How AI can improve current medical field?
The level of acceleration of growth in various industries has been pretty quick and sometimes totally unpredictable.
Artificial Intelligence (AI), the most advanced technology, holds particular potential for improving medical care at the clinical level.
In computer science, AI research is defined as the study of "intelligent agents" - any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism.
AI is a collection of multiple technologies that mimic human’s cognitive functions that humans associate with other human minds, such as "learning" and "problem-solving".
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering, and operations research.
It's possible to apply AI to both structured and unstructured data, with techniques including machine learning and natural language processing.
Apart from all the industries, it has been touching. You can say that the modern healthcare industry has been receiving paramount importance. There has been a paradigm shift in the way patients are treated by doctors because they now have inordinate amounts of data in their hands, and a good amount of this data can be put to good use.
The technology is widely used in all kinds of health-related aspects, but it is also important to note that the largest concentrated usage is cardiology, neurology, and oncology. Nurses and doctors have started adopting the advanced technology to reduce manual work, and to provide more accurate service and impactful interventions to patients.
Artificial intelligence is poised to take this to the next level.
It can, for example, reduce the time required to analyze a bacterial swab and recommend a suitable antibiotic prescription. This gives the physician more time and mental energy to perform higher-level functions such as patient education and clinical assessment.
Governments, tech companies, and healthcare providers are willing to invest and test out AI-powered tools and solutions.
Here are some AI advances in healthcare -
Robot-assisted surgery is considered "minimally invasive" so patients won't need to heal from large incisions. Via artificial intelligence, robots can use data from past operations to inform new surgical techniques. It can lead to a 21% reduction in a patient's hospital stay. One study explained that 379 orthopedic patients found that AI-assisted robotic procedure resulted in five times fewer complications compared to surgeons operating alone. A robot was used on an eye surgery for the first time, and the most advanced surgical robot, the Da Vinci allows doctors to perform complex procedures with greater control than conventional approaches.
Neurological diseases and trauma to the nervous system can take away some patients’ abilities to speak, move, and interact meaningfully with people and their environments. Brain-computer interfaces (BCIs) backed by artificial intelligence could restore those fundamental experiences to those who feared them lost forever. Brain-computer interfaces could drastically improve the quality of life for patients with ALS, strokes, or locked-in syndrome, as well as the 500,000 people worldwide who experience spinal cord injuries every year.
Virtual nursing assistants could save the healthcare industry $20 billion annually. Since virtual nurses are available 24/7, they can answer questions, monitor patients and provide quick answers. Most applications of virtual nursing assistants today allow for more regular communication between patients and care providers between office visits to prevent hospital readmission or unnecessary hospital visits.
Antibiotic resistance is a growing threat to populations around the world as overuse of these critical drugs fosters the evolution of superbugs that no longer respond to treatments. Multi-drug resistant organisms can wreak havoc in the hospital setting, and claim thousands of lives every year. Electronic health record data can help to identify infection patterns and highlight patients at risk before they begin to 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.
Using AI to diagnose patients is undoubtedly in its infancy, but there have been some exciting use cases. A Stanford University study tested an AI algorithm to detect skin cancers against dermatologists, and it performed at the level of the humans. The algorithm analyzed what a person says, the tone of voice and background noise and detected cardiac arrests with a 93% success rate compared to 73% for humans.
Medical devices and machines
In the medical environment, smart devices are critical for monitoring patients in the ICU and elsewhere. Using artificial intelligence to enhance the ability to identify deterioration, suggest that sepsis is taking hold, or sense the development of complications can significantly improve outcomes and may reduce costs related to hospital-acquired condition penalties. Inserting intelligent algorithms into the devices can reduce cognitive burdens for physicians while ensuring that patients receive care in as timely a manner as possible.
AI can impact healthcare is to automate administrative tasks. It is expected that this could result in $18 billion in savings for the healthcare industry as machines can help doctors, nurses and other providers save time on tasks. Technology such as voice-to-text transcriptions could help order tests, prescribe medications and write chart notes. For example, IBM uses IBM’s Watson to mine big data and help physicians provide a personalized and more efficient treatment experience.
Clinical decision making
Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics and clinical decision support tools that clue providers into problems long before they might otherwise recognize the need to act. AI can provide earlier warnings for conditions like seizures or sepsis, which often require intensive analysis of highly complex datasets. Machine learning can also help support decisions around whether or not to continue care for critically ill patients, such as those who have entered a coma after cardiac arrest.
Image analysis is very time consuming for human providers, but an MIT-led research team developed a machine-learning algorithm that can analyze 3D scans up to 1,000 times faster than what is possible today. This near real-time assessment can provide critical input for surgeons who are operating. It is also hoped that AI can help to improve the next generation of radiology tools that don’t rely on tissue samples.
Reduced mortality rate
Introducing systems that clinicians can access through AI will help reduce death rates by prioritizing those in more urgent need. It can also help by recommending individualized treatments, as this plays an ever greater role in medical care.
Developing a new drug through clinical trials is often a very costly affair. But, the efficient supercomputer-fueled AI can root out new drugs from a database of molecular structures that no human could ever dare to analyze. A prominent example is Atomwise AI, which was able to predict two drugs that could put a stop to the Ebola virus epidemic. In less than one day, their virtual search was able to find two safe, already existing medicines that could be repurposed to fight the deadly virus.
As AI applications become increasingly integrated with medicine, more and more people will gain access to high-quality, efficient healthcare.