Artificial intelligence in healthcare relies on analyzing and interpreting vast amounts of data to help doctors make better decisions, manage patient data and information, create personalized medical plans from complex data sets and even discover new medicines. Huge amounts of data need to be collected to train machine learning and deploy AI in healthcare. There are various amounts of health data in this area, and artificial intelligence needs to sort and present this data in order to learn and build networks. [Sources: 0, 1, 9]
Strong AI techniques can reveal relevant information hidden in vast quantities of data, guiding clinical decision-making through relevant clinical questions. In this section, we will review AI devices and techniques that are useful in media applications. ML is a well constructed data analytical algorithm that extracts features from data. [Sources: 12] Integrating AI in the healthcare ecosystem provides a variety of benefits, including automating tasks and analyzing large patient data to ensure better healthcare at lower costs. Machine learning is a technique that uses analytical algorithms to filter out certain patient characteristics, including information collected from patient visits and physicians. Deep learning is a subset of artificial intelligence designed to identify patterns using algorithms and data to give healthcare providers automated insights. [Sources: 9, 14] Machine learning algorithms can facilitate physicians “use of electronic medical records (EHRs) and management systems, provide clinical decision support, automate image analysis, and integrate telemedicine technologies. Using artificial intelligence to improve EHRs and management can improve patient care, reduce healthcare administrative costs, and streamline operations. Machine learning can provide added value through predictive analysis, translating data for decision-makers, identifying process gaps, and improving the overall operation of healthcare. [Sources: 8] Artificial intelligence in healthcare is a general term used to describe the use of machine learning algorithms and software (AI) that mimics human cognition by analyzing, presenting and understanding complex medical and medical data. The combination of machine learning, health informatics and predictive analytics offers the opportunity to improve health processes and transform tools to support clinical decision making to help improve patient outcomes. A study of what machine learning can do in healthcare shows that technological innovations can lead to effective, holistic care strategies that improve patient outcomes. [Sources: 0, 8] AI is entering healthcare to play an important role in automating the drudgery and routine tasks of medical practice and managing patients and medical resources. As developers develop AI systems to take on this new role, several risks and challenges have popped up — including the risk of injury to patients from errors in the AI system — the risk to patient privacy arising from the data collection and AI conclusions — and much more. Potential solutions are complex and include investments in infrastructure, high-quality representative data, joint oversight by the Food and Drug Administration and other healthcare stakeholders and changes in medical education to prepare providers for the changing roles in an evolving system. [Sources: 10] For example, medical leaders can design meaningful and explicable AI that provides insights and information to support decision-making and deepen medical professionals “understanding of their patients. I predict that medical organizations will integrate and embrace technologies that enable and encompass algorithmic solutions such as smartphones and tablets, followed by pattern recognition software and machines that generate best practices for individual patients. Over time, patients will be able to use a variety of AI tools to take care of themselves, while today they are dealing with many other aspects of their lives. [Sources: 3, 13] The growing availability of health data and the rapid development of big data analysis methods have enabled the recent successful application of AI in healthcare. We believe that humans will not be replaced in the foreseeable future by machines, but artificial intelligence can help doctors make better clinical decisions and replace human judgment in certain areas of health care, particularly in radiology. Healthcare AI has proven useful in clinical decision support, helping doctors make better decisions by recognizing patterns and registering health complications that are registered in the human brain. [Sources: 1, 12] While research into the use of AI in healthcaaims to confirm its effectiveness in improving patient outcomes, widespread use of AI and its use are leading to several new types of risks for patients and healthcare providers such as algorithmic distortions (such as implications that do not resuscitate”) and other machine morality issues. The most obvious risk is that AI systems may be wrong, resulting in patient injuries and other health problems. Many injuries are caused by medical errors in today’s healthcare system due to the inclusion of AI. [Sources: 0, 10] Steven A. Wartman and C. Donald Combs argue that medical education should be transformed from a focus on recall of knowledge to educating students to interact with and handle smart machines, and this requires careful attention to the ethical and clinical complexities that can arise between patients, caregivers and machines. In their commentary on the introduction of intelligent computer algorithms into the medicine “workflows”, the authors emphasize the importance of the users and technical knowledge when interpreting AI-guided test results and identifying potential ethical dilemmas. [Sources: 7] Leonard Davolio, Ph.D., founder and CEO of Cyft, a company that helps companies to adjust their workflows and systems to reduce costs and improve outcomes, sees artificial intelligence not as a solution, but as a capability that enables us to learn in ways that we have never had before. A report by Chilmark Research further notes that precision medicine, accompanied by machine learning and artificial intelligence, can fully take advantage of the ability to analyze large data sets for doctors and medical researchers. [Sources: 2] Health care providers can use these insights to improve patients “journey through the health system and free them from traditional confusion. The use of artificial intelligence is revolutionary in healthcare and can offer significant efficiency gains compared to patient care. [Sources: 5, 6]
Reference
[0]: https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare [1]: https://healthcareweekly.com/artificial-intelligence-in-healthcare/ [2]: https://www.himss.org/resources/ai-healthcare-how-its-changing-industry [3]: https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/transforming-healthcare-with-ai [4]: https://www.managedhealthcareexecutive.com/view/artificial-intelligence-saves-payers-time-and-money [5]: https://builtin.com/artificial-intelligence/artificial-intelligence-healthcare [6]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/ [7]: https://journalofethics.ama-assn.org/article/ethical-dimensions-using-artificial-intelligence-health-care/2019-02 [8]: https://healthinformatics.uic.edu/blog/machine-learning-in-healthcare/ [9]: https://www.healthrecoverysolutions.com/blog/the-growth-of-artificial-intelligence-ai-in-healthcare [10]: https://www.brookings.edu/research/risks-and-remedies-for-artificial-intelligence-in-health-care/ [11]: https://www.modernhealthcare.com/indepth/artificial-intelligence-in-healthcare-makes-slow-impact [12]: https://svn.bmj.com/content/2/4/230 [13]: https://www.forbes.com/sites/robertpearl/2018/03/13/artificial-intelligence-in-healthcare/ [14]: https://www.businessinsider.com/artificial-intelligence-healthcare
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