Top 7 Ways Artificial Intelligence Will Impact Healthcare

There are endless ways to leverage technology to give more accurate, effective, and impactful interventions


There are endless ways to leverage technology to give more accurate, effective, and impactful interventions at precisely the appropriate time in a patient's care, whether for cancer, chronic diseases, or risk assessment, where the medical industry need significant reforms. Moreover, artificial intelligence is becoming the engine that drives changes across the care continuum as payment mechanisms change, patients demand more from their providers, and the amount of available data is growing at an alarming rate.


What are the highlighted seven areas of the healthcare sector most likely to experience a significant impact from artificial intelligence within the next ten years at the 2018 World Medical Innovation Forum (WMIF) on artificial intelligence, which Partners Healthcare presented? Let's look at them now.


Expanding access to care in underserved or developing areas :

In developing countries, a lack of trained healthcare providers, such as ultrasound technicians and radiologists, can severely limit access to life-saving care. Artificial intelligence could help mitigate the severe shortage of qualified clinical staff by taking over some diagnostic duties traditionally assigned to humans. AI imaging tools, for example, can screen chest x-rays for signs of tuberculosis, often with accuracy comparable to humans. In addition, this capability could be available to providers in low-resource areas via an app, reducing the need for a trained diagnostic radiologist on-site. 


Bringing intelligence to machinery and medical equipment :

Real-time video from inside a refrigerator and cars that recognise when a driver is distracted are just a few innovations that smart devices are bringing to the consumer world. Smart gadgets are essential in the medical setting for monitoring patients in the ICU and other locations. Artificial intelligence (AI) has the potential to improve outcomes significantly. It may even lower expenses associated with hospital-acquired disease penalties by enhancing the ability to recognise deterioration, hint that sepsis is setting in, or feel the emergence of complications. While ensuring that patients receive care as quickly as possible, incorporating intelligent algorithms into these devices can lessen the cognitive demands on doctors.


Minimising the threat of antibiotic resistance :

Antibiotic resistance is a growing threat to populations worldwide, as overuse of these critical drugs promotes the evolution of superbugs that are no longer treatable. Multi-drug-resistant organisms can wreak havoc in hospitals and take thousands of lives yearly. EHR data can assist in identifying infection patterns and highlighting patients at risk before they show symptoms. Leveraging machine learning and AI tools to drive these analytics can enhance their accuracy and create faster, more accurate alerts for healthcare providers.


Lowering the cost of using electronic health records:

EHRs have played an essential role in the healthcare industry's digitalisation journey. Still, the transition has brought many issues, such as overload, endless documentation, and user burnout. AI allows for creating more intuitive interfaces and automating some of the routine processes that process so much of a user's time.


Developing  more precise analytics for pathology images :

Pathologists are one of the most critical sources of diagnostic data for providers across the care continuum. Analytics that can drill down to the pixel level on huge digital images can help providers spot nuances that the human eye may miss. For example, AI can better predict whether cancer will progress quickly or slowly and how this will affect how patients are treated based on an algorithm rather than clinical staging or histopathologic grade. AI can also boost productivity by identifying key slide features before a human clinician reviews the data. 


Creating the next generation of radiology instruments : 

Radiological images produced by MRI machines, CT scanners, and x-ray machines provide non-invasive access to the human body's inner workings. However, many diagnostic processes still rely on physical tissue samples obtained through biopsies, which carry risks such as infection. Experts predict that AI will enable the next generation of radiology tools that are accurate and detailed enough to replace the need for tissue samples in some cases. Instead of basing treatment decisions on the properties of a small segment of the malignancy, this may allow clinicians to understand better how tumours behave. Providers can also define cancers' aggressiveness and target treatments accordingly more accurately.


Promoting the use of immunotherapy in the treatment of cancer :

One of the most promising methods for treating cancer is immunotherapy. Patients may defeat tenacious tumours by employing the body's immune system to combat malignancies. However, the existing immunotherapy approaches are only effective in a tiny percentage of patients, and oncologists still need a precise and dependable mechanism for determining which patients would benefit from this strategy. Machine learning algorithms' capacity to combine extremely detailed information may open up new possibilities for tailoring treatments to each patient's genetic profile.


At ITWerks Group of Companies, we  support a wide range of hardware and software vendor ecosystems with healthcare robotics solutions that have the potential to revolutionise healthcare. 


Don't hesitate to contact us if you need a better understanding of the technology for your business. We'd be happy to provide a non-obligated detailed explanation.