Could AI Be Used to Quash the Opioid Epidemic?

Opioid addiction and opioid-involved deaths have been on the rise in America over the past few decades, so much so that the Centers for Disease Control have rightly labeled it an “epidemic.” One researcher has thought of a novel approach to solve the opioid crisis, and it involves leveraging the power of AI to find alternative painkillers that can be just as effective but non-addictive.

The National Institute on Drug Abuse has awarded $1.5 million over five years to researcher Benjamin Brown of the Vanderbilt Center for Addiction Research and the Center for Applied Artificial Intelligence in Protein Dynamics. Brown developing an AI solution to analyze billions of potential opioid drugs to reveal detailed insights into how they interact with key proteins. Brown says that he views the opioid problem on a molecular level. Painkillers used legitimately in medicine, such as oxycodone, are highly addictive, but a better understanding of how their molecules interact with proteins in the body could lead to the formulation of nonaddictive alternatives.

Brown says he is focusing his research on “Mu-opioid receptors,” which are signaling proteins in the central nervous system that bind with opioids. These receptors modulate pain, stress, mood, and other functions. Drugs that target these receptors are among the most powerful analgesics, but they also are the most addictive.

Brown’s AI platform models drug-protein interactions in a way that accounts for their dynamic physical movements. These movements called “conformational changes,” can occur in milliseconds and make a big difference in how a protein behaves and binds or interacts with a small molecule drug.

By using AI to model this motion, the algorithms can more effectively predict how tightly proteins and drugs will interact and the effects of this interplay. This information is used to screen billions of potential drugs-an unprecedented scale for highly dynamic proteins-or design new ones with properties that lead to fewer addictive side effects.

How Damo Helps Bring Advanced AI Solutions to Healthcare

We share Ben Brown’s belief in the transformative power of AI, particularly in the areas of medical research and drug development.

However, Damo Consulting recognizes that digital maturity varies widely across enterprises and technology solution providers. We are proud to help clients develop digital transformation roadmaps that can be implemented with informed technology choices to meet organizational objectives.

We bring deep industry knowledge, market insights, and technology skills to help develop and implement enterprise digital roadmaps. The companies that we have recognized in the past with the DigiM Award and will do again with the 2023 nominees and recipients represent those that are leading the way with best-in-class programs for digital health, technology-led innovation, and organizational governance models to drive the healthcare industry’s transformation to a digital future.

Damo Consulting focuses exclusively on the healthcare market with a strong understanding of the provider and payer space. We make it our mission to explore, understand, and evaluate the most pressing issues at the intersection of healthcare and technology, specifically in the context of digital transformation in healthcare.

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

Since 2012, Damo Consulting has been working with leading healthcare enterprises on technology strategy and digital transformation. The firm has worked with some of the leading technology firms and emerging health IT companies to transform their brands and accelerate growth. For more information, visit Damo Consulting.

Can AI Help Deliver on the Promise of Value-Based Care in Medicine?

To say that 2023 was a profoundly transformative year for AI in healthcare would be an understatement. Most significantly, 2023 was the year when, for the first time, generative AI (GAI) tools became widely available to businesses and consumers, and their impact and rate of adoption in healthcare and drug discovery have been unprecedented. Now, as GAI becomes more and more ubiquitous in medicine, will AI prove to be the launching pad for the much-talked-about Value-Based Care (VBC) model long sought after by the healthcare industry?

VBC is a macro trend that is moving U.S. healthcare from a system driven by volume, i.e., a “fee-for-service” model, to one driven by patient outcomes and care quality. VBC emphasizes and incentivizes overall health but also poses administrative, clinical, and financial challenges for healthcare payers, self-funded employers, and providers.

However, GAI is poised to change all that and perhaps finally deliver on VBC’s promise. AI has tremendous potential to address the technology challenges of VBC and empower healthcare’s transition from volume to value.

