Artificial intelligence (AI) and similar technologies are becoming more common in business and society, and now even integrating in healthcare. AI-related technologies have the ability to change and modify many aspects of patient care as well as administrative processes inside provider, payer, and pharmaceutical companies. Recent research has already discovered that AI can outperform humans in critical healthcare activities such as illness diagnosis. Algorithms are already outperforming clinicians in terms of diagnosing deadly cancers and directing researchers on how to assemble cohorts for costly clinical trials. This essay addresses the potential for AI to automate portions of treatment and benefits of implementing AI in healthcare.
Machine Learning Advantages in Healthcare
Artificial intelligence is a collection of technologies, including big data. The majority of these technologies are immediately applicable to the subject of healthcare, despite the fact that the exact procedures and tasks they assist differ significantly. AI has several advantages over conventional analytical and clinical decision-making methods. While they interact with dataset, machine learning techniques can become more exact and accurate, allowing people to acquire unparalleled insights into diagnoses, care procedures, therapy variability, and patient outcomes.
Unlike the previous generation of AI systems, which depended on human curation of medical knowledge and the construction of rigorous decision criteria, contemporary AI research has used machine-learning approaches to detect patterns in data that may account for complicated relationships. Basic machine-learning algorithms are classified into two sorts based on the types of tasks they attempt to solve: supervised and unsupervised (Yu et al., 2018). Supervised machine-learning approaches collect a large number of ‘training’ examples using inputs (such as fundus pictures) and the output signal labels (Yu et al., 2018). The algorithm learns to provide the proper output level and input on new cases by analyzing the patterns in all of the tagged input–output pairs. The effective application of deep learning — which includes training a learning algorithm with several layers (that is, a ‘deep’ neural network) on massive datasets — to enormous sources of labelled data has fueled the recent resurgence in AI to a considerable extent (Yu et al., 2018). Deep learning has showed significant advances in picture categorization problems since the beginning of the last decade.
Starting from the 1950s, AI researchers have sought to understand human language. Natural language processing (NLP) applications include speech recognition, discourse analysis, translation, and other language-related aims (Davenport & Kalakota, 2019). There are two approaches: statistical NLP and semantic NLP. Statistical NLP is known as machine (particularly deep learning neural networks) and has led to a recent boost in recognition accuracy (Davenport & Kalakota, 2019). It is necessary to have a substantial ‘corpus’ or body of language from which to learn. The most prevalent applications of NLP in healthcare include the creation, understanding, and classification of patient data, as well as published research (Davenport & Kalakota, 2019). NLP systems may analyze unorganized clinical information on patients, create reports (e.g., on radiological exams), transcribe patient conversations, and perform conversational AI. Such information which is done by analysis of big data may help professionals to increase accuracy of assigning treatment and identifying diseases.
The implementation of AI in healthcare can help to mitigate antibiotic resistance. Today antibiotic resistance is an increasing hazard to communities worldwide, as abuse of these vital antibiotics promotes the emergence of superbugs that are no longer treatable. Multidrug resistant pathogens may wreak havoc in hospitals and take hundreds of lives each year. C. difficile, for example, costs the US healthcare system over $5 billion per year and claims more than 30,000 lives (Rong et al., 2020). Data from electronic health records can help discover infection trends and identify people at risk before symptoms appear. Using machine learning and AI techniques to drive these insights can improve their accuracy and give healthcare practitioners with faster, more accurate warnings.
Robots and Healthcare
Aside from analysing data, artificial intelligence is associated with robots that can be used in healthcare effectively. Robots have lately grown more collaborative with people and are easier to teach by guiding them through a desired job. They are also growing smarter as more AI capabilities are integrated into their main operating system. The same advancements in intelligence that was seen in various areas of AI are likely to be applied into physical robots over time. Surgical robots were initially permitted in the United States at the beginning of 21st century. They provide help for surgeons allowing them to see better, make more accurate and least invasive incisions, suture wounds, and including other important procedures (Davenport & Kalakota, 2019). However, human surgeons continue to make important judgments and their importance is still relevant.. Robotic surgery is commonly used in different type of surgery, such as prostate surgery, and head and neck surgery.
In the field of supported living for the elderly and disabled, AI technologies combined with smart robotic systems are leading the way for better life quality. Recently, an overview of smart home functionalities and tools for individuals with loss of autonomy (PLA) was released, as well as sophisticated solution approaches wireless sensing networks, data mining, and AI (Rong et al., 2020). The neural network is now learned to identify human facial expressions as commands using certain image-processing processes. Furthermore, human-machine interfaces based on facial movement analysis enable persons with impairments to control wheelchairs and robot support vehicles without the use of a joystick or detectors attached to the body.
In addition, there is robotic process automation which conducts organized digital activities for administrative reasons, such as those using information systems, as though they are performed by a human following a script or set of rules. They are affordable, simple to program, and transparent in their activities when compared to other kinds of AI. Automation of robots does not explicitly use robots, but rather computer applications running on servers (Davenport & Kalakota, 2019). To serve as a semi-intelligent user of the systems, it uses a combination of processes, business rules, and ‘presentation layer’ connection with information systems. They are employed in healthcare for repetitive operations such as prior authorization, updating patient information, and billing. They may be used to extract data from faxed photographs and feed it into transactional systems when paired with other capabilities like image recognition.
Conclusion
To conclude, AI has been employed in a variety of technological disciplines, including IoT computer vision automated vehicles natural language processing, and robotics, due to the rapid growth of AI technology and computer technologies. Most intriguingly, biomedical researchers have been actively attempting to use AI to enhance analysis and treatment outcomes, hence increasing the overall efficacy of the healthcare business. Healthcare should strive to become more personalised, predictive, preventive, and interactive, and AI may help in these areas significantly.
References
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94.
Rong, G., Mendez, A., Assi, E. B., Zhao, B., & Sawan, M. (2020). Artificial intelligence in healthcare: review and prediction case studies. Engineering, 6(3), 291-301.
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719-731.