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Machine perception is that aspect of digital technology that involves developing computers that can sense or “perceive” the outside world in a way that accurately mimics the five human senses – sight, sound, touch, smell, and taste — as well as taking in information in ways that humans cannot.

As you might imagine, machine perception is an integral part of machine learning and AI, particularly in AI applications that require fast decision-making based on perceiving the surrounding environment, such as autonomous driving.

By definition, perception is the process by which sensory information is captured from the world around us and then interpreted, understood, and organized to make decisions based on the input of the sensory data. In humans, that data is obtained by our sensory organs, such as our eyes, ears, skin, nose, and tongue. These specialized organic receptors transmit their information across neural pathways to the brain to organize and interpret the data they obtain.

In machine learning and AI, a variety of digital sensors are used to replicate or augment the human sense organs. These sensors then work with a complex network of hardware and software that is, in essence, a digital parallel of the nervous system to create “machine perception.”

Machine perception is a cornerstone of every AI sensory model or cognitive digital twin application. The algorithms convert the data gathered from the world into a raw model of what is being perceived by the AI or the twin.

In theory, any direct, computer-based gleaning of information from the world is a kind of machine perception. That is anything from the photoelectric sensors that automatically turn on your car’s headlights at night to how your Roomba vacuum navigates around your living room. Right now, AI and machine perception applications are in development that are designed to emulate each of the human senses, such as:

• Machine or computer vision via optical camera

• Machine hearing (computer audition) via microphone

• Machine touch via tactile sensor

• Machine smell (olfactory) via electronic nose

• Machine taste via electronic tongue

• 3D imaging or scanning via LiDAR sensor or scanner

• Motion detection via accelerometer, gyroscope, or magnetometer

• Infrared and thermal imaging sensors

From this list above, you can see how critical machine perception can be to AI applications such as medical diagnoses, as well as developing truly safe and ubiquitous autonomous vehicles. Innovation in machine perception will also pave the way for next-generation “robotic assistants” or companions.

But developing machine perception is not as easy as it may seem. Computers may be able to solve complex equations and process data vastly superior to humans; however, there are some things that humans still do a lot better than machines. Perception and acting quickly and spontaneously on data from our senses is one of them. Things we do with ease are proving to be very hard to “teach” computers. Take, for example, handwritten text. Handwriting varies greatly from individual to individual, yet, we all can pick up and read handwritten text with no problem for the most part, yet it is difficult to get AI to decern those variables in letter composition.

Similarly, a two-year-old can learn to catch a tossed ball after only a few attempts. But teaching a robot to do the same takes a lot more work. That’s because we are not even sure of the infinite combinations of data processing that almost instantaneously take place as your eyes perceive a ball coming towards you and your brain puts your hand up in time to catch it.

In the 1980s, Hans Moravec, famed member of the Robotics Institute of Carnegie Mellon University in Pittsburgh, described the paradox this way, “It is comparatively easy to make computers exhibit adult-level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.”

However, such limitations will likely be more easily overcome as AI, and machine learning begin to peel away from conventional computers and enter the realm of quantum computing.

Quantum computing holds the promise of exponentially improving the way AI algorithms process, analyze and present sensory findings and predictions and may hold the key to bringing machine perception more analogous to the functionality of the human brain and nervous system.

A number of companies — startups as well as established challengers — are working to make their AI models perceive the world more as humans do, and BigRio is helping to enable much of this advancement.

How BigRio Helps Facilitate Advancement in Machine Learning

Like breakthroughs in machine perception, BigRio looks for and helps to facilitate such innovation in machine learning and AI.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the growing AI investment community.

Because we see so many potential AI innovators, we are also ideally suited to facilitate advancements in machine learning, such as improved machine perception, that will usher in a new generation of autonomous vehicles and other smart machines.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

 

AI is known for its ability to make very accurate predictions. But often, human prognosticators are pretty good at it too! In this article, we take a look at what leading IT experts say they think will be the top five advances in AI and machine learning in 2023, as compiled by The Enterprisers Project.