AI technology—with the ability to aggregate massive volumes of data, uncover analytic insights, streamline repetitive tasks, minimize errors, and improve care quality—is already improving our ability to offer better care at a lower cost, which will lead to a successful VBC future. This is made even more possible when you add the power and potential of GAI and Large Language Model (LLM) solutions into the model. Such applications are quite capable of improving efficiency and reducing administrative burdens for clinical and non-clinical healthcare professionals. As GAI and LLM-driven tools become more sophisticated, they can take over repetitive and time-consuming tasks, such as manual data entry and reporting. That frees up human resources to focus on patients’ and members’ needs – the cornerstone of VBC.

Bessemer Business Partners, in its recently published 2024 Healthcare and Life Sciences Predictions, stated it believes that “This year, we predict we’ll see continued iteration in VBC models focused on specialties like cardiology, neurology, nephrology, and oncology. Innovation will be propelled by evolving payment models and newly approved high-cost therapeutics and diagnostics. For example, we expect to see new companies rise to meet the need to diagnose patients with early-onset Alzheimer’s and other neurodegenerative diseases after a new wave of neurodegenerative drugs like Leqembi are widely available.”

However, as with much we have written about AI and healthcare, for VBC to live up to its potential, all of this must be approached with serious consideration of transparency and ethics. The stakes are extremely high in healthcare, so there must be accountability to ensure patient and member safety. In fact, Bessemer included on its list of predictions that “It’s conceivable that the next largest healthcare AI startup could be a compliance-focused platform for monitoring privacy, data, and model assets in the wild.”

How Damo Helps Bring Advanced AI Solutions to Healthcare

We share the belief in the transformative power of GAI, particularly in the areas of medical research and the delivery of healthcare.

However, Damo Consulting recognizes that digital maturity varies widely across enterprises and technology solution providers. We are proud to help clients develop digital transformation roadmaps that can be implemented with informed technology choices to meet organizational objectives.

We bring deep industry knowledge, market insights, and technology skills to help develop and implement enterprise digital roadmaps. The companies that we have recognized in the past with the DigiM Award and will do again with the 2023 nominees and recipients represent those that are leading the way with best-in-class programs for digital health, technology-led innovation, and organizational governance models to drive the healthcare industry’s transformation to a digital future.

Damo Consulting focuses exclusively on the healthcare market with a strong understanding of the provider and payer space. We make it our mission to explore, understand, and evaluate the most pressing issues at the intersection of healthcare and technology, specifically in the context of digital transformation in healthcare.

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

Since 2012, Damo Consulting has been working with leading healthcare enterprises on technology strategy and digital transformation. The firm has worked with some of the leading technology firms and emerging health IT companies to transform their brands and accelerate growth. For more information, visit Damo Consulting.

Canada Uses AI to Solve Healthcare Worker Shortage!

Canadian healthcare workers turn to AI for help amid a staffing crisis!

Many countries, including the US, are facing a healthcare staffing crisis, particularly of medical support staff like skilled nurses. This shortage has only gotten worse since the COVID-19 pandemic. Canadian healthcare officials are turning to AI as a novel way to solve the problem.

The flagship example is an AI-driven system implemented by St. Michael’s Hospital in Toronto known as  CHARTWatch. Doctors and hospital administrators describe it as an “AI-powered early-warning system,” and so since its inception, it has made a real difference and has saved many lives. Case in point, last year, during one of her shifts on the internal medicine unit, Yuna Lee received an alert on her phone from CHARTWatch, indicating that a patient in the ward was at high risk of dying or needing intensive care.

Dr. Lee, the division head of general internal medicine, checked on the woman and found nothing obviously amiss. She ordered extra blood tests just to be safe. The results revealed the patient’s liver enzymes were elevated, prompting Dr. Lee to call for an ultrasound of her liver.

As the patient was about to be transferred to the imaging department, she spiked a fever and developed pain in her abdomen – the first overt symptoms of what turned out to be an inflamed gallbladder. CHARTWatch, which was developed by St. Michael’s data science team and analyzes hundreds of points of patient data to produce hourly risk scores, had figured out something was seriously wrong before doctors or nurses did!

“That was very surprising,” Dr. Lee said. “It made me go, ‘Wow, CHARTWatch is amazing.’ ”

In the two years since CHARTWatch’s launch, St. Michael’s general internal medicine unit has experienced a nearly 36-per-cent reduction in the relative risk of death among non-palliative patients compared with the same period in the four previous years.

