Artificial Intelligence (AI) is increasingly being used in healthcare for a wide range of applications.
AI isn’t something from a faraway sci-fi future – it’s a living reality right here and now. In fact, it’s been used in healthcare for decades.
But what actually is AI in healthcare? Why does it matter? What are the applications? And what does the future hold for AI in medical settings?
In this blog post, you’ll have answers to all these questions, plus examples of how it’s being used to benefit clinicians and patients alike.
We’ve read through the critical research and transformed it into a digestible, easy-to-read guide, backed by expert science.
Artificial intelligence (AI) in healthcare refers to machine learning algorithms and cognitive technologies that are applied in medical settings.
Essentially, this involves computer models being fed data, which they interpret and learn from, so they can predict future behaviour. They mimic human cognition and can make decisions without human input.
To illustrate our point, the computer models could be fed data like patient records. Then, the machine learning algorithm would learn from this data to make future decisions. As a result, it can help with diagnosis and personalised treatment plans.
For example, Empa Healthcare is using AI to work out exact dosages of painkillers for patients.
It’s worth noting that humans are still involved in this decision-making. AI just provides them with more data and insights to work with.
AI could have widespread implications for the medical industry.
It can assist doctors, nurses and other medical professionals in making life saving decisions.
Not only that, it can help manage people’s pain levels, so they’re more comfortable whilst receiving treatment.
AI useful for treating disease, as well as diagnosis and prevention, too.
Overall, AI leads to better patient outcomes and helps doctors make more informed, data-driven decisions.
But it isn’t just useful on a patient level. It can predict larger healthcare trends, such as the spread of diseases, which can help inform policy making to reduce the impact of outbreaks and pandemics.
AI can save time and resources for tasks that don’t require human input, such as scheduling appointments.
This can give administrators time to focus on more important areas that do benefit from human input, like communicating difficult messages with patients.
Saving time on administrative tasks will increase the productivity of the workforce overall. It will help administrators as well as doctors. For example, physicians will be able to spend less time reviewing patient history, as algorithms can help with reviewing this data.
Medical research is a slow process. And one of the most time-consuming elements is collecting and analysing data. AI can speed this process up, and perform these tasks quickly and at scale. In turn, this will help medical researchers find treatments for diseases faster.
Doctors and medical professionals have a high degree of workplace stress. In one study, researchers found that 37.9% of physicians experienced burnout, compared to 27.8% of those in the control group. Since AI can streamline administrative processes, this can reduce workload and help physicians achieve a healthier work-life balance.
AI can predict, prevent and diagnose disease, often at a faster rate than most medical professionals. In one study, AI was “significantly better” at diagnosing breast cancer than 11 pathologists.
Developing a new medical drug can take years and cost billions of dollars. AI can speed up the process, thus making it more efficient and cost-effective. In particular, AI can analyse data points at a much faster rate than humans.
AI has direct benefits for patients. For example, Spring Health is using AI to analyse patient data and match them with the right specialist for their mental health treatment. This means their support is more personalised and more likely to be effective.
AI robots have been assisting during surgery for years. They’re used in small procedures and operations like heart surgery, too. They can help surgeons with precision, so there’s less room for human error. For example, they can hold cameras, operate surgical equipment and more. Accuracy have created a CyberKnife, which provides radiotherapy treatment that’s personalised to each patient. It has sub-milimetre accuracy without sacrificing efficiency or patient comfort.
Machine learning is where you ‘train’ models with data, so they can predict future behaviour. For example, you can give the model data about different patient attributes – age, sex, environment, past health conditions – and use this to predict what types of treatment are likely to succeed. It can see patterns where humans can’t. One application is that it’s being used to spot features in radiology scans, which is helping to foresee whether patients could develop cancer.
Statistical Natural Language Processing (NLP) is based on machine learning algorithms. With a large data set, it can understand and predict language patterns. It has applications such as informing speech recognition software, and analysing patient notes to create reports. Already, some patients have conversations with bots thanks to AI. These bots can answer simple frequently asked questions without the need for human input.
Robots have been undertaking pre-defined tasks for years. But now, they’re incorporating more AI elements. For example, they can aid with radiotherapy treatment, and intelligently workout where to treat in the body, so there’s minimal damage elsewhere.
This refers to AI that’s improving administrative tasks, such as those in information systems. AI helps automate billing, updating patient records and booking appointments. This is more to do with workflow and processes in healthcare, rather than providing actual treatment.
Here are some of the key points from the latest academic research into AI in healthcare:
AI technology has existed for decades. The challenge lies in scaling AI and embedding it in a range of healthcare systems.
One of the main barriers to scalability is collaboration. There are sometimes conflicts of interest between the healthcare sector and the startups/tech companies designing the AI software.
Startups want to move fast and launch their product. In comparison, healthcare providers’ first priority is making sure the technology is safe and reliable.
Greater empathy from both parties will be needed to work together and scale AI solutions.
Another barrier is education. AI will require leaders to be well versed in data science and analysis.
Healthcare providers need to think about how they’re going to upskill their existing workforce, and embed this in learning for the next generation.
Digitisation also needs to happen before AI can be embedded more widely.
Providers need robust ways to collect and analyse data, as well as strict governance in place to ensure there are no security issues. Without this, there will be no digital infrastructure in place to cope with the influx in AI technology.
There are some sceptics of AI, and there are inaccuracies that need to be addressed.
But, generally, professionals predict that AI will only become more sophisticated and accurate in the future.
Where AI already exists in healthcare, such as administration, this will likely increase and become more widespread.
Progress will be slow at first. The healthcare system needs to get up to speed with digitisation and educating their workforce in data literacy. But in the next 10-20 years, AI will become a normal part of everyday life in healthcare settings.
Having said that, AI won’t replace clinicians. It will only enhance their decision-making and reduce their workload.
Doctors won’t disappear. They’ll just have more time to focus on their critical thinking, creative and communications skills – which, after all, is what makes us different from any kind of AI technology.
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