1. There Will Be Continue Advancement of AI Applications in Healthcare

“AI will yield tremendous breakthroughs in treating medical conditions in the next few years. Just look at the 2021 Breakthrough Prize winner Dr. David Baker. Dr. Baker used AI to design completely new proteins. This ground-breaking technology will continue having huge ramifications in the life sciences, potentially developing life-saving medical treatments for diseases like Alzheimer’s and Parkinson’s.” — Michael Armstrong, Chief Technology Officer, Authenticx.

2. Continued Merging of AI and Quantum Computing

Phil Tee, Co-founder, and CEO, of Moogsoft, says to, “Watch the crossover from fundamental physics into informatics in the guise of quantum and quantum-inspired computing. While I’m not holding my breath for a practical quantum computer, we will see crossover. The mix of advanced mathematics and informatics will unleash a new generation of engineers uniquely placed to exploit the AI wave.”

3. AI Will Not Replace Humans

Despite dozens of sci-fi movies and novels to the contrary, the experts do not believe that AI will replace humans in 2023 and the years ahead, but instead, they expect to see increased interaction between human and artificial intelligence with an increased synergy between the two. “While there will be growing adoption of AI to enhance our collective user experience at scale, it will be balanced with appropriate human intervention. Humans applying the insights provided by AI will be a more effective combination overall than either one doing it alone. How and where this balance is struck will vary depending on the industry and the criticality of the function being performed. For example, radiologists assisted by an AI screen for breast cancer more successfully than they do when they work alone, according to new research. That same AI also produces more accurate results in the hands of a radiologist than it does when operating solo.” – E.G. Nadhan, Global Chief Architect Leader, Red Hat

4. A Move Towards More Ethical AI and an AI Bill of Rights

As we reported earlier this year, the Biden administration had launched a proposed “AI Bill of Rights” to help ensure the ethical use of AI. Not surprisingly, it is modeled after the sort of patient “bills of rights” people have come to expect as they interact with doctors, hospitals, and other healthcare professionals.
David Talby, CTO of John Snow Labs, says to see continued movement in this direction. “We can expect to see a few major AI trends in 2023, and two to watch are responsible AI and generative AI. Responsible or ethical AI has been a hot-button topic for some time, but we’ll see it move from concept to practice next year. Smarter technology and emerging legal frameworks around AI are also steps in the right direction. The AI Act, for example, is a proposed, first-of-its-kind European law set forth to govern the risk of AI use cases. Similar to GDPR for data usage, The AI Act could become a baseline standard for responsible AI and aims to become law next Spring. This will have an impact on companies using AI worldwide.”

5. AI Will Support Increased and “Smarter” Automation

“Everyone understands the value of automation, and, in our software-defined world, almost everything can be automated. The decision point or trigger for the automation, however, is still one of the trickier elements. This is where AI will increasingly come in: AI can make more intelligent, less brittle decisions than automation’s traditional ‘if-this-then-that’ rules.” – Richard Whitehead, CTO, and Chief Evangelist, Moogsoft.

How BigRio Helps Facilitate the Future of AI

At BigRio, we not only agree with these experts on these top five advances in AI that will likely occur in 2023, but we are also actively trying to facilitate them!
We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the growing AI investment community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

The Enterprisers Project is a community and online publication helping CIOs and IT leaders solve problems and drive business value. The Enterprisers Project, supported by Red Hat, also partners with Harvard Business Review.

Most patients and every medical practitioner know that when it comes to chronic debilitating diseases, the earlier they can be detected and treated, the better. This is one of the major promises of AI in healthcare, improved diagnostics for earlier detection and better patient outcomes.

The latest such application comes as researchers are developing an AI solution that can find the early sign of osteoarthritis of the knee. Like many AI-driven diagnostic enhancements, this one can see subtle signs on X-rays better than the human eye. This is critical because x-rays are the primary diagnostic method for early knee osteoarthritis. An early diagnosis can save the patient from unnecessary examinations, treatments, and even knee replacement surgery.

Osteoarthritis is the most common joint-related ailment globally. In Finland alone – where this research took place — it causes as many as 600,000 medical visits every year. It has been estimated to cost the national economy up to EUR1 billion every year.

