Artificial intelligence in medicine is the use of machine learning models to search medical data. It helps to uncover insights to help improve health outcomes and patient experiences. Thanks to recent advances in computer science and informatics. Which makes artificial intelligence (AI) is quickly becoming an integral part of modern healthcare. AI algorithms and other applications powered by AI are being used to support medical professionals in clinical settings and in ongoing research.

Currently, the most common roles for AI in medical settings are clinical decision support and imaging analysis. Clinical decision support tools help providers make decisions. It can assist about treatments, medications, mental health and other patient needs. By providing them with quick access to information or research that’s relevant to their patient. In medical imaging, AI tools are being used to analyze CT scans, x-rays, MRIs and other images for lesions or other findings that a human radiologist might miss.

In this post, Pritish Kumar explained about Use of artificial intelligence in medical field:

AI applications in medicine

There are numerous ways AI can positively impact the practice of medicine. whether it’s through speeding up the pace of research or helping clinicians make better decisions. Here are some examples of how AI could be used:

·       AI in disease detection and diagnosis

Unlike humans, AI never needs to sleep. Machine learning models could be used to observe the vital signs of patients receiving critical care and alert clinicians if certain risk factors increase. While medical devices like heart monitors can track vital signs. At the other end AI can collect the data from those devices and look for more complex conditions.

·       Personalized disease treatment

Precision medicine could become easier to support with virtual AI assistance. Because AI models can learn and retain preferences, AI has the potential to provide customized real-time recommendations to patients around the clock.

·       AI in medical imaging

AI is already playing a prominent role in medical imaging. Research has indicated that AI powered by artificial neural networks can be just as effective as human radiologists. it can used at detecting signs of breast cancer as well as other conditions. In addition to helping clinicians spot early signs of disease, AI can also help make the staggering number of medical images.

·       Clinical trial efficiency

A lot of time is spent during clinical trials assigning medical codes to patient outcomes and updating the relevant datasets. AI can help speed this process up by providing a quicker and more intelligent search for medical codes.

·       Accelerated drug development

Drug discovery is often one of the longest and most costly parts of drug development. AI could help reduce the costs of developing new medicines in primarily two ways: creating better drug designs and finding promising new drug combinations. With AI, many of the big data challenges facing the life sciences industry could be overcome.

Benefits of AI in medicine

Informed patient care – Integrating medical AI into clinician workflows can give providers valuable context while they’re making care decisions.

Error reduction – There is some evidence that AI can help improve patient safety. A recent systemic review of 53 peer-reviewed studies examining the impact of AI on patient safety. This found that AI-powered decision support tools can help improve error detection and drug management.

Reducing the costs of care – There are a lot of potential ways AI could reduce costs across the healthcare industry. Some of the most promising opportunities include

  1. reducing medication errors.
  2. customized virtual health assistance.
  3. fraud prevention.
  4. supporting more efficient administrative and clinical workflows.

Increasing doctor-patient engagement – Many patients think of questions outside of typical business hours. AI can help provide around-the-clock support through chatbots. That can answer basic questions and give patients resources .

Providing contextual relevance – One major advantage of deep learning is that AI algorithms can use context to distinguish between different types of information. For example, if a clinical note includes a list of a patient’s current medications  along with a new medication their provider recommends. On that case a well-trained AI algorithm can use natural language processing . It helps to identify which medications belong in the patient’s medical history.