“I am absolutely convinced that advanced data analytics and artificial intelligence is going to transform health care as we know it,” said Tim Rutledge, the president and chief executive officer of Unity Health, the network that includes St. Michael’s, St. Joseph’s Health Centre, and Providence Healthcare. “If we can automate tasks that are now laborious, it allows our clinicians to spend more quality time interacting with patients.”

Rutledge’s assessment is exactly why Canadian healthcare officials believe AI can help alleviate the country’s staffing crisis by taking rote tasks such as writing clinical notes off the plates of overworked nurses and doctors.

Along with CHARTWatch, other AI solutions developed at St. Michael’s include a tool for assigning emergency department nurses to different posts, such as triaging patients or working in the ER’s resuscitation bay. That assigning task, which used to require hours of manual input on an Excel spreadsheet, is now done by an algorithm in less than 15 minutes.

Another tool analyzes patient information that triage nurses punch into their computers and uses the data to produce wait-time estimates that flash on a screen in the ER, cutting down on the number of times harried staff members are asked, “How long will it be?”

Yet another project synthesizes the electronic medical records of patients with multiple sclerosis into a concise, visual timeline that is particularly helpful for junior doctors who may only have a 10-minute window to prepare for an appointment. The model can summarize seven years’ worth of charts in less than two seconds.

As it is in the US, Generative AI and Large Language Model (LLM) solutions are poised to be the next big thing in Canada for healthcare. St. Michale’s in-house AI R&D lab is developing an LLM-powered medical chatbot and automated clinical note generator called “Clinical Camel.”

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

When it comes to leveraging GAI and LLMs for healthcare, there are two primary approaches: utilizing existing models developed by big tech companies like Google or taking the route of St. Michael’s in Canada and developing your own property LLM solutions.

Of course, it is much easier to use an off-the-shelf LLM solution; however, while these “open source” GAI/LLM solutions like ChatGPT have gained significant attention across various fields, including healthcare, they are limited by their need to be non-specific in scope and ability.

Of course, there are many advantages to building an LLM model for your healthcare organization’s unique targets and needs. But not every hospital has the luxury of an in-house AI R&D lab like the one that developed CHARTWatch; however, that’s where BigRio can Help!

Creating a large language model from scratch requires extensive resources, the expertise of AI developers and data scientists, the MLOps team, and computational power. It involves training the model on massive datasets, fine-tuning it through multiple iterations, and optimizing its performance. This process demands substantial time, expertise, and computational resources, including high-performance hardware and storage systems. The good news is that the BigRio team can offer you all of the above and more!

BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery, originally in the field of diagnostics and research. We have recently been focusing our efforts on supporting startups and developing our own solutions that use LLMs and GAI to improve those areas of healthcare as well as in direct patient interactions and customer relationship management.

NEW GAI WORKSHOP LAUNCHED:

https://www.damoconsulting.net/gai-workshops-for-healthcare-providers/

You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

Rohit Mahajan is a Managing Partner with BigRio. He has 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.

What Will Quantum Computing Really Bring to AI in Healthcare?

The marriage between quantum computing and AI promises to be the next big thing in IT, probably no more so than how it will impact and advance AI applications for healthcare.

AI is already being used in many areas of healthcare, from analyzing medical images to developing personalized treatment plans for patients. While quantum computing is still in its infancy, it has the potential to solve complex problems much faster than classical computers. When these two technologies are combined as is beginning to be realized, the possibilities are endless, particularly in healthcare delivery and drug research.

The revolution is already beginning. For example, Moderna and IBM recently announced a partnership that will explore various use cases of quantum computing within the life sciences, particularly mRNA medicine design.

As part of the collaboration, both organizations will employ MoLFormer, an AI-based foundation model, to predict the properties of molecules and gain insights into the characteristics of potential mRNA medicines.

What Makes Quantum Computers So Special?

