Artificial Intelligence Will Impact Healthcare

Artificial Intelligence is to become a transformative force in healthcare. How providers and patients will get advantage from the influence of AI-driven tools?

Maturity in healthcare industry became a reason for some major changes. From chronic diseases and cancer to radiology and risk assessment, there are nearly endless opportunities to leverage technology to set up more precise, decisive, and impactful interposition at exactly the right time in a patient’s care.

As an evolvement in payment structure, patient’s expectations gets more from their providers, and the volume of feasible data continues to increase at an astounding rate, artificial intelligence is hover to be the engine that drives improvements across the care perpetual.

AI offers an abundant of dominances over usual analytics and clinical decision-making techniques. Learning algorithms can become more precise and accurate as they communicate with training data, acknowledge humans to gain exceptional insights into diagnostics, care processes, treatment variability, and patient outcomes.


It is Obvious that using computer as medium to computer is not a new idea by any means, but without need of mice, monitor and keyboard creating interface between technology and mind is cutting-edge technology, an area of research that has significant applications for some patients.

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. By artificial intelligence this could restore those fundamental experiences to those who feared them lost forever.

Our AI is to be advanced, that for example “If I’m in the neurology ICU on a Monday, and I see someone who has suddenly lost the ability to move or to speak, we want to restore that ability to communicate by Tuesday”.

“By using an artificial intelligence, we could decode the neural activates associated with the intended movement of one’s hand, and we should be able to allow that person to communicate the same way as many people in this room have communicated at least five times over the course of the morning using a mobile communication technology like a tablet computer or phone”.

AI solutions could desperately improve quality of life for patients with its capability, strokes, or locked-in syndrome, as well as the 500,000 people worldwide who experience spinal cord injuries every year.


Radiological images obtained by MRI machines, CT scanners, and x-rays offer non-invasive visibility into the inner workings of the human body. But many diagnostic processes still rely on physical tissue samples obtained through biopsies, which carry risks including the potential for infection.

Artificial intelligence will enable the next generation of radiology tools that are accurate and detailed enough to replace the need for tissue samples in some cases, as our experts predict.

We want to bring together the diagnostic imaging team with the interventional radiologist and the pathologist. That coming together of different teams and aligning goals is a big challenge.

The information from the imaging of tissue samples we want, then we are heading to have to be able to achieve very close registration so that the ground truth for any given pixel is known.

Speed, accuracy, and affordability are paramount to healthcare organizations looking to invest in systems driven by machine learning — and fortunately for providers, developers are pushing ahead rapidly with all three.

Following after in above, may allow clinicians to develop a more accurate understanding of how tumors behave as a whole instead of basing treatment decisions on the properties of a small segment of the hatred.

Providers may also be able to better define the aggressiveness of cancers and target treatments more appropriately.

Artificial intelligence is helping to enable “virtual biopsies” and advance the innovative field of radiomics, which focuses on harnessing image-based algorithms to characterize the phenotypes and genetic properties of tumors.


Shortages of trained healthcare providers, including ultrasound technicians and radiologists can significantly limit access to life-saving care in developing nations around the world.

Artificial intelligence could help mitigate the impacts of this severe deficit of qualified clinical staff by taking over some of the diagnostic duties typically allocated to humans.

For example, AI imaging tools can screen chest x-rays for signs of tuberculosis, often achieving a level of accuracy comparable to humans. This capability could be deployed through an app available to providers in low-resource areas, reducing the need for a trained diagnostic radiologist on site.

The potential for this tech to increase access to healthcare is tremendous. However, algorithm developers must be careful to account for the fact that disparate ethnic groups or residents of different regions may have unique physiologies and environmental factors that will influence the presentation of disease.

As we’re developing these algorithms, it’s very important to make sure that the data represents a diversity of disease presentations and populations — we can’t just develop an algorithm based on a single population and expect it to work as well on others.


EHRs have played an instrumental role in the healthcare industry’s journey towards digitalization, but the switch has brought infinite problems associated with cognitive overload, endless documentation, and user burnout.

In creation of more intuitive interface and automate few routine processes are done within the Artificial Intelligence of HER development, which avoids consumption of much user’s time.

Users spend the majority of their time on three tasks: clinical documentation, order entry, and sorting through the in-basket.

In an improvement of Clinical documentation process, voice recognition and transcription help means a lot but Natural Language Process (NLP) tool might not be going far enough.

We may need to be even bolder and consider changes like video recording a clinical encounter, almost like police wear body cams. And then can use AI and machine learning to index those videos for future information retrieval.

Routine process requests from inbox may take help of Artificial Intelligence like medication refills and result notifications. AI may also work on prioritize tasks that really need clinical attentions. It will be great work for user to go through their to-do lists very easily.


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.

One of the growing threat to global populations is antibiotic resistance, as overuse of these critical drug fosters the evolution of superbugs that no longer respond to treatments. Multi-drug resistant organisms can cause chaotic situation in the hospital setting, and assert thousands of lives every year.

Highlighting patient’s risks before they begin to show symptoms and identifying infection patterns can be done using the help of Electronic health record data. Enhancing accuracy and faster creation can be achieved by leveraging machine learning and AI tools to drive these analytics assists more accurate alerts for healthcare providers.

