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Here Are 4 Real-World Examples of AI in the Healthcare Industry

Here Are 4 Real-World Examples of AI in the Healthcare Industry

With regards to our health, particularly in matters of life and death, the promise of AI to improve results is interesting. While there is still a lot to do to achieve AI-dependant healthcare, most strikingly data privacy concerns and fears of mismanaged care because of machine error and lack of human expertise, there is adequate potential that legislatures, tech organizations, and healthcare providers are willing to contribute and test out AI-powered tools and solutions. Here are four of the AI advances in healthcare that seem to have the most potential.

  1. Robotic Surgery, With AI

With an estimated value of $40 billion to healthcare, robots can analyze data from pre-operation medical records to guide a surgeon’s instrument during a medical procedure, which can prompt a 21% decrease in a patient’s hospital stay. Robot-assisted surgery is considered “minimally intrusive” so patients won’t have to heal from enormous cuts. By means of AI, robots can use data from past operations to stay put to new surgical procedures. The positive outcomes are indeed promising. One study that included 379 orthopedic patients found that AI-assisted robotic procedure brought about five times fewer complications contrasted with surgeons operating alone. A robot was used on eye surgery for the first time, and the most advanced surgical robot, the Da Vinci allows doctors to perform complex procedures with more prominent control than traditional approaches. Heart surgeons are assisted Heartlander, a miniature robot, that enters a small incision on the chest to perform mapping and therapy over the surface of the heart.

  1. Virtual Nursing Assistants

From communicating with patients to directing patients to the most effective care setting, virtual nursing assistants could save the healthcare industry $20 billion every year. Since virtual nurses are available day in and day out, they can respond to questions, monitor patients and give fast answers. Most applications of virtual nursing assistants today consider regular communication among patients and care providers between office visits to prevent hospital readmission or pointless hospital visits. Care Angel’s virtual nursing assistant can even give health checks through voice and AI.

  1. Clinical Judgment or Diagnosis

In fact, using AI to diagnose patients is undoubtedly in its earliest stages, yet there have been some exciting use cases. A Stanford University study tested an AI algorithm to skin cancers against dermatologists, and it performed at the level of humans. A Danish AI software company tested its deep learning program by having a computer eavesdrop while human dispatchers accepted emergency calls. The algorithm analyzed what an individual says, the manner of speaking and background noise and identified heart failures with a 93% success rate contrasted with 73% for humans. Baidu Research as of late announced that the consequences of early tests on its deep learning algorithm demonstrate that it can outperform humans when identifying breast cancer metastasis. PM Theresa May announced an AI revolution would help the National Health To support (NHS), the UK’s healthcare system, foresee those in an early stage of cancer to eventually forestall thousands of cancer-related deaths by 2033. The algorithms will look at medical records, habits and genetic information pooled from health charities, the NHS and AI.

  1. Image Analysis

At present, image analysis is very tedious for human providers, however, an MIT-led research team developed an ML algorithm that can analyze 3D scans up to 1,000 times faster than what is possible today. This near real-time assessment can give critical input for surgeons who are operating. It is also hoped that AI can improve the next generation of radiology tools that don’t rely on tissue samples. Furthermore, AI image analysis could bolster remote areas that don’t have easy access to healthcare providers and even make telemedicine increasingly successful as patients can use their camera phones to send in pics of rashes, cuts or wounds to determine what care is required.

Final Thoughts

In the extremely intricate world of healthcare, AI tools can support human providers to give faster service, diagnose issues and analyze data to identify trends or hereditary data that would incline somebody to a particular illness. When saving minutes can mean saving lives, AI and ML can be transformative for healthcare as well as for every single patient.

About the author

Peter Gunnell

Peter is a reputed freelance medical and healthcare writer and editor with over 2 decades of experience. He has won several writing and journalism awards for his contribution. An expert at meeting deadlines, He is proficient at writing and editing educational articles for both consumer and scientific spectators, as well as patient education materials.

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