The new AI-based method was trained to detect a radiological feature predictive of osteoarthritis from x-rays. The method was developed in cooperation with the Digital Health Intelligence Lab at the University of Jyvaskyla as a part of the AI Hub Central Finland project. It utilizes neural network technologies that are widely used globally.

“The aim of the project was to train the AI to recognize an early feature of osteoarthritis from an x-ray. Something that many experienced doctors can visually distinguish from the image, but cannot be done automatically, and is often missed by the untrained eye,” explains Anri Patron, the researcher responsible for the development of the method.

The anomaly the AI has been trained to automatically detect is to see if there is “spiking” on the tibial tubercles in the knee joint or not. Tibial spiking is known to be an early sign of osteoarthritis.

The researchers say that the AI matched human doctors’ assessment of the presence of spiking in nearly 90% of cases, instantly, without the need to scrutinize and deeply examine the x-rays as the human orthopedic surgeons did.

The research offers definitive proof that AI can support early diagnosis of osteoarthritis at the point of primary healthcare before a patient is referred to an orthopedic specialist, which can make a major difference in catching and treating knee arthritis early.

“If we can make the diagnosis in the early stages, we can avoid uncertainty and expensive examinations such as MRI scanning. In addition, the patient can be motivated to take measures to slow down or even stop the progression of symptomatic osteoarthritis. In the best possible scenario, the patient might even avoid joint replacement surgery,” sums up professor of surgery Juha Paloneva, one of the Finnish researchers on the project.

How BigRio Helps Healthcare AI Startups

Like the technology developed by the researchers with the Central Finland Health Care District, BigRio is also a facilitator and incubator for AI startups, particularly in healthcare. In fact, it was my father’s own battle with and eventual death from lung disease that set me on my path to finding ways to use AI to improve healthcare delivery.
Eventually, among our other success stories, we did collaborate with a researcher who is in the process of developing a cognitive digital twin of the human lung. Right now, that technology is being used specifically in the realm of testing inhalers for asthma patients, but like the UWS tool, it has broader implications for better diagnostics and treatments for COPD and other lung diseases.
We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the biomedical community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

Some of the most innovative advances in AI are not taking place in the US; there are some powerful incubators all through Europe and places such as Israel. In fact, six of the most impressive new AI startups are all based in Austria!

Those that follow the IT industry have long pointed out that Austria has been the up-and-coming “Silicon Valley” of Europe, attracting very high-caliber talent and enabling some impressive AI startups. Here are six quite noteworthy AI startups that are located in Austria.

1. Adverity
Adverity, founded in 2015, assesses and visualizes expenses, performance, and returns. The program integrates campaign data from hundreds of data sources, including LinkedIn, Google Ads, and Facebook, before sending the findings to business management systems via native connectors. According to Marktechpost, the California-based AI news hub, the platform is used by well-known companies like Red Bull, IKEA, and Zurich Insurance and is accessible to agencies, brands, and e-commerce providers.

2. Robart
Also established in Austria, Robart provides full AI navigation solutions for mobile robots, including hardware, software, and connected IoT applications such as your robotic vacuum and similar devices. Using Robart’s solutions, robots can map a whole home, categorize it into rooms, design particular cleaning procedures, identify deviations like an open window, learn user patterns, and adjust to changes in user habits.

3. Cortical.io
Cortical.io provides free access to rudimentary tools like term disambiguation, text comparison, and keyword extraction on its website. The developers claim that such tools can reduce the time-consuming task of reviewing complex documents such as legal contracts by as much as 80%.

4. Medicus AI
Medicus AI created a medical app that explains and analyzes blood tests and medical reports and gives customers individualized healthcare recommendations in line with its results. Like Adverity the company was also founded in Austria in 2015. Medicus AI supports hospitals, diagnostic labs, and other healthcare facilities by improving interactions with patients by providing simple-to-understand health reports, guiding patients through disease treatment and prevention, as well as providing remote monitoring of patient behavior to ensure compliance and enhance long-term health outcomes.