Quantum computing operates by substituting classical computing’s bits with quantum bits, commonly referred to as “qubits.” Unlike bits, which can only store binary values of 0 or 1, qubits can exist as a superposition of both 0 and 1 simultaneously. This is made possible through a phenomenon in quantum mechanics called entanglement. This gives them computing power vastly superior to even today’s fastest supercomputers. Quantum computers’ ability to process vast amounts of data very quickly and very intuitively makes them particularly useful for advanced AI algorithms, particularly the kinds that are used in healthcare diagnostics and medical research where analyzing vast amounts of data for often minute details are required.

Let’s drill down on what quantum computing and AI when combined, can mean for healthcare delivery and for drug discovery.

Healthcare Delivery

Quantum computing can be used to process large amounts of data from medical records, electronic health records, and other sources to provide personalized healthcare services. The use of AI and quantum computing in healthcare delivery can also help in developing predictive models that can forecast the likelihood of certain diseases or health conditions. This information can help healthcare providers take proactive measures to prevent or treat diseases early, thus reducing the overall cost of healthcare and improving patient outcomes.

Additionally, AI can be used to develop chatbots that can provide immediate responses to patients’ queries and concerns. Chatbots can help reduce the workload on healthcare providers and provide quick and efficient responses to patients’ questions, thus improving patient satisfaction and overall healthcare experience.

Drug Research

As evidenced by the announced partnership between Moderna and IBM, drug discovery and pharmaceutical research is where quantum computing and AI is posed to make their most significant leap.

The development of new drugs is a complex and time-consuming process that can take years or even decades to complete. However, the combination of AI and quantum computing can significantly speed up this process. Quantum computing can be used to simulate chemical reactions and predict the properties of new molecules, making it possible to identify promising drug candidates faster.

AI can then be used to analyze the vast amounts of data generated by quantum computing simulations to identify the most promising drug candidates for further testing. This approach can significantly reduce the time and cost of drug development and bring new treatments to market that much faster.

The combination of AI and quantum computing has the potential to revolutionize healthcare delivery and drug research. It can help healthcare providers deliver personalized healthcare services, develop predictive models, and improve patient outcomes. It can also significantly speed up the drug development process and bring new treatments to the market much faster. While there are challenges that need to be addressed, the possibilities are endless, and the future of healthcare looks promising with the integration of these two technologies.

How Big Rio Can Help

Quantum computing is still very much an emerging technology with large-scale and practical applications still a way off. However, the technology is steadily graduating from the lab and heading for the marketplace. In 2019, Google announced that it had achieved “quantum supremacy.”

Much like the partnership between Moderna and IBM BigRio has partnered with our sister company, Citadel Discovery, to use AI to advance drug discovery. Citadel was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research streamlining and lowering the costs of drug discovery and biological research.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

You can read much more about how AI is redefining drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint, with a particular emphasis on drug discovery and Pharma research.

Rohit Mahajan is a Managing Partner with BigRio. He has 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.

What the Integration of Cognitive Digital Twin Tech and IoT Will Mean for Consumers and Industry

The integration of cognitive digital twin technology with the Internet of Things (IoT) has the potential to revolutionize the marketplace by providing companies with valuable insights into their products and processes.

What is Cognitive Digital Twin Technology?

Cognitive digital twin technology is a virtual model of a physical system that uses data and artificial intelligence (AI) to simulate and predict the behavior of that system. This technology combines data from sensors and other sources with machine learning algorithms to create a digital representation of a physical system.

A cognitive digital twin model can be used to monitor and analyze the behavior of a system in real-time, and it can be used to simulate the behavior of that system under different conditions. By using this technology, companies can gain insights into the performance of their products, optimize their operations, and reduce maintenance costs.

What is the Internet of Things (IoT)?

The Internet of Things (IoT) is a network of physical devices, vehicles, home appliances, and other items that are embedded with sensors, software, and other technologies that enable them to connect and exchange data with other devices and systems over the Internet.

IoT devices can collect data from their environment, such as temperature, humidity, and pressure, and transmit that data to other devices or systems for analysis. By using IoT devices, companies can monitor their products and processes in real-time and gain insights into how they are performing.

The Impact of Integrating Cognitive Digital Twin Technology With IoT?

Cognitive digital twin technology can be integrated with IoT by using data from IoT devices to create a digital twin model of a physical system. IoT devices can provide data about the performance of a product or process, which can be used to create a digital twin model.

