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The Importance of Artificial Intelligence in Healthcare

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1. Introduction

Artificial intelligence offers an incredible opportunity to transform the healthcare system. Whether reading the ECG, doing initial diagnosis of cancer or other diseases, or providing recommendations for treatment, AI applications have started playing a crucial role. AI has been in development for more than 40 years, mainly due to the increase in data which has enabled it to be utilized for clinical use. Today, the power of AI represents an umbrella for a set of statistical methods and technologies including deep learning, machine learning, and natural language processing. These can all be used to mimic human cognition in the analysis of complex medical data. AI in health consists of using complex algorithms and software to emulate human cognition in the analysis, interpretation, and comprehension of complex medical and health data. It can be used simply to document patient health records, or to perform more complex tasks such as implementing an intervention in a patient’s treatment. AI can accelerate clinical research and has been key in changing the medical profession by developing a scientific basis for clinical practice and healthcare delivery. AI in healthcare can provide a great opportunity to reform and transform healthcare, and it will be a great complement to the current healthcare system. This is largely driven by the increase in available data, increasing computational power, and theoretical advances.

2. Benefits of Artificial Intelligence in Healthcare

AI is also able to integrate and interpret data from a variety of sources. This ability can be used to benefit the epidemiology of a population. By analysing data from sources such as social media or online search queries, it is possible to predict disease outbreaks or trends. For example, Google recently developed a model that predicts the spread of the flu in real time. Another way that AI can be deployed to use data is the automation of administrative tasks and analysis. This will reduce the burden of monotonous tasks on healthcare staff and reduce costs of hiring admin services.

As mentioned, the scope that AI can be applied to is vast. One of the major benefits is that it can automate tasks. As an example, it can be used to automate the analysis of images from MRI scans. It is estimated that there are millions of diagnostic imaging errors each year in the UK, this technology could reduce these substantially. It can also be applied to personalising patient care, using the large amount of data AI can interpret to tailor treatment and guidelines for patients. This leads to better patient outcomes and safer care. On a larger scale it can be applied to the management of chronic diseases. Simulation of disease models can be generated to understand the progression of a disease, thus allowing for early intervention and preventative measures.

Artificial intelligence in healthcare is an up and coming field that is proving to be a game changer in many areas of the medical sector. Its use is not only cost effective, but it also provides highly efficient results and better patient outcomes. There are a variety of benefits that have been found from using AI in healthcare. These benefits are wide ranging and extensive. They’re not limited to one condition or one field, AI is able to be applied to most areas and types of healthcare.

3. Challenges and Ethical Considerations

Current healthcare regulations hold the requirement for an explanation of exactly what factors are taken into account when a decision is made. “Right to explanation” is now part of the EU general data protection regulation which gives the citizens the right to contest any decision made about them that has legal or otherwise significant effect. This may prove to be difficult with AI whose methods of deep learning can produce an answer without a method to trace exactly how and why this prediction was made. Failure to provide an explanation could lead to the mistrust of AI systems by doctors and would likely hinder acceptance and uptake of the new technology.

There are a number of new ethical and legal questions that arise with the development and use of AI in healthcare. The lack of immediate experience with using the new learning techniques may result in a lack of knowledge concerning how to address these sorts of issues. By training AI through the use of prior medical records and the pattern recognition, it is possible to be discriminating, even unintentionally, against certain minority groups. Decisions made by AI by weighing up probabilities may also prove to be problematic within an individual patient setting where a binary yes or no would be expected from a healthcare professional. These patterns of decision making could introduce uncertainty and inconsistency when the decisions made by AI on similar cases are not consistent and therefore hard to justify to the patient by healthcare professionals.

4. Implementation Strategies

Simulation is also an option to test AI theories without putting them to actual clinical use. The virtual assistant agent is controlled by cognitive heuristics and clinical algorithms. The first real test towards AI clinical implementation starts with contemporary medicine in an attempt to automate a physician with the use of an AI tool. These can be simple to very complex, such as decision support systems with a vast range of variety. A great example of this would be an expert system determining the best treatments for specific diagnoses. Following this, there should be continued research and development into AI applications to provide continuing education and general tools for evidence-based medicine and improving patient care.

There are many paths like a first step in the implementation attribution that lead towards a future in AI in healthcare. Today, natural language processing allows a patient to have a conversation with a computer-generated “doctor.” This wonderful tool takes in direct input from the patient, answering questions and taking in symptoms, thus providing a diagnosis. The most rational evidence-based AI system is then cited in a study where informatics are used to track down implicit patterns in clinical data to improve patient care. The system was self-sufficient with using machine learning technology, where it used trial and error to understand the surrounding environment. It goes on to apply the said gained knowledge to restructure an existing process in order to optimize the task (Kaplan, 2001).

5. Conclusion

In conclusion, artificial intelligence has become an essential part of the healthcare industry. The advantages it has provided are truly remarkable, since AI has managed to solve problems which were previously believed to be insoluble. AI systems give clinicians access to more targeted information and support, potentially leading to more precise diagnosis and treatment. Take IBM Watson, which recommends evidence-based treatment options for cancer patients based on the individuals’ clinical information. Or Google’s DeepMind, which has created an app that provides digital eye scans and uses the results to recommend treatment for patients with diabetes-related vision loss. AI has also improved the speed and efficiency of healthcare delivery. Robot-assisted surgery and virtual nursing assistants are all examples of how AI is being used to automate tasks. An analysis of the robot-assisted surgery market suggests that the systems are cost-effective in the long run and could lead to shorter hospital stays and a faster return to daily activities. Ultimately, AI has the potential to provide more cost-effective and personalized care. An example of this is a trial which used AI chatbots to reach out to heart disease patients; the chatbots provided supportive conversations and reminders to stick with treatment plans and managed to lower the patients’ cholesterol and blood pressure. AI consulted care is also growing in popularity, since patients are now more eager to trust a system that is data driven and statistically validated. Foreign companies have recognized the potential and are ready to outsource the IT development and processing of information for precision medicine in the United States. AI has already made a positive impact in specific care services; its influence will only continue to grow as it becomes more refined and accessible. In saying that, the implementation of AI in healthcare does not come without concerns or limitations. Unquestionably the most debated issue is the risk to privacy and the security of patient data. Mishandling of the data or the results provided by AI systems could have serious consequences. Measures must be adapted to ensure the patients are informed on who can access their data, where it will be stored and for how long. It is also important that patients have the ability to preview and contest the data to protect against false inferences. Restrictions have already been applied to some AI technologies, an example being a government order for Watson to only use public data for its cancer treatment recommendations. Another well-publicized issue is the potential for job loss among healthcare workers. Although AI systems are being designed to augment the work of professionals, there is strong evidence on automation’s capability to do a high proportion of the tasks currently performed by doctors and other clinicians. This could obviously have a detrimental effect in a jobs market already facing shortage in many health professions, however the possible cost savings and benefits are likely to influence the further development and implementation of AI. Other more specific concerns lie in the risks associated with new treatments or methods influenced by AI and the danger of over-reliance on AI systems, since nothing is infallible and humans are required to interpret and carry out the recommendations provided. Limitations of AI in healthcare are mostly centered round the fact that it is still early days in the development and relatively little has been implemented into standard clinical practice. However, AI is a fast-moving and growing field and the evidence of its potential impact illustrates the need for consideration of the ramifications it may bring.

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