AI Agents Raise New Possibilities for GAI and New Concerns

The latest generation of “AI agents” have transformed the way businesses operate and interact with customers; however, they have also raised some new ethical concerns. AI agents may be the next big thing in Generative AI tools; however, they also may present new challenges and concerns for users.

To date, GAI tools like ChatGPT have become remarkably good at being prompted by humans to create text, images, videos, and music, but they’re not all that good at doing things for us. That’s where “AI agents” come in.

A recent MIT newsletter says to think of AI agents as AI models “with a script and a purpose.” The piece went on to describe the two types of AI agents currently being developed and deployed.

The first, called tool-based agents, can be coached using natural human language -instead of any kind of coding – to complete digital tasks for us. Anthropic released one such agent in October, the first from a major AI model-maker. It can translate instructions, i.e., “fill in this form for me,” into actions on someone’s computer, moving the cursor to open a web browser, navigating to find data on relevant pages, and filling in a form using that data. Salesforce has released its own agent, too, and OpenAI reportedly plans to release one in January.

The other type of agent is called a “simulation agent,” and you can think of these as AI models designed to behave like human beings. The first people to work on creating these agents were social science researchers. They wanted to conduct studies that would be expensive, impractical, or unethical to do with real human subjects, so they used AI to simulate subjects instead. This trend particularly picked up with the publication of an oft-cited 2023 paper by Joon Sung Park, a PhD candidate at Stanford, and colleagues called “Generative Agents: Interactive Simulacra of Human Behavior.”

Recently, Park and his team published a new paper on arXiv called “Generative Agent Simulations of 1,000 People.” In this work, researchers had 1,000 people participate in two-hour interviews with an AI. Shortly after, the team was able to create simulation agents that replicated each participant’s values and preferences with stunning accuracy.

There are two really important developments here. First, it’s clear that leading AI companies think it’s no longer good enough to build dazzling generative AI tools; they now have to build agents that can accomplish things for people. Second, it’s getting easier than ever to get such AI agents to mimic the behaviors, attitudes, and personalities of real people. What were once two distinct types of agents—simulation agents and tool-based agents—could soon become one thing: AI models that can not only mimic your personality but go out and act on your behalf.

Dangers and Ethical Concerns

If such tools become cheap and easy to build, it will raise lots of new ethical concerns, but two in particular stand out. The first is that these agents could create even more personal and even more harmful deepfakes. Image generation tools have already made it simple to create videos using a single image of a person, but this crisis will only deepen if it’s easy to replicate someone’s voice, preferences, and personality as well.

The second is the fundamental question of whether we deserve to know whether we’re talking to an agent or a human. If you complete an interview with an AI and submit samples of your voice to create an agent that sounds and responds like you, are your friends or coworkers entitled to know when they’re talking to it and not to you? On the other side, if you ring your cell service provider or doctor’s office and a cheery customer service agent answers the line, are you entitled to know whether you’re talking to an AI?

Implementing AI agents can result in substantial cost savings for businesses. By reducing labor costs and optimizing resource allocation, organizations can achieve greater operational efficiency. AI agents also minimize the risk of human error, which can lead to costly mistakes. Industries such as manufacturing, logistics, and customer support have witnessed significant cost reductions through the integration of AI agents.

However, it is essential to consider the potential disadvantages and ethical challenges associated with their implementation.

How BigRio Helps Bring Advanced and Responsible AI Solutions to Healthcare

Personally, I could not agree more with the concerns about the proliferation of AI agents raised by MIT. This is why, at BigRio, we are dedicated to providing solutions and supporting AI startups that focus on the ethical and responsible use of AI.

Now that we are focusing our efforts company wide on GAI and LLM solutions, we are committed to the practice of building AI solutions that have clear and transparent rules on how they process data and use it to provide your company with the insights you need to be more productive from an ethical, responsible, and legal point of view.

AI startups face numerous challenges when it comes to demonstrating their value proposition, not the least of which is responsible AI (RAI). This is particularly true when it comes to advanced GAI solutions for pharma and healthcare – an area that BigRio focuses on. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs and particular challenges of GAI adoption in the healthcare field.

You can read much more about how AI is redefining business 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, but it also discusses the impact of AI on all operations and businesses.

Key Takeaways from Digital Health Counsel 2024 AI Summit

The Digital Health Counsel 2024 AI Summit recently took place in Seattle, WA. Attendees of the two-day event left with an undeniable realization of the profound impact that Generative AI (GAI) and data-powered innovation are having on healthcare.