5. Semanticlabs
Austria-based Semanticlabs creates tools for large-scale data analyses, such as semantic algorithms. The company has developed out-of-the-box solutions for collaborative document management, automated tagging, and topic extraction from text using techniques for natural language processing. According to Marktechpost, their customers include Kronen Zeitung, the largest newspaper in Austria, and Erste Group, one of the largest financial service providers in Central and Eastern Europe.

6. Scarlet red
Scarletred is another AI healthcare solution designed to improve diagnostics, in this case, for the remote detection of various skin disorders. The company was founded in 2014. The platform comprises a web tool for picture processing, an iOS app, and a skin patch that calibrates photographs for different light and distance situations. Patients upload a photo of the region being examined and their skin tag to their healthcare provider’s online portal using the iOS app. The automated skin area analysis is then performed by the web-based analytical platform using computer vision.

How BigRio Helps Facilitate Investment in AI Startups
Like the agencies and investors who are helping places like Austria become hubs of AI innovation, BigRio is also a powerful facilitator and incubator for AI startups in the US and around the world. We specialize in bringing healthcare AI solutions such as Medicus AI and Scarletred mentioned above to market.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people who have a vested interest in advancing AI and Machine learning technologies.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

Urinary tract infections, commonly known as UTIs, usually do not pose serious health risks – when they are detected and treated early. However, when allowed to advance undetected past a certain point, a number of serious adverse outcomes can result from late or misdiagnosis of UTI.

A group of researchers from the University of Edinburgh and Heriot-Watt University are developing artificial intelligence and “socially assistive robots” to detect urinary tract UTIs earlier and ensure better patient outcomes.

UTIs affect 150 million people worldwide annually, making it one of the most common types of infection. When diagnosed early, it can be treated with antibiotics. If left untreated, UTIs can lead to sepsis, kidney damage, and even loss of life.

Diagnosis, however, can be difficult with lab analysis, a process taking up to 48 hours, providing the only definitive result. Early signs of a UTI can also be challenging to recognize because symptoms vary according to age and existing health conditions. There is no single sign of infection but a collection of symptoms which may include pain, fever, increased need to urinate, changes in sleep patterns, and tremors.

To address these concerns, the researchers are working with two industry partners from the care sector who are helping the scientists to develop machine learning methods and interactions with socially assistive robots to support earlier detection of potential infections and raise an alert for investigation by a clinician.

The project will gather continual data about the daily activities of individuals in their homes via sensors that could help spot changes in behavior or activity levels and trigger an interaction with a socially assistive robot. Known as “FEATHER,” the AI platform will combine and analyze these data points to flag potential infection signs before an individual or caretaker is even aware that there is a problem. Behavioral changes that could indicate UTI include changes in walking pace, increased frequency of urination, changes in cognitive function, or a change in sleep patterns, all of which could be noticed and documented by interaction with the assistive robot.

The AI and implementation aspects of the project will be led by Professor Kia Nazarpour, Dr. Nigel Goddard, and Dr. Lynda Webb from the University of Edinburgh. The Human-Robot Interaction aspects will be led by Professor Lynne Baillie, assisted by Dr. Mauro Dragone, from Heriot-Watt University.

Professor Kia Nazarpour, project lead and Professor of Digital Health at the School of Informatics, University of Edinburgh, said, “This unique data platform will help individuals, caretakers, and clinicians to recognize the signs of potential urinary tract infections far earlier, helping to prompt the investigations and medical tests needed. Earlier detection makes timely treatment possible, improving outcomes for patients, lowering the number of people presenting at hospital, and reducing costs to the NHS.”

How BigRio Helps Bring Advanced AI Solutions to Healthcare

Like the FEATHER project, improving disease detection, medical imaging, and diagnostics is an area where AI and machine learning are making one of the technology’s biggest impacts.

BigRio prides itself on being a facilitator and incubator for such advances in leveraging AI to improve diagnostics. In fact, it was my father’s own battle with and eventual death from lung disease that set me on my path to finding ways to use AI to provide earlier detection of serious medical conditions for improved patient outcomes.

