In recent years, the healthcare sector has seen an influx of advances in technology, and Artificial Intelligence (AI) & Machine learning (ML) are among the most promising. In this article, we'll explore the potential of AI & ML in healthcare and discuss their benefits - from improved diagnosis accuracy to reducing administrative costs. Read on to discover how these powerful tools can help unlock the full potential of healthcare!
What is AI and how is it used in healthcare?
In its simplest form, AI is the ability of machines to perform tasks that would normally require human intelligence, such as understanding natural language and recognizing objects. AI solutions in healthcare is being used in a variety of ways to help improve patient care and outcomes. One area is in the diagnosis of diseases. AI can be used to examine images of the body, such as X-rays and CT scans, to look for signs of disease. AI can also be used to analyze large amounts of data to identify patterns that may indicate the presence of disease. AI is also being used to develop personalized treatment plans for patients based on their individual genetic makeup.
Finally, AI is being used to help improve the efficiency of healthcare delivery. For example, chatbots are being used to provide 24/7 access to medical advice and appointment scheduling. AI is also being used to streamline administrative tasks such as billing and coding.
What is ML and how is it used in healthcare?
Machine learning is a specific type of artificial intelligence that helps to draw medical insights from large amounts of data without much human intervention. ML means that machines can automatically improve given more data. This is important for healthcare organizations because it can help to improve patient care and outcomes by providing more accurate and timely diagnosis and treatment recommendations. ML has the potential to transform the way we deliver and manage care.
For healthcare organizations, ML is especially valuable in cases where traditional methods of data analysis would be too time-consuming or expensive. For example, ML could be used to analyze patient records to identify patterns in disease progression or treatment response. It can also be used to predict which treatments will work best for a patient, based on that patient's individual characteristics, and to help manage chronic diseases.
Let’s take a look at some of the main benefits of AI and ML in healthcare:
1. Increased efficiency of the diagnostic process leading to lower costs
AI can help to increase the efficiency of the diagnostic process by automating certain tasks traditionally carried out by humans, such as image recognition and data analysis. This not only saves money on resources, but it can free up time for doctors and other medical professionals, so that they can focus on more complex cases. Additionally, AI can help to improve the accuracy of diagnoses by providing second opinions or catch errors that human doctors may overlook leading to higher efficiency in a number of ways, from reducing paperwork to optimizing clinical decision-making. Ultimately, AI and ML have the potential to help healthcare organizations save money while also improving patient care.
2. Developing new treatments
By analyzing large data sets, AI can help identify patterns that could lead to new insights about disease. For example, Google’s DeepMind Health team is using AI to better understand how the UK National Health Service (NHS) treats patients with kidney disease. By analyzing millions of anonymized patient records, they were able to identify a previously unknown link between certain types of kidney injury and the use of a certain type of medication. This knowledge could potentially help doctors make better decisions about which medications to prescribe for their patients.
3. Enhancing patient care
AI and ML provides clinicians with timely and actionable insights that can help them make better decisions about their patients’ care. For example, an AI system might review a patient’s medical record, lab results, and symptoms to identify patterns that could indicate a serious condition.
4. Easy information sharing
The easy sharing of information is one of the many benefits that AI and ML bring to healthcare. By digitizing medical records and making them accessible to AI and ML algorithms, a new level of insights and predictions that were not possible before is now possible. For example, IBM Watson has been used to successfully diagnose cancer patients faster and more accurately than traditional methods.
5. Better prevention care
AI and ML help to identify individuals at risk for certain conditions, such as heart disease or cancer. This information can then be used to develop targeted prevention and care plans. It can also be used to monitor individual health over time. This data can be used to identify early signs of disease or other health problems. This information can then be used to provide timely intervention and care.
Wearable AI and ML in healthcare
Wearable healthcare technology also uses AI to better serve patients – being able to assess one’s own health through technology eases the workload of professionals and prevents unnecessary hospital visits or remissions.
By analyzing a patient’s medical history and current health data, AI can also create a customized plan that considers the individual’s specific needs. This type of personalized care is becoming increasingly important as the population ages and the prevalence of chronic diseases increases.
AI and ML in healthcare can also help to prevent unnecessary hospital visits or readmissions. For example, if a patient is at risk of developing a certain condition, AI and ML could be used to identify early warning signs and provide treatment before the condition becomes serious enough to require hospitalization. Additionally, AI and ML could also be used to monitor patients after they are discharged from the hospital, helping to ensure that they do not experience any unnecessary setbacks or relapses.
Biobeat ML and EWS (Early Warning Score)
One area where ML can have a big impact is in early warning systems. EWS are designed to identify patients who are at risk of decline and notify the care team so that they can intervene before the patient deteriorates.
Biobeat's EWS uses machine learning to analyze data from wearable sensors to predict when a patient is at risk of decline. The system has been shown to be accurate 30 hours (!) before the decline, which is much earlier than when a medical team would notice it. This could potentially save lives by allowing the care team to intervene sooner.
Conclusion
There are many potential applications for AI and ML in healthcare. It would not be an overstatement to say that it is a total game changer. It can be used to diagnose diseases, predict, and improve patient outcomes, personalize treatments, enhance efficiency, develop new drugs and treatments, increase patient quality of life, reduce hospital visits, and save costs.
With AI and ML integrated in the healthcare system, the future looks brighter for both hospitals, doctors, and their patients.