The digital twin model can then be used to simulate the behavior of the physical system under different conditions and to predict how the system will behave in the future. By using IoT data to create a digital twin model, companies can gain insights into the performance of their products and processes, and they can optimize their operations to reduce costs and improve efficiency.

There are several benefits to integrating cognitive digital twin technology with IoT, including:

  1. Predictive Maintenance: By using a cognitive digital twin model, companies can predict when maintenance is required on their products or processes, reducing downtime and maintenance costs.
  2. Improved Efficiency: By monitoring the performance of their products and processes in real-time, companies can optimize their operations to improve efficiency and reduce costs.
  3. Reduced Waste: With CDT, companies can reduce waste by identifying areas where resources are being wasted.
  4. Enhanced Product Design: By using a cognitive digital twin model, companies can simulate the behavior of their products under different conditions and make design changes in the earlier stages of R&D to improve performance, reduce costs, and cut time from POC to market.
  5. Improved Customer Experience: By monitoring the performance of their products in real-time, companies can improve the customer experience by identifying and addressing issues before they become major problems.

How the Market is Already Benefiting from Digital Twin and IoT Technologies

Many industries are already benefiting from the kinds of integration between CDT and IoT technologies. Chief among these is the transportation industry.

Cognitive digital twin technologies coupled with IoT are already proving invaluable for predictive maintenance of high-value military vehicles, airplanes, ships, and even passenger cars. For example, digital twin solutions like those developed by CarTwin extend the lifespan of cars and other vehicles by monitoring the vehicle’s “health” through its “digital twin.”

Basically, CarTwin can provide diagnostic and predictive models for all vehicle systems for which data is available (either directly or indirectly) onboard the vehicle.

Virtually any part of the vehicle that has sensors or that sensors can be developed for can be “twinned.” These data sets are then enhanced and augmented with design and manufacturing data that is already available by the OEM.

Primarily designed for use in fleets of vehicles, in combination with powerful AI models, CarTwin predicts breakdowns, monitors and improves performance, and measures and records real-time greenhouse gas emissions, which reduces expensive maintenance costs and avoids lost revenue associated with fleet downtime.

You can read much more about how AI and digital twin technology in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. While the book’s primary focus is on healthcare delivery, it also takes a deep dive into digital twin tech, with an entire chapter devoted to CDT, as well as IoT, and the development and launch of CarTwin!

Rohit Mahajan is a Managing Partner at BigRio and the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative AI and machine learning solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

CarTwin has leveraged AI and Digital Twin technologies to create a digital, cloud-based clone of a physical vehicle designed to detect, prevent, predict, and optimize through AI and real-time analytics. 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.

What Is Adaptive AI and What Could it Mean for AI Startups?

The next major evolutionary step in AI and machine learning will be the large-scale implementation of “adaptive AI.” What exactly is “adaptive AI,” and what will the leap to this new technology mean for fledgling AI companies and startups?

The power of AI is its ability to take in and interpret quite large volumes of data and then accurately generate insights and predictions that can lead to smarter decision-making by the humans leveraging the algorithms. As the name implies, adaptive AI systems take that ability to the next level by being able to “adapt” or continuously respond to new as it becomes available and modify its outputs accordingly.

Adaptive AI dynamically incorporates new data from its operating environment to generate more accurate insights on a real-time basis. It is increasingly regarded as artificial intelligence’s next evolutionary stage. By incorporating a more responsive learning methodology, such as agent-based modeling (ABM) and reinforcement learning (RL) techniques, adaptive AI systems are more reactive to the changing world around them and can thus more seamlessly adapt to new environments and circumstances that were not present during the earlier stages of the AI system’s development.

This kind of almost instantaneous adaptability is certain to prove critical over the coming years, during which the likes of the Internet of things (IoT) and autonomous vehicles are expected to expand greatly in popularity. Such applications must continuously consume massive quantities of data to reflect ongoing changes in the external environment in real time.

Well-known IT Analyst Erick Brethenoux observed in October 2022. “Adaptive AI systems aim to continuously retrain models or apply other mechanisms to adapt and learn within runtime and development environments—making them more adaptive and resilient to change.”

