Artificial intelligence (AI) and related technologies have significantly expanded their presence across various sectors, including business, society, and healthcare. The potential of AI to revolutionize patient care and streamline administrative processes in healthcare organizations is immense.
Research studies have shown that AI can perform on par with or even outperform humans in diagnosing diseases and executing healthcare tasks. For instance, AI algorithms have demonstrated superior capabilities to radiologists in detecting malignant tumors and guiding researchers in clinical trial cohorts.
However, the full integration of AI into broader medical processes is expected to take several years. In this article, we explore the potential of AI in automating aspects of care and the barriers hindering its rapid implementation in healthcare.
AI encompasses various technologies that are relevant to healthcare. These include machine learning (neural networks and deep learning), natural language processing (NLP), rule-based expert systems, and physical robots.
Machine learning is a statistical technique employed to fit models to data, and it is commonly used in AI applications. In healthcare, machine learning finds significant applications in precision medicine, where it predicts treatment success based on patient attributes and context.
Traditional machine learning algorithms help make accurate predictions, while neural networks, a more complex form of machine learning, are utilized for disease prediction and categorization tasks. Deep learning, a subset of neural networks, involves models with multiple levels of features that predict outcomes. Deep learning algorithms are commonly used in radiology image analysis and oncology. Additionally, deep learning is employed in speech recognition, falling under the umbrella of NLP.
NLP encompasses various applications, such as speech recognition, text analysis, translation, and clinical documentation analysis. With NLP, AI systems can analyze and interpret clinical notes, enabling the accurate extraction of critical information. Speech and text recognition technologies are already being utilized for patient communication and capturing clinical notes, with their usage expected to increase further.
Rule-based expert systems were widely used in the past for clinical decision support. However, they are gradually replaced by data-driven approaches and machine learning algorithms. These systems utilize predefined rules and logical inferences to provide recommendations based on input data.
Physical robots play a significant role in several industries, including healthcare. They are becoming increasingly collaborative and intelligent, providing valuable enhancements to surgeons during precise and minimally invasive procedures. Surgical robots are used in various procedures, such as gynecologic surgery, prostate surgery, and head and neck surgery. However, it is essential to note that human surgeons still make critical decisions during these operations.Robotic Process Automation: Streamlining Administrative Tasks
Robotic Process Automation (RPA) is a technology that performs structured digital tasks to automate administrative processes. Unlike physical robots, RPA relies on computer programs running on servers.
RPA is inexpensive, easy to program, and transparent in its actions compared to other forms of AI. RPA is particularly useful in healthcare for repetitive tasks like prior authorization, patient record updates, and billing. Combining RPA with image recognition can extract data from faxed images and seamlessly input it into transactional systems, further enhancing its utility.
AI has long been focused on improving the diagnosis and treatment of diseases. Since the 1970s, early rule-based systems like MYCIN were developed to diagnose bacterial infections. IBM's Watson gained significant attention for its potential in precision medicine. However, integrating AI systems like Watson into existing care processes and systems has proven challenging. AI-based diagnosis
And treatment capabilities often rely on radiological image analysis, retinal scanning, or genomic-based precision medicine.
While AI has made remarkable advancements in these areas, its implementation in clinical practice still needs to be improved. Many developments are primarily found in research labs and tech firms, and integrating AI-based recommendations into clinical workflows and electronic health record (EHR) systems presents several challenges.
Adopting AI in clinical practice encounters several hurdles, which must be addressed to realize its full potential. These challenges include regulatory approval, integration with EHR systems, standardization, clinician training, funding, and regular updates. Each of these aspects plays a crucial role in successfully implementing AI technologies in healthcare.
Furthermore, the lack of labeled data, changes in medical regulations, and the involvement of health insurance providers are essential factors in advancing AI-based image analysis. With adequate data and regulatory frameworks, the widespread clinical use of AI systems for image recognition tasks, such as radiology, may be completed on time.
The use of AI in healthcare raises important ethical considerations. Historically, humans have made healthcare decisions, often guided by their experience and judgment. Introducing AI systems in decision-making processes raises questions of accountability, transparency, permission, and privacy.
Deep learning algorithms, particularly in image analysis, need help with interpretability and explainability. Patients may want to understand why a particular diagnosis is made based on an image. Still, explanations may need to be more readily available from deep learning algorithms or physicians.
Mistakes and errors in AI systems present another ethical concern. Like any technology, AI systems can make errors in patient diagnosis and treatment. Establishing accountability for these mistakes can be challenging, and patients may prefer to receive medical information from empathetic clinicians rather than relying solely on machine learning systems.
The future of AI in healthcare holds great promise. AI will be critical in shaping future healthcare offerings, particularly in precision medicine development. Machine learning algorithms will continue to evolve, improving the accuracy of diagnosis and treatment recommendations. AI's potential in imaging, speech recognition, and text analysis will further revolutionize healthcare practices.
Despite the challenges faced in AI adoption, significant progress is expected within the next decade. While the use of AI in clinical practice may be limited in the next five years, more extensive integration is anticipated within the next ten years. As AI technology matures and becomes more accessible, its benefits will become increasingly evident in improving patient outcomes and streamlining healthcare processes.
By collaborating with AI systems, healthcare providers can focus on tasks that require empathy, persuasion, and big-picture integration, ensuring the highest quality of care for patients.
Sources: ibm.com / ncbi.nlm.nih.gov / arm.com / foreseemed.com / builtin.com
