Artificial Intelligence in Healthcare
Artificial Intelligence in Healthcare
Before the Terminator gets back, AI will have found many applications in several industries. In the healthcare industry, it cannot be disassociated from telehealth. It encompasses the development of human-like cognitive capabilities, focusing on many applications ranging from diagnosis, alleviating stress from care providers (as it allows for easier prioritization of tasks), to improving patient adherence. Thus, AI in healthcare is a helpful tool that can help doctors and medical providers deliver the highest level of care in the shortest amount of time.
AI Technology in Healthcare
We can differentiate between different types of these technologies to develop multiple applications for telemedicine solutions. Machine learning, neural networks, and deep learning are the most robust segregation.
Machine Learning in Healthcare
Machine learning is an algorithmic approach that analyzes large amounts of data and learns from them. In healthcare, it mainly focuses on precision medicine, predicting treatments for patients based on their condition and other contextual variables. AI in healthcare, therefore, has the capacity to help with patient treatment.
Neural Networks in Healthcare
Another facet of this technology, neural networks have been around since the 1960s, and their everyday application is in the prediction of whether an individual will acquire/develop a particular disease. It is based on inputs viewed as problems which are then presented as outputs. The two are connected through layers (variables).
Deep Learning in Healthcare
The most complex form of healthcare AI, deep learning is a similar algorithmic approach with thousands of hidden features affecting the outcomes of inputs. They are uncovered through the faster graphic processing units and cloud architectures. Consequently, it has arguably the most important application: it can analyze radiology images and detect potentially cancerous lesions based on gene expression data. The benefits of applying this technology are twofold: it acts as the second pair of eyes that, unlike the clinicians’, don’t have to rest. And two: applying this artificial intelligence in healthcare allows caregivers to prioritize cases based on the level of danger and seriousness of the situation.
Diagnosis and Treatment using Medical AI
Following years of medical training, an individual will be able to diagnose a disease properly. It is still an arduous and imperfect process. Diagnostic errors are not uncommon: they account for 17% of preventable errors in hospitalized patients, and subsequent autopsies revealed that 9% of patients had been misdiagnosed.
As was already discussed above, AI in healthcare has the capacity to right these wrongs, despite needing a lot of examples to learn how to view patterns and symptoms in the same way which humans do. That is why these algorithms are most useful in areas in which the patient data is already digitized, such as:
- MRI Scans
- CT Scans
Rather than interpreting data and effectively cutting the need for a clinician in the first place, AI and healthcare work in symbiosis: the algorithm extrapolates the data. It presents it to the clinician allowing him to focus on the treatment instead of losing time on data analysis.
The development of a new drug usually takes decades and costs billions. It is a long trial-and-error process in which 90% of drugs don’t make it past human and animal testing. That is where AI algorithms come in handy. They can sort through large amounts of information such as blood samples or tissues to identify which proteins can play an essential role in developing future drugs. This is made possible by integrating electronic health records and booming technologies, making patient data easily accessible and analyzed rapidly.
In hundreds of thousands of miniature experiments, robots, which constitute an already widespread body of AI technology, can now be applied to healthcare. They can be used to apply drugs to diseased cells and determine whether an assigned therapy can battle a disease without disrupting healthy cells in hundreds of thousands of little experiments.
While the benefits of this technology seem apparent, there are also a number of ethical concerns that need to be addressed.
Ethical Issues with AI in Healthcare
If not used properly, a number of security and privacy issues can arise from the use of ML in healthcare. Furthermore, despite all the benefits, AI is not immune to mistakes and problems similar to humans make. Even though significant advancements have been made, there is still a long way to go before complete integration with the healthcare system is possible.
Informed Consent in Healthcare
AI applications in healthcare open similar questions as the use of any technology in any sensitive industry: how much data is given to third parties? Can this information be traced back to individuals? To what extent is function creep present in telehealth?
The inquiries get additionally webbed-up in this instance. Even though the traditional patient/clinician relationship can be made more accessible and improved through Ai and machine learning in healthcare, do the same principles of informed consent apply?
To that end, machine learning applications pose the question of: is the clinician obligated to inform the patient that such technologies are used? Under what circumstances does the care provider have to educate the patient fully about algorithms they don’t fully understand? Does the same go for data inputs and outputs? And should the patient be informed of this usage to begin with?
Before the inquiry gets too tangled up, we must address additional issues.
Artificial Intelligence Healthcare Safety Issues
Watson for Oncology (WFO) is a clinical decision-support system developed to assist oncologists with evidence-based treatment options. Watson uses algorithms to assess patients’ information to explore potential treatments for cancer patients. However, like any program, Watson is as reliable as the datasets he feeds. In other words, with an inadequate dataset, the safety of patients can be jeopardized by healthcare artificial intelligence.
Healthcare professions belong to a group of occupations that can’t have bad apples. Using a deep learning algorithm adds another cook to the broth-making process. And if it learned from a sour recipe: that broth is going to get spoiled.
Such was the case in which Watson recommended unsafe and incorrect treatments. And since Watson isn’t really helping us cook – instead, it is helping us save lives – the recipe needs to be spot on.
AI and Bias
The spread of machine learning healthcare means that we can improve global health. With the ever-growing evolution of remote health, diagnostics and telemedicine applications can soon find their way across the globe and be applied to the resource-poor parts of the world. However, much like human migrations often result in cultural clashes, technology doesn’t come without its fair share of prejudices.
Just like any human is as trustworthy as his mentor, an ML system isn’t immune to wrong inputs. Therefore, artificial intelligence and healthcare can come with a warning reading: could contain discrimination. We see an example of this in the following: imagine software that was to be deployed to help diagnose and treat cancer patients. Now imagine that the algorithm learned from studies conducted mostly on Caucasian participants. It is not unfathomable that the software should give incomplete, inaccurate, or simply dangerous recommendations for subpopulations for which there wasn’t enough data in the sample.
Artificial Intelligence in Healthcare Examples
Despite some obstacles, we have already demonstrated how AI can assist us in healthcare. All that is left is to see what are some of the real-life examples of its usage:
- Chatbots: these virtual caregivers have the ability to provide patients with immediate responses and, should they be executed properly, can often exceed patients’ and clinicians’ expectations
- Robotic surgeries: Artificial intelligence technology in healthcare can now gold assistant positions during surgeries. It can help even the most skilled surgeons to reduce shudders or accidental movements, therefore, mitigating the risks and performing better precision cutting and stitching
- Automation of administration: one of the “simpler” tasks that healthcare machine learning can take care of is the prioritization of tasks, allowing doctors, nurses, and all care providers to focus on their day-to-day duties and other non-patient activities
Here at Vicert, we already have experience developing Artificial intelligence technology in healthcare. Working with DXS, we helped update and improve certain features of IDx-DR – a diagnostic system that autonomously diagnoses patients for diabetic retinopathy and macular edema.
IDx-DR – a diagnostic system that autonomously diagnoses patients for diabetic retinopathy and macular edema.
Future of Artificial Intelligence in Healthcare
It remains to be seen just how impactful this technology will be in the years to come. While we cannot know what futuristic tech will come our way, we know that AI in the healthcare market is expected to grow from USD 6.9 billion in 2021 to 44 billion by 2027. One can only hope that SKYNET doesn’t take off.