Advancements in adaptive AI will also greatly improve AI applications in healthcare and will likely save lives. The ability to consistently analyze data related to thousands, if not millions, of patient symptoms and vital signs can enable adaptive AI systems to optimize the clinical recommendations they produce.

Over the long term, adaptive AI delivers faster, more accurate outcomes, which should mean that more meaningful insights can be gleaned by any enterprise relying on AI for intuitive decision-making.

IT research and consulting group Gartner has predicted that by 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the time and the number of processes it takes to operationalize AI models by at least 25 percent.

All of this speaks volumes to the opportunities for AI startups that focus their R&D efforts on adaptive AI.

How BigRio Helps Bring Advanced AI Solutions to the Marketplace

Adaptive AI, indeed, will be one of the next big leaps forward in artificial intelligence and machine learning. At BigRio, we are at the leading edge of helping such advancements in AI get to market.

BigRio prides itself on being a facilitator and incubator for these kinds of revolutionary breakthroughs in AI.

In fact, 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 out 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 expertise to not only weed out the companies that are not ready for the market, 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 AI investment community.

You can read much more about how AI is redefining the world in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. While the book’s primary focus is on healthcare delivery, it also takes a deep dive into AI in general, with specific chapters on advances such as adaptive AI.

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.

Well Known UK Biotech Company Leverages Open AI Technology for Drug Discovery

UK Biotech innovator e-Therapeutics says it intends to integrate Open AI’s GPT technology into its efforts to develop novel RNAi medicines.

The company has long been at the forefront of computational drug discovery; now, chief executive Ali Mortazavi has signaled e-Therapeutics’ intent to use Open AI’s GPT large language model (LLM) to further automate the quest to find new drug targets, notably in the area of gene silencing.

According to a company press release, specifically, he wants to transform and leverage its current technology HepNet using artificial intelligence.

“By placing LLMs at the core of our computation and harnessing GPT-4’s capabilities, we can now create specialized LLM ‘agents’ which will transform HepNet into a dynamic knowledge resource,” Mortazavi said in the release.

“GPT-4 and LLM integration will provide a unifying framework from which to drive every aspect of our pipeline and position e-Therapeutics as a global leader in hepatocyte biology and related diseases.

“Our long-term vision is to fully automate the preclinical drug discovery process, using GPT-4 and LLMs to access real-time information and interface with external applications, ultimately accelerating the development of life-saving treatments.

In the same press release, e-Therapeutics said it had a busy year, having made significant strides in its RNAi strategy, developing an expanding in-house pipeline of early candidates using the HepNet computational platform. The company said it is actively addressing high-need medical areas, with a focus on cardiometabolic diseases.

Citadel and AI for Drug Discovery

Similar to the way that e-Therapeutics is using Open AI’s GPT LLM to better determine RNAi drug targets, Citadel Discovery was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research.

Our platform provides an alternative framework for early drug discovery by leveraging our access to DNA-Encoded Libraries (DELs) to rapidly and cost-effectively generate readouts on tens of millions of small molecules that are used to train custom, project-specific AI models.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

You can read much more about how AI is redefining drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint, with a particular emphasis on drug discovery and Pharma research.

Rohit Mahajan is the President and Co-Founder of Citadel Discovery. He has particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

Citadel Discovery is dedicated to leveraging AI and MI for the purpose of democratizing access to the data and technology that will drive the future of biological exploration, drug discovery, and health technologies. 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 University Of Southern California Launches $1 Billion-plus Initiative to Advance Quantum Computing

The University of Southern California (USC) has announced that it has launched the single largest comprehensive academic initiative in the university’s history — Frontiers of Computing. The initiative integrates computing throughout education and research to enhance digital literacy for all students as the university aims to hold its lead as a top provider of tech professionals.

USC President Carol L. Folt says that quantum computing and AI will be a big part of the $1 billion-plus initiative. In addition to a focus on AI and machine learning, Folt said that “data science, augmented and virtual reality, robotics, gaming, and blockchain” will all be part of the initiative.

