AI and Robotics Revolution in Healthcare

With rapid digital transformation, the healthcare industry has seen massive changes. For instance,  robots in the clinical field are changing the way medical procedures are performed. Organizations offer various solutions for improving healthcare technology, including surgical help, social, versatile, and independent robots. Currently, robots are utilized not only in the surgical room but also within clinical settings to help the healthcare workers and improve patient care. In fact, during the peak of the COVID-19 pandemic, hospitals and emergency clinics started using robots for a wide range of tasks to avoid exposure to microorganisms. The operational efficiencies and risk reduction provided by AI robots are quite valuable in many areas. 

For example, according to the American Cancer Society, Mammogram reviews and translations using AI are 30 times faster and have a 99% accuracy rate, decreasing the need for unneeded biopsies and the uncertainty and stress of a misdiagnosis. Another example is the CardioDiagnostics device, which is designed to improve cardiac monitoring and rhythm management by remotely monitoring its wearer for heart anomalies.

Here are a few of the advantages of robotics in healthcare:

Patient treatment has improved

Medical robots perform minimally invasive procedures, customized and frequent monitoring of patients with chronic diseases, intelligent therapeutics, and social engagement for elders. In addition, robots alleviate workloads, allowing nurses and caregivers to interact more frequently with patients. This, in turn, promotes the long-term well-being of the patient.

Some robots also perform cleaning and disinfection, navigating through infectious disease wards, operating rooms, laboratories, and public hospital spaces. This helps humans from catching any infectious disease and decreases dependency on manual labor.                                                                            

Improved operational effectiveness

Service robots streamline routine tasks, reduce the physical demands on human workers, and ensure more consistent processes.

These robots can track inventory and place timely orders, helping make sure supplies, equipment, and medication are where they are needed. Though the cost of infrastructure is high and specialized training is required for those operating via robots, considering the long-term benefits, it will be a fruitful investment.

Artificial intelligence (AI) along with robotics is quickly making its way into the healthcare industry. It is being used in medicine in many ways with several applications to analyze complicated medical data, compute information, and assist doctors in improving the patient’s treatment outcomes.

Safe Working Conditions

 Service robots help to keep healthcare workers safe by transporting supplies and linens in hospitals where pathogen exposure is a risk. Robots help hospitals to sanitize rooms. They also assist in cleaning and sanitizing equipment and reducing hospital-acquired infections.

Many healthcare facilities are already using robots. Social robots are also helpful in heavy lifts, such as moving beds or patients, which reduces physical strain on healthcare workers. 

Early detection of disease

AI and ML programs are designed to diagnose the disease and are trained to gather and save data like patient’s history, lab results, scans, symptoms, and pictures of confirmed and susceptible cases. They help in detecting unseen facts and provide results efficiently and more accurately.

Several AI machines like Viz.ai, PathAI, and Enlitic are there, using which symptoms and diseases can be identified early and cured in time.

Assistance with Surgery

 As motion control technologies are becoming advanced day by day, surgical-assistance robots have become more precise. These advanced robots help surgeons to perform complex micro procedures without making large incisions. As surgical robotics continue to evolve, AI enables the robots with the use of computer vision to navigate to specific areas of the body while avoiding nerves and other internal obstacles. Some surgical robots can complete tasks autonomously, allowing surgeons to oversee procedures and the whole process from a console.

Assistance Robots Classification

Assistive robots fall into the following categories:

Modular Robots:

In healthcare, modular robots enhance other systems and can be configured to perform multiple functions. These include therapeutic exoskeleton robots and prosthetic robotic arms and legs, which can help with rehabilitation after strokes, paralysis, traumatic brain injuries, or multiple sclerosis. Now robots are also equipped with AI-enabled cameras that can monitor a patient’s form as they go through prescribed exercises, body movements, and measuring degrees of motion in different positions.

Service Robots:

Service robots relieve the daily hardships of healthcare workers by handling routine logistical tasks. These robots can send a report when they complete a task. These robots can set up patient rooms with sanitization supplies, track them, file purchase orders, restock medical supply orders, and transport bed linens to and from laundry facilities.