Organized by Ogden Murphy Wallace and sponsored by Fenwick, a leading law firm for the tech and life sciences industries, the summit was a fantastic opportunity to connect and exchange ideas with fellow lawyers, in-house professionals, and AI industry leaders leading the way in this exciting space.

Here are a few key takeaways from the event, according to a Fenwick press release.

AI governance is taking center stage. AI governance is becoming an increasingly critical issue across industries, particularly in healthcare, as new legislation, rulemaking, conventions, case law, etc., continue to flesh out this fast-developing space. The EU’s AI Act has entered into force and is gradually entering effect alongside a host of state-specific legislation in the United States, including 17 AI bills recently signed by California Gov. Gavin Newsom covering a gamut of issues, including disclosure, transparency, and digital likenesses. But beyond these new laws, it’s important to keep track of various emerging AI governance frameworks, such as the National Institute of Standards and Technology’s AI Risk Management Framework, which is increasingly being featured in AI-specific legislation.

Don’t fear the regulator. While the FDA has not yet authorized any generative AI-based medical devices or issued formal rules specifically about GAI, the agency is actively assessing the technology and various regulatory strategies. However, digital health startups should not be intimidated; instead, they should employ best practices for engaging with the FDA and maintain open lines of communication to help minimize their risk. Given the unique aspects of generative AI, post-market performance monitoring will likely be an important regulatory tool.

Prioritize empathetic and ethical AI. Accuracy is not enough. AI—particularly in the healthcare context—must operate ethically and from a place of empathy. This starts with mitigating bias from the underlying data used to train the AI or machine-learning algorithm. Not only does that encourage more equitable and empathetic service for end users, but it can also reduce the risk of hallucinations that pose broader technical problems.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

Like the key takeaways from the Seattle Summit, we understand the transformative impact that GAI is having on the healthcare industry. However, we also recognize that these kinds of moves toward greater use of GenAI-enabled technology must be done with care. In particular, the inherent issues of inaccuracy, bias, and hallucinations of commercially available GAI tools can be quite concerning in the healthcare arena.

In fact, a recent report produced by the AMA pointed out that many challenges remain before the medical industry embraces GAI on a major scale. Most of the concerns the AMA identified are problems when healthcare organizations and clinicians turn to “commercial off the shelf” GAI solutions such as those created by big tech companies like Google, Microsoft, or OpenAI.

But what if you could surmount privacy, bias, and other challenges by building a GAI-LLM model from the ground up for your healthcare organization’s unique targets and needs? You can, with BigRio’s Help!

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 direct patient interactions, customer relationship management, EMR integration, and so much more.

We are proud to have recently launched our proprietary LLM-driven tool, Odyssey Accelerator. It is a first-of-its-kind solution that enables businesses of all sizes who want to use GAI to transform the way they search or query enterprise data, design workflows, derive enterprise analytics, report generation, dashboards, and more, to build a proof of concept before scaling it enterprise-wide.

Leading healthcare IT innovator Vivid Health partnered with BigRio to develop an LLM-driven end-to- end solution, that today can assess a patient’s status on any combination of more than 100 chronic conditions across sixteen specialties, generating personalized care plans at scale in under 30 seconds. That’s over 75 million possible care plan combinations in a fraction of the time it typically takes to pull together individualized plans. Its successful development and deployment stand as a testament to GAI’s potential to enhance the quality and efficiency of healthcare delivery.

Complimentary Gen AI Webinar: https://bigr.io/genai-webinar-december-2024/

You can read much more about how AI is redefining healthcare delivery and drug discovery in my 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.

New Study Finds That Generative AI Can Track Cognitive Decline using EMRs!

A new study out of Boston suggests that combining large language models (LLMs) with traditional AI and deep learning models enhances accuracy in identifying early signs of cognitive decline, offering new hope for early diagnosis and effective treatment of dementia. The study recently published in in eBioMedicine evaluated the effectiveness of LLMs in identifying the signs of cognitive decay in electronic health records (EHRs). They also compared the performance of large language models with conventional AI models trained with domain-specific data.

Using the International Classification of Diseases, tenth revision, clinical modification (ICD-10-CM) criteria for Mild Cognitive Impairment (MCI), the researchers analyzed proprietary and open-source LLMs at Boston’s Mass General Brigham. They studied medical notes from four years before a 2019 MCI diagnosis among individuals 50 years and over.

The study dataset looked at nearly 5000 clinical note sections of about 2000 individuals, among whom 53% were female with a mean age of 76 years. Cognitive function keywords filtered the notes to develop study models. The testing dataset without keyword filtering comprised 2000 sections of clinical notes from about 1200 individuals, among whom 53% were female with a mean age of 77 years.

