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Top Five AI Use Cases in Medicine and Healthcare That You Should Know

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Ways technology has improved healthcare

When it comes to analyzing big data, even the best trained human specialists are no match for Artificial Intelligence (AI) and Machine Learning (ML) systems. AI is making inroads into all spheres of human activity, with medicine and healthcare being one of the leaders in terms of technology adoption. Hospitals have huge databases filled with patient data. They are a great testing ground for hot new developments in AI tech.

For example, technology is already able to discern whether a person is at an increased risk of getting cancer just by analyzing their selfie. A two-pronged algorithm that combines computer vision and machine learning checks the selfie taker’s sclera for visible signs of heightened bilirubin levels. And that is just one of the many examples of AI-assisted healthcare.

How Does AI Benefit Healthcare?

According to Business Insider, medicine and healthcare will benefit from accelerated AI adoption more than other industries. The rate at which the medical software market currently expands also supports that assessment. With such global challenges as pandemics and an aging population, the demand for healthcare services will only continue to grow, even leading to healthcare personnel shortages in the near future. AI will step in to correct that supply and demand imbalance, allowing doctors and nurses to perform their duties faster and more efficiently by taking over some of their functions.

According to McKinsey, the application of modern digital solutions will cover about 10 percent of the demand for clinician services by 2030. More optimistic estimates put that number around 20 percent.

What AI Can Offer to Medical Professionals:

  • Improved Standards of Patient Care — Diseases can be detected earlier and treated more efficiently, which raises a patient’s chances for recovery by 30 percent and more.
  • Better Decisions Supported by Data — By applying Machine Learning, AI-enhanced medical software can advise doctors on a course of treatment, giving them a more detailed clinical picture.
  • Reduced Overhead Costs — Repetitive administrative tasks like data entry and registration and scheduling appointments can be fully automated.

AI Use Cases in Healthcare Services

The wide adoption of AI can save thousands of lives and significantly improve the quality of diagnostic methods and the standards of care. While some application developments target medical professionals and hospital managers, others are meant for the general consumer market. Let’s take a look at some of the most interesting use cases of AI in healthcare.

Virtual Assistants and Healthcare Chatbots

Health apps have flooded the market with hundreds available in online stores for download. From providing fitness advice, nutrition plans, and workout programs, these apps can be used for patient prescreening. By filling in questionnaires or answering questions from a digital voice assistant, a person can provide necessary information about their health and fitness condition before being admitted to a hospital.

Virtual assistants can be especially useful for elderly patients, providing them with a high standard of care at home and monitoring their vitals without the need for a human caretaker to be present.

Similarly, chatbots can help patients by providing automated counseling sessions, scheduling meetings with human healthcare specialists, assisting in paying medical bills, and automating other requests. With AI, chatbots can become fully customizable and adapt to a patients’ concerns and needs.

Medical Imaging Analysis

AI-enhanced computer vision can speed up the process of reviewing medical scans and images to help a clinician with diagnostics. Cardiologists and radiologists can get additional insights into a patient’s condition and alerts about critical cases.

A comprehensive clinical study usually leads to the creation of dozens of images and other media, which can take hours and days to review. Properly trained MLalgorithms can deal with such situations in seconds, saving hundreds of hours that are better spent attending to patients. AI can compare a certain scan with similar images and studies to find discrepancies and patterns, prioritize health issues, and provide a concise summary.

Medicine Research and development

Powerful ML algorithms can process gigabytes of data in seconds to predict molecular structures of potential medicines that can be used to treat certain conditions. The technology behind this process is similar to AI-driven cars. A computer analyzes millions of experiments and measurements to predict how certain molecules will affect a human organism or a microbe.

This method can help find safer and more efficient solutions among thousands of patented and not yet registered drugs, which still undergo clinical trials. AI speeds up research by months and even years, which has already been proven by studies.

Medical Emergency Response

If a person experiences a sudden heart attack or another emergency, medical professionals must arrive as fast as possible. AI-enhanced software can “listen” to 911 calls in real-time, analyzing the speech and behavior patterns of callers.

It can accurately predict the condition of a person in an emergency situation and advise the medical emergency response team on how to prioritize situations. A computer can predict whether the person is going to experience a heart attack using speech recognition and detecting “red flags” — crucial factors that increase the chance of an emergency. Such software is invaluable in cases of personnel or ambulance shortage, as it allows management of limited resources efficiently.

Predictive Analytics

AI algorithms serve as a predictive analytics instrument. When used in software, these algorithms can deliver entire medical histories with relevant information in real-time to help clinicians improve their workload and workflows and make better decisions for patients.

AI can also analyze not just a patient’s history but the histories of their family members, finding chronic symptoms, genetic abnormalities, and dormant illnesses. It can assign a risk score to a not-yet diagnosed patient and advise on a course of treatment before the patient’s condition becomes life-threatening. It tracks the progression of chronic diseases like pneumonia, liver cancer, diabetes, and generates insights for clinicians.

Conclusion

Artificial Intelligence can assist clinicians by improving administrative tasks, diagnostics, providing better care standards, and finding cures for different diseases.