What is artificial intelligence in medicine?
The challenges that the COVID-19 pandemic has created for many healthcare systems have also led many healthcare organizations around the world to begin field testing new AI-assisted technologies, such as algorithms to monitor patients and AI-powered tools to screen patients with COVID-19.
Yet the opportunities for AI to benefit clinicians, researchers, and the patients they serve are ever-increasing. At this point, there is little doubt that AI will become a central part of the digital health systems that shape and support modern medicine.
AI can positively impact the practice of medicine in a variety of ways, from accelerating the pace of research to helping clinicians make better decisions
Getting a Personalized treatment for a disease
Precision medicine could become easier to support with virtual assistance from AI. Because AI models can learn and retain preferences, AI has the potential to provide real-time, personalized recommendations to patients around the clock. Rather than having to repeat information to a new person each time, a healthcare system could offer patients always-on access to an AI-powered virtual assistant, who could answer questions based on the patient's medical history, preferences, and personal needs.
AI is already playing a leading role in medical imaging. Research has shown that AI-powered by artificial neural networks can be just as effective as human radiologists in detecting signs of breast cancer and other diseases. In addition to helping physicians spot early signs of disease, AI can also help make the staggering number of medical images physicians must keep more manageable by detecting critical pieces of history. of a patient and presenting them with relevant images.
During clinical trials, a lot of time is spent assigning medical codes to patient results and updating relevant datasets. AI can help speed up this process by providing faster and smarter searches for medical codes. Two IBM Watson Health customers recently found that with AI, they could reduce their number of medical code searches by more than 70%.
Drug discovery is often one of the longest and most expensive parts of drug development. AI could help reduce the costs of new drug development in two main ways: by creating better drug designs and by finding promising new drug combinations. With AI, many of the big data challenges facing the life science industry could be addressed.
Integrating medical AI into physician workflows can give providers valuable context when making healthcare decisions. A trained machine learning algorithm can help reduce research time by providing clinicians with valuable research results, along with factual information about treatments and procedures, while the patient is still in their office.
It is somewhat obvious that AI can help improve patient safety. A recent systemic review of 53 peer-reviewed studies investigating the impact of AI on patient safety found that AI-powered decision support tools can help improve error detection and medication management.
AI could reduce costs in healthcare in many ways. Reducing medication errors, personalized virtual medical assistance, fraud prevention, and supporting more efficient administrative and clinical workflows are some very promising possibilities.
Many patients have questions outside normal opening hours. AI can provide round-the-clock support with chatbots that can answer basic questions and give patients resources when their provider's office isn't open. AI could also be used to triage questions and flag information for further investigation, which would alert providers to health changes that need further attention.
One of the main benefits 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 as well as a new medication recommended by the provider, a well-trained AI algorithm can use natural language processing to identify which medications should be on the list. the patient's medical history.
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