“I want every student who comes through our programs, whether they are in science, business, the humanities, or the arts, to have a solid grounding in technology and the ethics of the work that they do,” Folt said. “We will integrate digital literacy across disciplines to create responsible leaders for the workforce of the future.”

Seeded with a $260 million gift from the Lord Foundation of California, USC Frontiers of Computing encompasses a multipronged effort to push the boundaries of computing into a new era.

USC leaders began developing Frontiers of Computing three years ago, before the recent rise of artificial intelligence and generative AI.

USC already is the leading provider of tech talent for the nation. More than 1,300 students per year graduate with bachelor’s, master’s, and PhDs in computer science.

“We all know the world is changing very fast right now,” Folt said. “We need to take that momentum of change — and couple it with USC’s history of innovation — to create what has never been done before. And we’re going to do it.”

How Big Rio Can Help

Much like the USC initiative, one of BigRio’s ultimate goals is to help foster the next generation of IT innovators with our focus on the support of startups in AI and quantum computing.

Quantum computing is still very much an emerging technology with large-scale and practical applications still a way off. However, the technology is steadily graduating from the lab and heading for the marketplace. In 2019, Google announced that it had achieved “quantum supremacy,” IBM has committed to doubling the power of its quantum computers every year, and numerous other companies and academic institutions are investing billions toward making quantum computing a commercial reality.

Quantum computing will take artificial intelligence and machine learning to the next level. For example, quantum computing’s ability to process and manipulate vast amounts of data simultaneously can enhance data processing capabilities in AI systems. This is particularly beneficial for handling big data applications, where quantum computers can provide faster data analysis, more efficient data clustering, and improved data compression techniques.

The marriage between the two is an area to pay very close attention to for startups as well as for where Big Tech will be going over the next five to ten years, and wherever this road can take us, BigRio will be there to help get startups and society as a whole to its ultimate destination.

You can read much more about how quantum computing will redefine AI and machine learning in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI is being used to improve healthcare and society as a whole.

Rohit Mahajan is a Managing Partner with BigRio. He has 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 US Government Calls for Updates to Its National AI Policies

Against the backdrop of growing concerns about the skyrocketing increase in the use of generative AI tools such as ChatGPT, the US government is updating its national AI strategy and is asking the IT industry and the public for input on amending national AI policies.

Policymakers worldwide are hastening to craft strategies and enforce regulations that safeguard public safety while asserting national leadership in this transformative field. However, while many countries are developing policies to regulate AI, the US is a clear leader in AI, hosting most of the largest generative AI companies, and any policy document drawn up by the US is likely to have a substantial impact on the field.

On May 23rd, 2023, the White House released an update outlining the Biden-Harris administration’s latest initiatives in AI policy under the banner “Biden-⁠Harris Administration Takes New Steps to Advance Responsible Artificial Intelligence Research, Development, and Deployment.”

A noteworthy addition to the updated National AI R&D Strategic Plan is an emphasis on a “principled and coordinated approach to international collaboration in AI research.” This shift aligns with President Biden’s broader international diplomacy strategy, representing a push to engage global discussion on data privacy, safety, and AI biases.

While there seems to be renewed emphasis on the “responsible and ethical” deployment of AI, overall, the strategies have not changed much from 2016 and 2019, with the latest 2023 update continuing to uphold the previously outlined strategies:

  • Creating effective methods for human-AI collaboration.
  • Addressing the ethical, legal, and societal implications of AI.
  • Ensuring AI systems’ safety and security.
  • Developing shared public datasets and AI training and testing environments.
  • Establishing standards and benchmarks for AI system evaluation.
  • Understanding the national AI R&D workforce needs.
  • Expanding public-private partnerships to speed up AI advances.

The Biden-Harris administration aims to understand AI models, robotics, and hardware’s potential capabilities and constraints in areas like climate change, agriculture, energy, and healthcare. The plan encourages the development of general-purpose systems capable of functioning in real and simulated environments. On a positive note, the plan mentioned the word “healthcare” fifteen times and had several references to the use of AI in drug discovery and medicine.

The updated National AI R&D Strategic Plan, alongside these additional initiatives, signifies the Biden-Harris Administration’s commitment to responsible AI development. The emphasis on international cooperation, the refinement of existing strategies, and the active solicitation of public input illustrate a balanced, future-oriented approach to AI.

