May 20, 2024

In order to improve patient outcomes and health outcomes, artificial intelligence in medicine uses machine learning models to assist in the processing of medical data and provide key insights to medical practitioners.

Read More: Medical AI

How is AI applied in the medical field?

Recent developments in informatics and computer science have made artificial intelligence (AI) a vital component of contemporary healthcare. Medical practitioners are supported by AI algorithms and other AI-powered technologies in clinical settings and current research.

Currently, image analysis and clinical decision assistance are the most prevalent uses of AI in medical contexts. Clinical decision support technologies give healthcare professionals rapid access to information or research that is pertinent to their patients, assisting them in making decisions about treatments, drugs, mental health, and other patient requirements. AI systems are being utilized in medical imaging to examine CT scans, x-rays, MRIs, and other pictures to look for lesions or other discoveries that a human radiologists would overlook.

Numerous healthcare organizations worldwide began field-testing novel AI-supported solutions, such as algorithms to assist monitor patients and AI-powered tools to screen COVID-19 patients, in response to the issues the COVID-19 epidemic caused for many health systems.

The overarching guidelines for the application of AI in medicine are still being developed, as is the research and test outcomes. However, there are more and more potential for AI to help researchers, doctors, and the patients they treat. There is now little question that artificial intelligence (AI) will play a major role in shaping and enabling digital health systems that underpin modern medicine.

Medical uses of AI

AI has a lot of potential benefits for the medical field, including accelerating research and assisting doctors in making better judgments.

The following are some potential applications of AI:

AI for diagnosing and detecting diseases

AI doesn’t require sleep, in contrast to humans. Critical care patients’ vital signs might be monitored by machine learning algorithms, which could notify doctors if any risk factors worsen. Vital indicators may be tracked by medical equipment such as heart monitors, but artificial intelligence (AI) can gather the data from such devices and search for more complicated illnesses like sepsis. An IBM client has created a 75% accurate prediction AI model for severe sepsis in preterm newborns.

Tailored medical care

With virtual AI help, precision medicine may become easier to support. AI models have the capacity to learn and remember preferences, which means that they might be used to continuously offer patients personalized real-time recommendations. A healthcare system might provide patients with 24/7 access to an AI-powered virtual assistant that could respond to inquiries based on the patient’s medical history, preferences, and individual requirements, saving them the trouble of having to repeat information with a different person every time.

Medical imaging with artificial intelligence

Medical imaging is already heavily reliant on AI. Studies have shown that artificial intelligence (AI) facilitated by artificial neural networks may detect symptoms of several illnesses, including breast cancer, with an accuracy comparable to that of human radiologists. By identifying important details of a patient’s history and showing the pertinent photographs to them, AI can assist physicians in not just identifying early indicators of sickness but also in managing the enormous volume of medical images that they must monitor.

Efficient clinical trial design

During clinical trials, a significant amount of work is spent updating pertinent databases and allocating medical codes to patient outcomes. AI can expedite this procedure by offering a more rapid and sophisticated way to look up medical codes. Recently, two clients of IBM Watson Health discovered that they could cut down on medical code searches by over 70% by using AI.

Sped up the development of drugs

One of the most time-consuming and expensive phases of medication development is frequently drug discovery. AI has the potential to lower the cost of producing new drugs in two main areas: improving medication designs and identifying novel, potentially effective drug combinations. Many of the large data problems that the life sciences sector is experiencing might be solved with AI.