incorporated into some EHR systems, but broader implementation may require further advancements or extensive integration efforts.
Patient engagement and adherence applications
Patient engagement and adherence are crucial for successful health outcomes. Big data and AI technologies are increasingly used to address these challenges. While healthcare providers design care plans, patient noncompliance remains a significant obstacle. To improve patient involvement, AI and machine learning are being integrated to personalize care delivery.
Tailored interventions can be implemented using business rules engines and messaging alerts to encourage patient actions. “Choice architecture” analyzes real-world evidence and patient data from various sources to recommend treatment pathways, empowering providers, patients, and care coordinators in making informed decisions. These AI-powered recommendations enhance patient engagement and adherence, leading to improved health outcomes.
AI has significant potential to streamline administrative tasks in healthcare. Technologies like Robotic Process Automation (RPA) reduce time spent on activities such as claims processing, clinical documentation, revenue cycle management, and medical records management. RPA addresses efficiency needs in healthcare organizations.
Chatbots with Natural Language Processing (NLP) support patient interaction, mental health, and telehealth, but concerns exist about confidentiality and usability. Machine learning aids claims and payment administration, identifying errors and incorrect claims. These AI-driven solutions save time, money, and effort, ensuring accurate claims processing and reducing financial discrepancies for insurers, governments, and healthcare providers.