What Role Can AI Play in Healthcare

Artificial Intelligence is revolutionising - and strengthening - modern healthcare through technologies that can predict, grasp, learn, and act, whether it’s employed to identify new relationships between genetic codes or to control surgery-assisting robots. It can detect minor patterns that humans would completely overlook. This blog explores and discusses the various modern applications of AI in the health sector. 

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Particularly, it focuses on the three most emerging areas of ai-powered healthcare - 

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1. AI-led drug discovery - It can be seen that pharmaceutical firms have benefited from AI in healthcare by speeding up their drug discovery process and automating target identification. 
2. Clinical Trials - Artificial Intelligence is also helping eliminate time-consuming data monitoring methods. AI-assisted clinical trials are capable of handling massive volumes of data and producing highly accurate results.
3. Patient Care - Medical AI companies develop systems that assist patients at every level. Patient’s medical data is also analysed by clinical intelligence, which provides insights to help them improve their quality of life - this is what wekare.ai does in a nutshell. 

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For good healthcare and better health 

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The healthcare industry is in the midst of a transformation. The causes of this revolution are rising total healthcare costs and a growing lack of healthcare experts. As a result, the healthcare industry is looking to implement new information technology-based solutions processes that can cut costs and give solutions to these rising difficulties. Healthcare systems around the world face huge issues, including a lack of access, high costs, waste, and an older population. Pandemics like the coronavirus (COVID-19) put a strain on healthcare systems, resulting in a lack of protective equipment, insufficient or erroneous diagnostic tests, overworked physicians, and a lack of information exchange, to mention a few consequences. 

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More crucially, a healthcare catastrophe like COVID-19 or the development of the human immunodeficiency virus (HIV) in the 1980s exposes the stark reality of our healthcare systems’ flaws. As healthcare crises exacerbate current difficulties, we can reinvent and actualise systems of care and back office health systems, such as inequitable healthcare access, that there are not enough on-demand healthcare services, high costs, and a lack of price transparency. 

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Technique! Thy name is technology... 

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Technological breakthroughs are being adopted slowly. Burnout among healthcare practitioners is a result of physicians’ incapacity to keep up with the latest breakthroughs in medicine due to the large amount of data to be assimilated. As we focus on these issues, we should keep in mind that they are interconnected, providing the impression that healthcare is difficult when, in fact, it is because of complex systems. This isn’t easy, to suggest providing outstanding healthcare is not difficult; nevertheless, we can create a system with less complication, resulting in better care and a system that works for everyone. AI should be a critical enabler of healthcare simplification and the development of intelligent care systems. The COVID-19 problem demonstrates how AI may be used for a variety of purposes, including diagnoses and treatment decision assistance, as we all contact tracing and the deployment of AI-driven technologies. 

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Each doctor’s accomplishments and failures must be learned via experience before becoming part of the standard of care and best practices. Doctors gain knowledge from other doctors, research studies, and medication and device businesses that promote goods, and their triumphs and mistakes with their patients. Each doctor’s error is to be identified and corrected, often at the expense of their patients. This form of learning represents human nature, and physicians are not immune to our brains’ and learning systems’ hard-wiring. 

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Provier’s prejudices 

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The issue is that the provider’s prejudice and limits are a result of this anecdotal experience. In reality, based on their observation, some physicians may mistakenly decide to believe that a diagnosis is correct or that a therapy is effective, even though it is counter to evidence backed by studies or the results of thousands of patients. Sometimes a clinician is just uninformed of new therapeutic care paths or better diagnostic modalities as a result of studies and data. To optimise reimbursement, clinicians must see as many patients as possible in the present medical environment. This leaves clinicians with little time to focus on secondary patient care chores, let alone stay current on medical breakthroughs. 

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Doctors, on the other hand, now have immediate access to the insights and best practices of hundreds of cohorts, and they don’t have to wait for best practices to be formalised into national standards of care. We can modify this calculus even more and act at a faster scale using AI than a particular physician or institution could. Health professionals use the expertise of large numbers of clinical studies, the lessons of large numbers of patient treatment routes, and the cumulative experience of thousands of clinicians because they didn’t have it at their fingertips. 

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This necessitates the use of technology, specifically artificial intelligence. Clinicians are as vulnerable to cognitive biases as humans. But by offering a technological balancer in the knowledge base of providers, we can reduce, if not eradicate, the effects of such biases in AI.  

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