The researchers tested a proprietary GPT-4 LLM tool and an open-source LLM – Llama 2. The team found GPT-4 more accurate and efficient than Llama 2. GPT-4 highlighted dementia therapy options like Aricept and donepezil. It also detected diagnoses like mild neurocognitive disorders, major neurocognitive disorders, and vascular dementia better than previous models. GPT-4 addressed the emotional and psychological consequences of cognitive problems, such as anxiety, often disregarded by other models.

Combining the two, along with a traditionally trained AI model, into an ensemble model dramatically improved performance. The ensemble study model attained 90% precision, 94% recall, and a 92% F1 score, outperforming all individual study models regarding all performance metrics with statistically significant results.

The researchers concluded that LLMs trained using general domains need additional development to improve clinical decision-making. Future studies should combine LLMs with more localized models, using medical information and domain expertise to improve model performance for specific tasks and experimenting with prompting and fine-tuning tactics.

You can read the full study entitled Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes by following this link.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

It’s interesting that the Boston researchers found that the LLM tools in the study worked best for healthcare analysis when they are proprietary, as opposed to “open source” like Lama 2. Furthermore, they found that even the proprietary tool in the study worked even better when it was customized specifically for the task at hand.

That kind of customization is the cornerstone of BigRio’s mission – to create LLM solutions that specifically target your healthcare organization’s unique needs. 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 direct patient interactions, customer relationship management, EMR integration, and so much more.

We are proud to have recently launched our proprietary LLM-driven tool, Odyssey Accelerator. It is a first-of-its-kind solution that enables businesses of all sizes who want to use GAI to transform the way they search or query enterprise data, design workflows, derive enterprise analytics, report generation, dashboards, and more, to build a proof of concept before scaling it enterprise-wide.

Leading healthcare IT innovator Vivid Health partnered with BigRio to develop an LLM-driven end-to- end solution, that today can assess a patient’s status on any combination of more than 100 chronic conditions across sixteen specialties, generating personalized care plans at scale in under 30 seconds. That’s over 75 million possible care plan combinations in a fraction of the time it typically takes to pull together individualized plans.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my 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.

Two Pioneers in Artificial Intelligence Receive Noble Prizes

Two pioneers of artificial intelligence — John Hopfield and Geoffrey Hinton — won the Nobel Prize in physics for helping create the building blocks of machine learning that is revolutionizing the way we work and live but also creates new threats for humanity.

Hinton, who is known as the godfather of artificial intelligence, is a citizen of Canada and Britain who works at the University of Toronto, and Hopfield is an American who is a professor emeritus at Princeton University.

“These two gentlemen were really the pioneers,” said Nobel physics committee member Mark Pearce.

The artificial neural networks — interconnected computer nodes inspired by neurons in the human brain — the researchers pioneered are used throughout science and medicine and “have also become part of our daily lives,” said Ellen Moons of the Nobel committee at the Royal Swedish Academy of Sciences.

Hopfield, 91, created an associative memory that can store and reconstruct images and other types of patterns in data.

“When you get systems that are rich enough in complexity and size, they can have properties which you can’t possibly intuit from the elementary particles you put in there,” he said in a press conference convened by Princeton. “You have to say that system contains some new physics.”

He echoed Hinton’s concerns, saying there was something unnerving about the unknown potential and limits of AI.

“One is accustomed to having technologies which are not singularly only good or only bad, but have capabilities in both directions,” he said.

The Royal Swedish Academy of Sciences said it awarded the prize to the two men because they used “tools from physics to develop methods that are the foundation of today’s powerful machine learning” that is “revolutionizing science, engineering and daily life.”

The award comes with a prize sum of 11 million Swedish crowns ($1.1 million), which is shared by the two winners.

British-born Hinton, 76, now professor emeritus at the University of Toronto, invented a method that can autonomously find properties in data and carry out tasks such as identifying specific elements in pictures, the academy said.

Asked about the concerns surrounding machine learning and other forms of artificial intelligence, Ellen Moons, chair of the Nobel Committee for Physics, said: “While machine learning has enormous benefits, its rapid development has also raised concerns about our future.

“Collectively, humans carry the responsibility for using this new technology in a safe and ethical way for the greatest benefit of humankind.”

The prizes have been awarded with a few interruptions since 1901. Outside the sometimes controversial choices for peace and literature, physics often makes the biggest splash among the prizes, with the list of past winners featuring scientific superstars such as Albert Einstein, Niels Bohr, and Enrico Fermi.

