Artificial intelligence, or AI, has the potential to revolutionize the medical world as we know it. The incredible capabilities can replicate and, in many cases, exceed the way humans think are a potent force multiplier.
Healthcare AI can be applied across a broad spectrum of uses, but these generally occur in three progressive categories. As described by McKinsey & Company, the first comprises replacing human effort in “repetitive and largely administrative tasks.” AI healthcare has already established a solid foothold here, automating efforts ranging from routine patient and customer service roles to paperwork and documentation. At a deeper level, healthcare AI can be used in diagnostic analytics, deep-diving into detailed data and generating insights much more involved than would be possible with strictly human effort.
What is the Purpose of Artificial Intelligence within the Healthcare System?
The overarching purpose of this development won’t be to replace doctors and nurses. Instead, practical applications of healthcare AI will augment provider capabilities to focus on what they’re best at: patient care. Ideally, the more deeply AI is integrated into healthcare, the more human interaction patients can expect to experience.
Additional goals of healthcare AI integration include lower costs, faster service times, and a more customized standard of care. Experiments in machine learning genomics using AI healthcare are already being conducted with positive results, customizing pharmacology and treatment recommendations based on a patient’s unique genetic markers and family history. One common societal concern is that medical care will suffer by being treated by “robots,” which implies that individuals will experience a lower and more industrialized standard of care. Based on AI healthcare applications so far, the opposite is happening.
One of the current challenges here is the ever-growing mountain of datae. AI platforms in healthcare excel in parsing big data and generating accurate, detailed insights in a fraction of the time a human could. This can be useful in providing customized recommendations for patient triage, pharmaceutical prescriptions, surgical procedures, and long-term care. Within each of these areas, AI in healthcare can augment and support provider capabilities to deliver better patient outcomes with fewer complications at lower costs.
Let’s take a look at seven specific ways AI and machine learning can help save lives.
1. Generative AI improves queries and analysis of medical records
Google Cloud recently announced new artificial intelligence-powered search capabilities to help healthcare workers quickly pull accurate clinical information from different types of medical records. AI combined with unified storage platforms can help clinicians find what they need quickly across multiple systems and formats. With the power of generative AI, clinicians don’t have to sift through those mountains of records for answers. For example, searching month by month to learn what medications a patient has taken can be asked of a large language model (LLM) in a single question and answer format. Citations with each answer can alleviate concerns about accuracy, too. Retrieval augmented generation (RAG)-based capabilities can offer quick answers to clinical questions from within “walled gardens” of evidence-based, expert-curated information and tools. And things like AI-native networking can enhance network reliability for patients, clinicians, and staff; optimize campus and data center operations; and strengthen cybersecurity protocols.
In the case where AI can help healthcare providers save time and energy, it’s indirectly helping save lives as quality of care improves.
2. Constant monitoring of persistent conditions
AI applications are really good at monitoring health conditions and alerting caregivers and patients to potentially dangerous changes. For example, there are apps that can continually monitor blood glucose levels and send personalized messages to users in real time, demonstrating how their dietary choices impact insulin needs. This app can be paired with an AI-driven insulin pump, which replicates how the pancreas would respond by adjusting baseline insulin levels every five minutes. The data-driven algorithms used by both of these applications can not only increase a diabetic patient’s quality of life but could also help increase lifespan and prevent life-threatening blood glucose increases.
3. Enhancing imaging technology
AI can be a serious advantage when it comes to healthcare imaging. In fact, in 2016, AI advocate Geoffrey Hinton rocked the medical world by saying, “We should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists.”
While Hinton’s bold statement may have missed the mark, AI has indeed made significant inroads into the radiology profession and healthcare imaging in general. Instead of replacing radiologists altogether, the technology is helping them be more accurate and quick at analyzing images to make diagnoses. AI algorithms can process millions of images in a snap and learn to identify subtle signs of illness that humans can’t detect.
Some automated systems do exist and have done better at detecting diseases, such as skin cancer and diabetic retinopathy, than their human counterparts. While many experts consider the healthcare industry to be in the very early stages of using AI to replace trained humans in analyzing images, the technology is certain to get better with time.
