How AI is transforming healthcare industry

The medical industry is utilising artificial intelligence (AI) to enhance the accuracy of diagnoses and improve the overall patient experience. Advanced computer programs and informatics are employed to process large amounts of medical data through machine learning models, resulting in improved medical outcomes, clinical settings, and research quality. 

In this article, we will explore the various ways AI is applied in the medical field and the benefits it offers.

 

AI in diagnosis and disease detection

Since AI can work round-the-clock, it provides more information than human beings about patients who are under critical care and need intensive observation. Modern machine learning models can consistently monitor several vital signs of patients in the intensive care unit. By analysing this data, these models can generate more precise diagnosis outcomes than human observation alone. In critical illnesses such as cancer, Alzheimer's, and sepsis, the accuracy of diagnosis has been enhanced by over 40% through the use of AI.

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AI in medical imaging

AI has the potential to address the significant shortage of qualified radiologists in countries like India. When diagnosing certain types of cancer or cardiovascular diseases, doctors must examine thousands of images or large amounts of data, which can be time-consuming and overwhelming. In such situations, overworked doctors may make errors in diagnosis. AI, on the other hand, can quickly analyse vast amounts of images and medical history data, making it a valuable tool for detecting abnormalities and tracking critical patient information.

AI in personalised disease treatment

Patients who receive extended treatment can benefit from personalised care through the use of AI. In some cases, doctors may struggle to keep track of a patient's medical history, particularly when dealing with chronic or multiple illnesses. AI models have the advantage of being able to recall extensive patient health records, enabling them to provide real-time, personalised treatment recommendations. Patients can access 24/7 AI assistance, which can inform them about their medical history, as well as their individual preferences and needs.

AI in drug trials 

Developing new drugs is a challenging and costly process, particularly when it comes to clinical trials. It can take several years of trials and significant financial investment to bring a new drug to market, with no guarantee of success. AI technology has the potential to streamline the process by analysing the medical histories of thousands of patients simultaneously, allowing researchers to identify ideal candidates for clinical trials. Once patients are selected for the trial, AI can track their responses to treatment with greater accuracy and efficiency than human assistants, significantly reducing trial time and costs.

Maintain electronic health records

Typing electronic health records on a computer screen can be a tedious task for healthcare professionals and increases the risk of human errors that could result in incorrect diagnoses or treatments. However, with AI-backed speech recognition technology, interactions with patients, diagnoses, and treatments can be recorded in real-time and stored for future reference. This eliminates the need for manual data entry and ensures accurate documentation. Additionally, when the same patient returns to the same healthcare professional, their previous records can be easily retrieved and updated, leading to more efficient and accurate patient care.

Benefits of AI in medical field

Informed decisions 

Machine learning algorithms can offer medical professionals contextual and more informed insights to guide care decisions. By reducing research time and increasing precision, AI can provide evidence-based insights more quickly. Professionals can use these insights in real-time while interacting with patients, enabling them to make more informed decisions about patient care.

Error reduction

While making a decision about a disease, AI algorithms analyse a number of similar cases, plus the machine learning process fills the system with new data. As a result, the system becomes increasingly informed and accurate, reducing the margin of error over time.

Cost reduction

AI can contribute to reducing healthcare costs in various ways. Customised health assistance, reduced diagnostic and medication errors, more efficient clinical and administrative workflows, and the prevention of fraud are some examples of how AI can help reduce errors. 

Better doctor-patient engagement

While it is necessary to schedule appointments to meet with doctors, they cannot be available around the clock. However, AI-powered chatbots can be available at all times and can respond to basic questions, help identify early signs of a disease, and suggest changes to medication or diet. So, when you visit the doctor next time, you will not ask the basic questions from him.