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Revolutionizing healthcare: the role of artificial intelligence in clinical practice Full Text

10 Benefits of Artificial Intelligence in Healthcare

benefits of artificial intelligence in healthcare

In fact, AI is already being used in healthcare patients, for drug “discovery and development,” to improve physician-patient communication, and to transcribe medical documents. From analyzing medical images to identifying patterns in patient data, AI has the potential to revolutionize the way healthcare is delivered. To mitigate these risks, health care providers should continue to take the traditional steps to ensure the security and privacy of patient data.

Human contribution to the design and application of AI tools is subject to bias and could be amplified by AI if not closely monitored [113]. The AI-generated data and/or analysis could be realistic and convincing; however, hallucination could also be a major issue which is the tendency to fabricate and create false information that cannot be supported by existing evidence [114]. This can be particularly problematic regarding sensitive areas such as patient care.

Typical Applications of AI in Healthcare

This can help doctors make more accurate diagnoses and provide more effective treatments. Beyond clinical applications, AI is streamlining administrative tasks in healthcare facilities. From appointment scheduling to billing and managing medical records, AI-driven automation ensures efficiency and accuracy. Furthermore, as an AI technology, sentiment analysis can help identify areas for improvement in healthcare services and enable healthcare providers to respond to patient concerns in real-time. As AI-based solutions become more widespread in the healthcare industry, there is a risk of job displacement and a change in the role of healthcare workers.


For example, almost none of the machine learning tools developed to tackle COVID-19-related challenges had a significant impact. OM1’s platform, PhenOM™, uses AI and OM1’s health data sets to identify risks and opportunities. This lets it give personalized healthcare insights, impacting everything from research to clinical decision-making. The use of AI in healthcare continues to gain momentum with studies confirming its effectiveness in diagnosing some chronic illnesses, increasing staff efficiency, and improving the quality of care while optimizing resources.

Career development

It is also crucial in streamlining and optimizing healthcare administration procedures. A second, but equally important subset of AI known as natural language processing, or NLP, makes it easier than ever to automate many of the complex, time-consuming, repetitive tasks that eat up a lot of resources in health care administration. With NLP, health care organizations can dramatically increase efficiency and accuracy in critical areas of care. Artificial intelligence (AI) in health care is the use of algorithms and software in the analysis, interpretation and comprehension of complicated medical and health care data to ultimately improve treatment options and outcomes.

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By tailoring treatment decisions to the specific genetic characteristics of each patient’s tumor, the center hopes to enhance treatment efficacy, reduce adverse effects, and advance pediatric oncology research. It may also be used to incorporate information from different sources depending on study. Software has been developed to help with specific diseases such as childhood cancer. The integration of application of AI in healthcare industry makes it easier to improve the patient-doctor relationship. Medical professionals can receive real-time updates via mobile alerts on patient status, emergency, and any other changes.

Babylon, an interactive symptom checker app, is a fantastic illustration of how AI in medicine can enhance the experience for the patient. According to Accenture, the healthcare AI market will grow to $6.6 billion by 2021 at a whopping CAGR of 40%. Another risk is the unique privacy attacks that AI algorithms may be subject to, including membership inference, reconstruction, and property inference attacks.

  • Doshi-Velez’s work centers on “interpretable AI” and optimizing how doctors and patients can put it to work to improve health.
  • AI algorithms are able to predict and diagnose diseases quicker than doctors with minimal error risk in comparison to humans, provided that the data quality is good.
  • One of the main applications of AI in this field is virtual simulation and training, allowing students to practice complex procedures on virtual patients without risking harm to real patients.
  • Medical research bodies like the Childhood Cancer Data Lab are developing useful software for medical practitioners to better navigate wide collections of data.

The technology facilitates the identification of patterns in enormous data sets containing medical records and genetic information, thereby facilitating the discovery of disease-mutation associations. AI can tell doctors what happens in the cell when DNA is modified through therapeutic or natural genetic variation. For example, they can find connections between DNA flaws and other illnesses that haven’t been known before. This branch of Artificial Intelligence uses an algorithm to analyze past data, current information, and interactions.

Furthermore, these tools can always be available, making it easier for patients to access healthcare when needed [84]. Another medical service that an AI-driven phone application can provide is triaging patients and finding out how urgent their problem is, based on the entered symptoms into the app. The National Health Service (NHS) has tested this app in north London, and now about 1.2 million people are using this AI chatbot to answer their questions instead of calling the NHS non-emergency number [85].

Expert systems require human experts and knowledge engineers to construct a series of rules in a particular knowledge domain. However, when the number of rules is large (usually over several thousand) and the rules begin to conflict with each other, they tend to break down. Moreover, if the knowledge domain changes, changing the rules can be difficult and time-consuming. They are slowly being replaced in healthcare by more approaches based on data and machine learning algorithms. There are already a number of research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks, such as diagnosing disease.

Some examples of AI in healthcare

When adverse health events occur, a trip to the emergency room or hospital admission is not uncommon, and both are very expensive for patients and insurers. When AI gets used to monitor patients, it serves as an early warning for patients. With this information, patients can seek medical attention outside of the hospital through their physicians. For the most part, the view is accurate, but there are more reasons to be optimistic than anxious about the improvements it brings. Few can argue that the advancement of machine learning and AI are fundamentally transforming every industry. Although AI can make excellent and precise predictions, it is often impossible to understand how it arrived at these results.

benefits of artificial intelligence in healthcare

Moreover, there are ethical considerations regarding the use of AI in exams, such as potential algorithmic bias, privacy issues, and the impact on human jobs. To address these issues, universities must carefully consider the benefits and drawbacks of AI integration and implement strict policies to ensure fair and ethical evaluation of medical students. It is also important for universities to educate students on the importance of academic integrity and ethical considerations related to AI use. When you look back to early-2020, when the pandemic hit, video doctor visits were met with some uncertainty. Patients didn’t understand how a doctor can take blood pressure or evaluate conditions efficiently if they were not face-to-face.

Streamlining Tasks

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