How BigRio Promotes Innovation in AI

We share the vision of these two AI pioneers and agree with the Noble Prize Committee’s recognition of the transformative impact of AI and now Generative AI (GAI)

To this end, we have launched an AI Studio specifically for US-based startups with GAI centricity. Our mission is to help AI startups scale and gear up to stay one step ahead of the pack and emerge as winners in their respective domains.

AI Startups face numerous challenges when it comes to demonstrating their value proposition, particularly when it comes to advanced AI solutions for pharma and healthcare. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs, which leads to a faster, more accurate output.

We provide:

  • Access to a top-level talent pool, including business executives, developers, data scientists, and data engineers.
  • Assistance in the development and testing of the MVP, Prototypes, and POCs.
  • Professional services for implementation and support of Pilot projects
  • Sales and Marketing support and potential client introductions.
  • Access to private capital sources.

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.

We believe that Generative AI will be most powerful when it is used to enhance, not substitute, human knowledge and creativity. For this reason, Damo and BigRio’s GenAI focus is on working with industry leaders and innovators to create custom tools that augment human intellect, allowing people to know more, do more, and create more than ever before. Customized Onsite, In-Person Healthcare-focused Learning & Ideation Workshops to Accelerate Your GenAI Journey: https://bigr.io/genai-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.

Pfizer Launches GAI-Driven App to Improve Access to Healthcare

Pfizer has launched a GAI-driven Direct-to-Consumer platform that it hopes will usher in a new era of healthcare accessibility.

Pharmaceutical giant Pfizer has launched an AI-driven “direct-to-consumer” healthcare platform to simplify access to healthcare information and services.

Nearly two in every three Americans (65%) say coordinating and managing healthcare is overwhelming and time-consuming, Pfizer said in a news release, citing a poll by the American Academy of Physician Associates. According to the same poll, 73% of them feel the healthcare system is not meeting their needs. Direct-to-consumer (DTC) healthcare models are designed to make healthcare more accessible, affordable, and convenient by reducing barriers to access, empowering patients, and simplifying the healthcare journey.

Such DTC solutions are only made possible through the use of Generative AI (GAI) and Large Language Model (LLM) technologies.

By leveraging GAI technology, the DTC healthcare model can play a critical role in addressing the growing doctor shortage in the U.S. — one projected to reach a deficit of 86,000 physicians by 2036.

While a drug company like Pfizer may not be the first name that comes to mind in providing a DTC healthcare solution, James Allen, vice president U.S. Channel Management and Partnerships, Primary Care at Pfizer, said that its efforts during the pandemic earned the company significant consumer trust, and this trust became the cornerstone of Pfizer’s strategic pivot to direct-to-consumer healthcare.

“We’ve recognized the responsibility that comes with this trust. It’s not just about COVID — it’s about other therapeutic areas where patients are looking for reliable guidance,” Allen said. “Each patient wants to retain the right to choose their care path. We provide options, whether that’s telehealth, seeing a doctor in person, or getting enough information to return to their regular provider.”

The platform known as “Pfizer For All” key focus is on providing patients with optionality, or multiple paths to care, by giving patients to access healthcare information, the ability to connect with healthcare providers, as well as enabling them to manage treatments more easily.

While it’s only been a few weeks since the launch, as data comes in, Pfizer expects to make adjustments to the platform, learning more about patient needs and how best to serve them. “We don’t yet know what all the feedback will look like, but we’re open to learning from it and evolving the platform accordingly,” said Allen. “We’re watching engagement closely — how patients interact with the site, where they continue their journey, and what needs they express that we aren’t meeting yet.”

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

As the U.S. faces a shortage of primary care physicians and specialists, like Pfizer, we see how improving access to healthcare via innovative use of GAI is becoming more pressing now than ever.

BigRio has long been a facilitator and developer 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 direct patient interactions, customer relationship management, EMR integration, and so much more.

We are proud to have recently launched our proprietary LLM-driven tool, Odyssey Accelerator. It is a first-of-its-kind solution that enables businesses of all sizes who want to use GAI to transform the way they search or query enterprise data, design workflows, derive enterprise analytics, report generation, dashboards, and more, to build a proof of concept before scaling it enterprise-wide.

We believe that Generative AI will be most powerful when it is used to enhance, not substitute, human knowledge and creativity. For this reason, BigRio’s GenAI focus is on working with industry leaders and innovators to create custom tools that augment human intellect, allowing people to know more, do more, and create more than ever before. Customized Onsite, In-Person Healthcare-focused Learning & Ideation Workshops to Accelerate Your GenAI Journey : https://bigr.io/genai-workshops-for-healthcare-providers/

Leading healthcare IT innovator Vivid Health partnered with BigRio to develop an LLM-driven end-to- end solution, that today can assess a patient’s status on any combination of more than 100 chronic conditions across sixteen specialties, generating personalized care plans at scale in under 30 seconds. That’s over 75 million possible care plan combinations in a fraction of the time it typically takes to pull together individualized plans.