Eventually, among our other success stories, we did collaborate with a researcher who is in the process of developing a cognitive digital twin of the human lung. Right now, that technology is being used specifically in the realm of testing inhalers for asthma patients, but like the FEATHER UTI detection tool, it has broader implications for better diagnostics and treatments for COPD and other lung diseases.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the biomedical community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

Artificial Intelligence has been promising to revolutionize healthcare for quite some time; however, one look at any modern hospital or healthcare facility, and it is easy to see that the revolution is already here.

In almost every patient touchpoint AI is already having an enormous impact on changing the way healthcare is delivered, streamlining operations, improving diagnostics, and improving outcomes.

Although the deployment of AI in the healthcare sector is still in its infancy, it is becoming a much more common sight. According to technology consulting firm, Gartner, healthcare IT spending for 2021 was a hefty $140 billion worldwide, with enterprises listing “AI and robotic process automation (RPA)” as their lead spending priorities.

Here, in no particular order or importance, are seven of the top areas where healthcare AI solutions are being developed and currently deployed.

1. Operations and Administration
A hospital’s operation and administration expenses can be a major drain on the healthcare system. AI is already providing tools and solutions that are designed to improve and streamline administration. Such AI algorithms are proving to be invaluable for insurers, payers, and providers alike. Specifically, there are several AI programs and AI healthcare startups that are dedicated to finding and eliminating fraud. It has been estimated that healthcare fraud costs insurers anywhere between $70 billion and $234 billion each year, harming both patients and taxpayers.

2. Medical Research
Probably one of the most promising areas where AI is making a major difference in healthcare is in medical research. AI tools and software solutions are making an astounding impact on streamlining every aspect of medical research, from improved screening of candidates for clinical trials, to targeted molecules in drug discovery, to the development of “organs on a chip” – AI combined with the power of ever-improving Natural Language Processing (NLP) is changing the very nature of medical research for the better.

3. Predictive Outcomes and Resource Allocation
AI is being used in hospital settings to better predict patient outcomes and more efficiently allocate resources. This proved extraordinarily helpful during the peak of the pandemic when facilities were able to use AI algorithms to predict upon admission to the ER, which patients would most benefit from ventilators, which were in very short supply. Similarly, a Stanford University pilot project is using AI algorithms to determine which patients are at high risk of requiring ICU care within an 18 to 24 hours period.

4. Diagnostics
AI applications in diagnostics, particularly in the field of medical imaging, are extraordinary. AI can “see” details in MRIs and other medical images far greater than the human eye and, when tied into the enormous volume of medical image databases, can make far more accurate diagnoses of conditions such as breast cancer, eye disease, heart, and lung disease and so much more. AI can look at vast numbers of medical images and then identify patterns in seconds that would take human technicians hours or days to do. AI can also detect minor variations that humans simply could not find, no matter how much time they had. This not only improves patient outcomes but also saves money. For example, studies have found that earlier diagnosis and treatment of most cancers can cut treatment costs by more than 50%.

5. Training
AI is allowing medical students and doctors “hands-on training” via virtual surgeries and other procedures that can provide real-time feedback on success and failure. Such AI-based training programs allow students to learn techniques in safe environments and receive immediate critique on their performance before they get anywhere near a patient. One study found that med students learned skills 2.6 times faster and performed 36% better than those not taught with AI.

6. Telemedicine
Telemedicine has revolutionized patient care, particularly since the pandemic, and now AI is taking remote medicine to a whole new level where patients can tie AI-driven diagnostic tools through their smartphones and provide remote images and monitoring of changes in detectable skin cancers, eye conditions, dental conditions and more. AI programs are also being used to remotely monitor heart patients, diabetes patients, and others with chronic conditions and help to ensure they are complying with taking their medications.

7. Direct treatment
In addition to adding better clinical outcomes with improved diagnostics and resource allocation, AI is already making a huge difference in the direct delivery of treatments. One exciting and extremely profound example of this is robotic/AI-driven surgical procedures. Minimally invasive and non-invasive AI-guided surgical procedures are already becoming quite common. Soon, all but some of the most major surgeries, such as open heart surgeries, can and will be done as minimally invasive procedures, and even the most complex “open procedures” will be made safer, more accurate, and more efficient thanks to surgical AI and digital twins of major organs such as lungs and the heart.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.
BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.