How BigRio Helps Bring Advanced AI Solutions to Improve All Industries

As a company dedicated to facilitating advancement in AI across many industries, and particularly in healthcare, BigRio supports the responsible use of AI and hopes that we can use our expertise in bringing AI responsibly to market to offer some input on the revised national AI strategy.

BigRio prides itself on being a facilitator and incubator for such advances in leveraging AI to improve the digital world.

In fact, 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 out 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 expertise to not only weed out the companies that are not ready for the market, 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 AI investment community.

You can read much more about how AI is redefining the Internet of Things in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. While the book’s primary focus is on healthcare delivery, it also takes a deep dive into AI in general, with specific chapters on IoT and NLP technologies.

Rohit Mahajan is a Managing Partner with BigRio. He has 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.

Startup Reveals Unprecedented Quantum Computer Chip!

Quantum computer startup SEEQC has just unveiled a quantum digital chip that can operate at super-cold temperatures.

The New York-based startup said in a recent press release that it has developed a digital chip that can operate at temperatures colder than outer space so it can be used with quantum processors that are often in cryogenic chambers.

Quantum computers, which leverage the properties of quantum physics, can complete some calculations millions of times faster than conventional computers. This makes them essential to the ongoing evolution of artificial intelligence.

One challenge is that quantum processors, unlike conventional computer chips, often need to be stored at very cold temperatures near zero Kelvin, or -273.15 Celsius. It has been very hard to find chips that can operate in that kind of environment and process the qubits that need to be in close proximity to the main processors in order for the “quantum entanglements” upon which quantum computing relies to work.

Before the announcement by SEEQC, in most quantum applications, hard wires connect the quantum processor in the freezing chamber to classical computers nearby but at room temperature, but the temperature change can slow the speed and cause other issues.

SEEQC seems to have solved that issue by developing a chip that can operate in the super-cold environment. The first such chip, which it unveiled recently, resides directly under the quantum processor and controls the qubits and reads out the results.

At least two other chips still under development will be in a slightly warmer part of the cryogenic chamber. These could further process the information needed for quantum computing.

The technology could make it easier to build more powerful quantum computers as each cryogenic chamber would be able to support a larger number of qubits, said John Levy, co-founder, and CEO of SEEQC. Today’s superconducting quantum computers have hundreds of qubits, but some estimate thousands or even a million could be needed to create a quantum computer to run next-gen AI algorithms.

The SEEQC digital chips are made at SEEQC’s fabrication facility in Elmsford using silicon wafers but do not use transistors, Levy said.

SEEQC was founded in 2018 and has raised a total of $30 million from investors, including Merck’s M Ventures and LG Tech Ventures.

How is This Discovery Relevant to AI?

Quantum computing will take AI and machine learning to the next level. The marriage between the two is an area to pay very close attention to for startups such as SEEQC as well as for where Big Tech will be going over the next five to ten years.

Consider this. We are at the limits of the data processing power of traditional computers, and the data just keeps growing. It has been estimated that we produce 2.5 exabytes (one exabyte = 1 billion GB ) of data every day. That’s equivalent to 250,000 Libraries of Congress or the content of five million laptops!

In order to handle this ever-increasing volume of data, there’s a race from the biggest leaders in the industry to be the first to launch a practical quantum computer. Only a quantum computer will be powerful enough to process all of this Big Data and be able to solve increasingly complex problems in order for AI to reach its full potential.

How Big Rio Can Help

Quantum computing is still very much an emerging technology with large-scale and practical applications still a ways off. However, the technology is steadily graduating from the lab and heading for the marketplace. In 2019, Google announced that it had achieved “quantum supremacy,” IBM has committed to doubling the power of its quantum computers every year, and numerous other companies and academic institutions are investing billions toward making quantum computing a commercial reality.

Quantum computing algorithms will allow us to enhance what’s already possible with machine learning and AI. BigRio will be there to help get startups and society as a whole to AI’s ultimate destination.

You can read much more about how quantum computing will redefine AI and machine learning in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI is being used to improve healthcare and society as a whole.

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.