AI tools can live up to the expectation for infection control and antibiotic resistance.

If hospitals are not able to create smarter, faster, and clinical trail design then the hospitals which were sitting on the mountain of HER data is a big failure for the HER technology. And it is failed to provide fullest potentials to the healthcare industry. It’s more like failure of all our EHR parts.


Seventy percent of all decisions in healthcare are based on a pathology result. Digital pathology and AI has an opportunities to deliver results where between 70 and 75 percent of all the data in an EHR are from a pathology result. Therefore we can achieve more accuracy, obtain right diagnosis, and the better we are going to be.

Identification of nuances that may escape by the human eyes can be analyzed by an analytics drilling down to the pixel level on extreme large in digital images.

We’re now getting to the point where we can actually do a better job of assessing whether a cancer is going to progress rapidly or slowly and how that might change, how patients will be treated based on an algorithm rather than clinical arrange or the histopathologic grade.

Before human clinical reviews the data, identifying of interest in slides productivity can be improved by this powerful Artificial intelligence.

Assessment of what’s important and what is not can be directed by these screen of slides in Artificial Intelligence. This increases an efficiency of the use of pathologist and increases the value of the time they spend for each case.


AI and machine learning are enabling retailers to engage and interact with consumer in a two-way, mutually-beneficial conversation instead of just talking at them.

Current devices in which monitoring patients in an ICO and elsewhere in medical environment are critical. Enhancing the abilities of Artificial Intelligence of identifying decay, advice that sepsis is taking hold, or sense that development of complications can significantly improve outcome and may reduce costs related to hospital-acquired condition penalties.

When we’re talking about integrating disparate data from across the healthcare system, integrating it, and generating an alert that would alert an ICU doctor to intervene early on — the aggregation of that data is not something that a human can do very well.

Inserting intelligent algorithms into these devices can reduce cognitive burdens for physicians while ensuring that patients receive care in as timely a manner as possible.

While ensuring patients receiving care in as timely a manner as possible, inserting intelligence algorithms into these devices to became smart for reduce cognitive burdens to the physicians.


Immunotherapy is one of the most promising avenues for treating cancer. By using the body’s own immune system to attack malignancies, patients may be able to beat stubborn tumors. However, only a small number of patients respond to current immunotherapy options, and oncologists still do not have a precise and reliable method for identifying which patients will benefit from this option.

Machine learning algorithms and their ability to synthesize highly complex datasets may be able to illuminate new options for targeting therapies to an individual’s unique genetic makeup.


EHRs are a goldmine of patient data, but extracting and analyzing that wealth of information in an accurate, timely, and reliable manner has been a continual challenge for providers and developers

Part of the hard work is integrating the data into one place, but another problem is understanding what it is you’re getting when you’re predicting a disease in an HER.

You might hear that an algorithm can predict depression or stroke, but when you scratch the surface, you find what they’re actually predicting is a billing code for stroke. That’s very different from stroke itself.

Relying on MRI results might appear to offer a more concrete dataset but now we have to think about who can afford the MRI, and who can’t? So what we end up predicting isn’t what we thought we were predicting. we might be predicting billing for a stroke in people who can pay for a diagnostic rather than some sort of cerebral ischemia.

EHR analytics have produced many successful risk scoring and stratification tools, especially when researchers employ deep learning techniques to identify novel connections between seemingly unrelated datasets, but ensuring that those algorithms do not confirm hidden biases in the data is crucial for deploying tools that will truly improve clinical care.


Researchers in the United Kingdom have even developed a tool that identifies developmental diseases by analyzing images of a child’s face. The algorithm can detect discrete features, such as a child’s jaw line, eye and nose placement, and other attributes that might indicate a craniofacial abnormality. Currently, the tool can match the ordinary images to more than 90 disorders to provide clinical decision support.

The majority of the population is equipped with pocket-sized, powerful devices that have a lot of different sensors built in, this is a great opportunity for us. Almost every major player in the industry has started to build AI software and hardware into their devices. That’s not a coincidence. Every day in our digital world, we generate more than 2.5 million terabytes of data. In cell phones, the manufacturers believe they can use that data with AI to provide much more personalized and faster and smarter services.

Using smartphones to collect images of eyes, skin lesions, wounds, infections, medications, or other subjects may be able to help underserved areas cope with a shortage of specialists while reducing the time-to-diagnosis for certain complaints.


Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics and clinical decision support tools that clue providers in to 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 dataset. 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.

The process is time-consuming and subjective, and the results may vary with the skill and experience of the individual clinician. Sometimes when we’re looking to see if someone is recovering, we take the data from ten seconds of monitoring at a time. But trying to see if it changed from ten seconds of data taken 24 hours ago is like trying to look if your hair is growing longer.

But if we have an AI algorithm and lots and lots of data from many patients, it’s easier to match up what we’re seeing to long term patterns and maybe detect subtle improvements that would impact our decisions around care.

Leveraging AI for clinical decision support, risk scoring, and early alerting is one of the most promising areas of development for this revolutionary approach to data analysis.

By powering a new generation of tools and systems that make clinicians more aware of nuances, more efficient when delivering care, and more likely to get ahead of developing problems, AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.