Social Robots:

Social robots interact directly with patients. These “friendly” programmed robots can be used in long-term care environments to provide social interaction. They encourage patients to follow treatment regimens or provide cognitive engagement, keeping patients happy and positive. They also can be used to guide visitors with directions inside the hospital. Social robots help to reduce caregiver workloads and improve patients’ emotional well-being.

Mobile Robots:

Mobile robots are also very useful in healthcare centers for security as they move around hospitals and clinics following a wire or predefined tracks. For cleaning and disinfection, these robots use ultraviolet (UV) light, hydrogen peroxide vapors, or air filtration to help reduce infection and uniformly sanitize reachable places.

Autonomous Robots:

Autonomous robots have a robust range of depth cameras that can self-navigate to patients in hospital rooms, allowing clinicians to interact from afar. These robots controlled by a remote specialist or an employee can also accompany doctors as they make hospital rounds, allowing the specialist to contribute to on-screen consultation regarding patient diagnostics and care. These robots also can keep track of their batteries and make their way back to charging stations when required.

AI Healthcare System Challenges

Although AI has many potential benefits, it also has significant risks:

  • Error in AI systems 

The major risk is that AI systems can be wrong sometimes, and that can cause patient injuries as well as other healthcare problems. Say, an AI system robot recommends the wrong drug for a patient, fails to notice a tumor on a radiological scan, or assigns a hospital bed to one patient over another. Hence, the patient could be injured due to the programmed robots and there are possibilities of mistakes and risks. Many injuries occur due to medical errors in the healthcare system today, even without the involvement of AI or robots. However, AI errors are potentially different for at least two reasons.

First, patients and providers may react differently to injuries resulting from software than from human mistakes.

Secondly, if AI systems become widespread, an underlying problem in one AI or similar robot system might result in injuries to thousands of patients – rather than the limited number of patients injured by a single provider’s mistake.

  • Data Availability

Training robots require large amounts of data from different sources such as electronic health records, pharmacy records, insurance claims records, or consumer-generated information like fitness trackers or purchasing history. But health data is often tricky. Data is usually segmented across many different systems. Patients usually see different providers and switch insurance companies according to their needs, leading to data split into multiple systems and multiple formats. This segmentation decreases the thoroughness of data sets and increases the expenses of data gathering and the risk of error.

  • Privacy concerns

Another risk is centered around data privacy. The requirement of large datasets creates an impulse for developers to collect healthcare data from many patients. Some patients may be concerned that this collection may violate their privacy. On data-sharing between large health systems and AI developers, patients might consider this a violation of their privacy.

Upcoming Advancements

The advancement of robotics and bionics will be at the forefront of medical innovation in the coming years. We have seen earlier how robotic exoskeletons can assist people recovering from injuries and dealing with partial paralysis.

Robotics and cybernetics will become a regular feature for soldiers in the coming years both on and off the battlefield. But it will be a commercial market where these advancements will become most impactful, especially for patients recovering from severe accidents and injuries.

Another main innovation is neural implants, which are expected to become normal by mid-century. In addition to enabling brain-to-machine and brain-to-brain interfacing, soft and flexible enroot could also be used to address brain injuries and cure neurological diseases.

These advancements could conclude in the development of bionic enhancements, that are identical to the real thing — at least in terms of aspects.

Role of QA 

For maintenance and regular updates of AI robots. It is important to partner with development and QA teams. So that they can update and test the latest AI technology implemented in it. Technology is the biggest transformation in the healthcare sector. Applications are being created to deliver service, anytime anywhere and connected medical devices are helping experts to deliver their services even from a distance.

A few of the prominent reasons for testing in the healthcare sector are functional validation of the software, security of the applications, big data testing in healthcare, AI testing for better QA, usability testing for a better experience, etc.

Cognitive QA automation solutions powered by AI combine the best QA automation techniques to provide better results. Our main objective is to remove test coverage overlaps, optimize efforts with more predictable testing, and finally, move away from defect detection and toward defect prevention.

Finally, we conclude

AI and robotics are evolving the healthcare industry. The role of the health consultant and even the role of the patient is changing. Though some above-described challenges need to be addressed, the benefits outweigh them. Thus AI and robotics are revolutionizing the healthcare sector. To know how you can improve the quality and reliability of healthcare software and mobile applications, contact QASource now.

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