What will health technology look like in the nearest future? Say, in five years? It’s hard to say for sure, but we can be confident that it will involve machine learning ― a subset of artificial intelligence (AI) that allows technology to use historical data, learn from it, and predict outcomes without being explicitly programmed to do so. In the past years, the COVID-19 pandemic has accelerated the digitization of healthcare and the adoption of the newest forms of technology. Current technology trends in healthcare are all about AI. Artificial intelligence has skyrocketed in both research and implementation in the past few years, and it is sure to play a huge role in solving the current and upcoming healthcare challenges.
A 2021 KPMG survey showed that as much as 82% of healthcare and life sciences executives want to see their organizations adopting AI technology in the nearest future. In a Deloitte survey, nearly 3 in 4 healthcare organizations reported they will be increasing AI funding. And for a good reason: the KPMG study showed that in 56% of cases, AI technologies have delivered more value than expected by the organizations that adopted them. For example, in the US, AI technologies helped monitor the spread of COVID-19 cases, develop vaccines, and aid vaccine distribution. Artificial intelligence and machine learning technology helped reduce errors and improve medical outcomes. In 40% of healthcare organizations, AI technologies have helped increase patient engagement, which led to better health results. Finally, in a survey, about a third of executives reported that AI has improved administrative efficiency. Administrative load is a serious challenge for healthcare facilities all over the world, and a lot of investments are focused on electronic health record (EHR) management among everything else.
By looking at investments we can predict which of the healthcare technologies will benefit most from the introduction of machine learning and more broadly in the future. These are the following:
The KPMG survey found that 38% of the AI investment funding will go to developing telemedicine. Telemedicine, also referred to as telehealth and e-medicine, is the remote delivery of healthcare services over the telecommunications infrastructure. Before the pandemic, telemedicine wasn’t a popular solution for most healthcare facilities. It was widely believed that the doctor has to see the patient in order to diagnose them and carry out the treatment. In 2022, healthcare professionals regularly have video conferences with their patients. Telemedicine has made healthcare more accessible, cheaper, and more constant in patients’ lives. It has shifted the focus to preventive healthcare, increased patient engagement, and integrated well with other forms of patient care. Telemedicine is expected to grow to $185.6 billion by 2026.
The AI/machine learning use cases for telemedicine are the following:
- Telemonitoring. Machine learning algorithms allow remote monitoring tools to detect health problems early on. Wearables measure blood pressure, pulse rate, respiratory rate, blood oxygen level, weight, body temperature, and other factors. Alternatively, patients manually feed the data into the system at regular intervals. In both cases, AI helps to identify if the patient is at risk of specific conditions and needs a doctor’s appointment, a drug prescription, or further monitoring. Some remote monitoring devices can send the data directly to the telemedicine system for analysis.
- AI/machine learning is used to analyze images, such as X-rays (see our case study for one of such tools), CT scans, and diagnostic test results and provide recommendations based on the results, as well as the patient’s symptoms and medical history. Ideally, a scalable and secure cloud-based solution made for telemedicine would be used for that.
- Treatment plans. Machine learning algorithms can be used to identify which treatments are most effective for each patient based on their personal medical history. AI could then develop personalized treatment plans that would take this into account, as well as patient preferences, such as type of treatment.
- Patient support. Chatbots, famously made with machine learning technology, could provide patients with answers to common questions. They can do it 24/7, with no break or rest needed. Chatbots can also help patients to schedule appointments. AI can be also used to provide reminders for appointments and notify patients when it’s time to take medications.
- Chronic disease management. Living with chronic diseases such as diabetes and heart disease has improved significantly since the introduction of telemedicine. AI and machine learning can be used to further support the management of chronic diseases by tracking and analyzing progress, offering feedback, and measuring the likelihood of developing complications.
Electronic Health Records
Documentation and process automation is the area that will receive 37% of AI investments in the nearest future, according to a KPMG survey. Electronic Health Records (EHR) are being steadily improved over the years with the help of various technologies, and AI, machine learning, and deep learning are revolutionizing it to carry out more complicated tasks than simply keeping the data.
AI is already able to analyze images for abnormalities, recognize early warning signs of patient morbidity or mortality, and assist with diagnosis. Machine learning made that possible and continues to do so: previously learned images are applied to educating and testing AI to become even better at its job. AI is also used to train medical students and young doctors. AI creates “realistic” OR situations and even previously unseen imaging for the students to assess.
Robotic Surgical Systems
Further down the line, we have robotic surgical systems (also called robot-assisted surgeries). In orthopedics, robotic surgical systems are being used to cut bone, and fully autonomous procedures can be done today on fixed anatomical structures such as the eye and bone. Robots are preferred to surgeons because they ensure unparalleled precision, don’t get tired, and don’t take breaks. To improve their skills, artificial intelligence is being applied to surgical robotics. At the moment, machine learning data is collected from hours of watching surgeons perform to improve surgical robots. Thanks to this data and complex algorithms, AI can determine patterns within surgical procedures and use that to improve accuracy and precision in the future. Besides, laparoscopic video analysis of surgeries that are done with the help of AI like, for example, sleeve gastrectomy procedures, helps to identify missing or unexpected steps during the surgery ― in real-time.
It’s abundantly clear that in the upcoming years we’ll see the improvement of health technologies and the growth of AI adoption. Industry stakeholders are taking steps to advance the use of AI and machine learning in healthcare, and in 2021, the U.S. Food and Drug Administration (FDA) released its first AI and machine learning action plan, which is meant to advance the agency’s management of advanced medical software. This means that during the next five years, regulators, healthcare professionals, data scientists, and engineers will work together to achieve better, smarter, and faster health technology. This will not only improve patient care (which, of course, is the main priority), but also reduce the workload on health professionals who are struggling to do their job because of long hours, lack of resources, and constant need to improve their knowledge.