AI Applications Transforming Healthcare

Dr. Robot will see you now

It’s hard to imagine any areas of significant business or societal significance that will not be impacted by advances in AI technology in the coming decades. Healthcare is probably the most lucrative of these sectors, slated for sizeable developments thanks to improvements in the speed and utility of artificial intelligence. Some of the driving forces behind AI’s mainstream adoption in healthcare include the dropping prices of hardware and software, the proliferation of big data, the ageing of the world’s population, and the rising costs of drugs and medical care.

AI tools are gradually finding their way into clinical practice, as they mature and flaws in algorithmic logic are worked out. In the next decade there is much hope that computer algorithms will be able to diagnose serious diseases like cancer, Alzheimer’s, and others, simply based on patient data and knowledge about patterns.

Clinical trial diagnoses

One of the medical areas that is already positively impacted by artificial intelligence is the clinical trial process, which has been transformed by cutting trial times in half, improving results reliability and reducing costs. AI is achieving this by selecting the most eligible trial participants, analyzing trial data, and making breakthrough discoveries based on patterns that are off limits to the human brain. The infamous patent cliff is gradually evening out as a result of more rapid time-to-market for revolutionary life-saving drugs.

Preliminary detection and diagnosis

Early disease identification is at the core of selecting and prescribing the most optimal treatment options and securing favorable patient outcomes. The capacity of AI to diagnose diseases early is advancing at promising rates. Recently, data scientists have revealed AI models capable of detecting cardiovascular issues, ocular diseases, and even breast cancer in mammograms. The U.S. FDA has also approved an AI diagnostic device called IDx-DR, which can spot a type of eye disease from photos of the eye retina, taken with a special camera.

Robot-assisted surgery

Advances in robotic solutions are rendering additional types of surgery possible. Whereas human-driven surgeries are risky or challenging, AI can make a real difference in minimizing those risks and ensuring chirurgical success.

The rise of virtual nurses

Patient dissatisfaction at medical establishments continues to rise as qualified labor shortages impact facilities worldwide. The increasing HR pressure, driven by patient demand can be solved by introducing virtual nursing assistants – the less human but more attentive counterparts of today’s nurses. Unlike their human colleagues, virtual assistants can work around the clock without getting tired and allowing fatigue-related errors, in addition to delivering highly personalized treatment at any time.

Dosage error reduction

In today’s healthcare industry, medical errors are prevalent and, in some cases, – life-threatening. As a result, fines and penalties brought on by medical malpractice suits are becoming more sizeable and numerous. Dosage error is one such challenge that can be remedied by AI models with continuous access to patient vitals, thus taking the guesswork out of the process and bringing dosage-related risks to a minimum.

Connected machines

Sophisticated insights derived from terabytes of patient data would be of little use to healthcare professionals if the diagnostic equipment and specialist terminals couldn’t access them. As increasing numbers of hospital devices go online, it’s paramount that the latest patient data is propagated to them, and abnormalities – flagged and addressed in a timely fashion. In addition, connecting patient data to personal IoT devices can lead to improved post-treatment prognosis, thanks to highly customized care recommendations.

Administrative bottlenecks

Often, not having the right health insurance paperwork in place and on time can be a barrier to receiving timely treatment, which can prove life-threatening on occasion. Being able to easily integrate third-party systems with existing technology infrastructure in healthcare facilities, process data and medical files can result in tangible improvements for patients.  

Fraud detection and prevention

Healthcare isn’t immune to fraud, especially when it comes to handling increasingly complex hospital service offerings and charges or insurance claims for life-saving medication, treatment or rehabilitation procedures. Giving AI oversight of those processes can help detect and prevent sinister attempts early on, saving hospitals, governments and tax payers millions of euros.

Computer algorithms used to detect illnesses are showing great promise, though, they must be developed and applied with care. As healthcare practices become more AI-reliant, are you likely to trust the system more or less? Let us know on Twitter @pegusapps.

Copywriter: Ina Danova

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