With AI-assisted or AI-only imaging analysis, healthcare organizations can detect serious diseases earlier and faster, which allows them to provide treatment and raises the likelihood of positive outcomes.
4. Monitoring at-risk and critical care patients
AI is helping to save the lives of patients in intensive care units and other critical care settings.
In one example, University of Florida Health Center researchers are working on a “smart” ICU, which uses cameras, wearable sensors, lighting sensors, noise meters, and other monitoring equipment to sense when patients are in pain or need assistance. The system’s AI algorithms focus on body movement and facial cues to make its determinations, which can keep an eye on patients at every moment—even when nurses are helping other patients.
Another example of AI-based patient monitoring is the BioSticker sensor, developed by Philips and BioIntelliSense to monitor at-risk patients after they leave the hospital and return home. The wearable patch enables continuous passive monitoring of patient vitals and even parameters such as body position, coughing frequency, activity levels, and more. The data collected can help doctors personalize treatment plans for people living with chronic illnesses and can also alert caregivers to emergency situations that need immediate intervention.
5. Advancing surgical outcomes
One way to improve surgical outcomes is to use AI and machine learning in applications that predict surgical outcomes and help caregivers determine whether a particular operation is worth the risks. The system would take into account individual patient data, including the person’s condition and medical history, along with other factors, and offer a recommendation to doctors about the likelihood of surgical success.
New research has found that AI may be able to predict cancer survival, meaning it could become a critical tool for assessing risk and aiding patients and caregivers in making treatment decisions.
AI-assisted robotics are also being used more often in surgery, and these tools are helping to improve outcomes after surgery. Because surgical robots can be more precise and accurate than human surgeons in some cases, they can also positively affect outcome factors, such as reduced blood loss, reduced risk of errors, fewer complications, shorter average hospital stays, and faster recoveries.
At UC Berkeley, a team of postdoctoral researchers trained a da Vinci surgical robot to perform suturing tasks faster than an experienced surgical resident was capable of, with 99.4% accuracy.
6. Improving surgical techniques
Surgeons rarely experience the opportunity to improve their techniques, but this is one area where AI in healthcare holds tremendous potential. AI software could record surgical procedures and compare them to a bank of ideal techniques, allowing surgeons to evaluate their methods against established best practices and identify areas for improvement. AI could also augment physician technique by providing a host of customized training resources, as well as immediate and direct feedback on individual performance.
AI can also assist during surgical procedures to ensure that surgeons are able to do their best work. One example of this is a surgical navigation system that provides a real-time, enhanced 3D view of the surgical site anatomy. Using this type of tool, surgeons can complete procedures more quickly and accurately, decreasing the risk of errors due to fatigue—while also minimizing the degree of trauma at the surgical site.
7. Decreasing dosage errors
A recent research article reported that, in the US alone, 7,000 to 9,000 people die as a result of a medication error, and hundreds of thousands more suffer adverse reactions or other complications. In addition to the physical and psychological costs of those errors, the total cost of caring for patients with medication-associated errors exceeds $40 billion each year.
One way AI can help reduce medication dosage errors is through smart devices, such as insulin pens and inhalers, that can monitor how much medication a patient has already ingested and alert caregivers if the patient isn’t using the device correctly.
Another example is an AI-based system that takes individual patient data into account when a doctor is prescribing medication—including other current medications the patient is taking and any allergies the patient may have. While less personalized systems might greenlight the prescription of a specific medication for a patient’s condition, the AI-based system would flag any potential problems based on specific, personal data.
What is the Future of Healthcare AI?
As AI healthcare evolves, more applications across the entire spectrum of the medical world can be anticipated. From research and diagnostics to patient care and direct interaction, we can expect healthcare AI to become an ever-present reality. The benefits are all-encompassing, creating faster, more flexible healthcare that can be more easily customized at a lower cost than would be available with a system operated exclusively by humans. However, the medical community has been clear in its desire to use healthcare AI to promote higher levels of personal interaction between providers and patients.
The end result of AI healthcare integration will be better outcomes, with more depth, breadth, and customization of medical care across all specialties. This will be more economical as fewer new professionals are needed and promote a higher standard for patient care.