Vivid Health’s GAI-driven care management platform is currently the most powerful on the market. Its successful development and deployment stand as a testament to GAI’s potential to enhance the quality and efficiency of healthcare delivery.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my 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.

The Greatest Gift of AI and Gen. AI to Healthcare is Time!

From accelerating medical research to improving diagnostics, AI and, in particular, Generative AI (GAI) has had a transformative impact on healthcare. However, perhaps the greatest gift AI has given the healthcare industry is time.

While it was once thought that AI was going to take the human interaction out of medicine, as it turns out, quite the opposite is true. GAI tools are allowing doctors to spend much more quality time with their patients. GAI can streamline workflows for a better patient experience and work-life balance for providers. GAI has given patients more face-to-face interaction with their providers in the exam room and helps ensure their questions and concerns are heard.

To be sure, AI is dramatically speeding up drug trials and other research, but equally as important, it is giving back time to practitioners while still ensuring that the day-to-day tasks, like scheduling, notetaking, record sharing, and billing, can be efficiently completed. In fact, we could say that instead of making healthcare delivered by cold, emotionless machines as was once feared, AI is putting the heart back into healthcare and putting people first.

According to recent studies, over 80% of patients say the biggest problem with their healthcare experience is poor communication, particularly in the exam room.

Providers admit the problems and have always said it was one of time. They must spend endless hours filling out forms, patient notes, writing prescriptions, etc. All of which takes quality time away from patient interaction. In fact, one of the most pressing topics impacting healthcare conversations today is physician burnout. It is found that providers spend 5 hours a day performing administrative tasks.

But GAI is changing all of that. Thanks to Large Language Models (LLMs), GAI tools are streamlining all of these tedious and time-consuming processes, allowing doctors to focus more on care.

For example, Solutions like PRISMA, the healthcare industry’s first health information search engine, use AI to help formulate care plans based on the most relevant patient information while still providing the option to see greater patient detail when needed.

GAI tools have removed the need for notetaking during an appointment. With ambient listening, providers can record the appointment and review a conversation draft summary. The summary easily integrates into the patient’s progress note and recommends the next steps. This removes the device barrier, freeing the provider to spend more ‘stethoscope time’ with patients, meaning additional time for the physical examination with the additional time-savings and reduced administrative workload.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

We understand the profoundly transformative impact that GAI can have on health providers at the point of care. We are dedicated to giving doctors as well as health administrators back critical time by building GAI-LLM models specific to their organization’s unique targets and needs.

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 direct patient interactions, customer relationship management, EMR integration, and so much more.

We are proud to have recently launched our proprietary LLM-driven tool, Odyssey Accelerator. It is a first-of-its-kind solution that enables businesses of all sizes who want to use GAI to transform the way they search or query enterprise data, design workflows, derive enterprise analytics, report generation, dashboards, and more, to build a proof of concept before scaling it enterprise-wide.

Leading Digital Health innovator, Vivid Health partnered with BigRio to develop an LLM-driven end-to-end solution that today can assess a patient’s status on any combination of more than 100 chronic conditions across sixteen specialties, generating personalized care plans at scale in under 30 seconds. That’s over 75 million possible care plan combinations in a fraction of the time it typically takes to pull together individualized plans.

Vivid Health’s GAI-driven care management platform is currently the most powerful on the market. Its successful development and deployment stand as a testament to GAI’s potential to enhance the quality and efficiency of healthcare delivery.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my 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.

What Is Prompt Engineering and Why is it Important to Your Operations Use of GAI?

Prompt engineering is a fundamental aspect of AI implementation. It involves crafting effective instructions or prompts to guide AI models in generating accurate and relevant outputs. Effective, prompt engineering is essential for your operation to get the most out of GAI.

There is an age-old saying in IT and computer programming, “Garbage in – Garbage out.” It relates to the idea that as powerful as any computer can be, it can only respond at peak performance based on the quality of the queries asked of it.

Which brings us to “prompt engineering.”

Prompt engineering is the practice of carefully crafting instructions, known as “prompts,” to guide a Generative AI (GAI) model towards producing the desired output, essentially acting as the key to effectively communicating your intent and getting the most relevant and accurate results from your GAI system in operations by providing specific context and parameters within the prompt itself; making it crucial for ensuring your GAI functions as intended and delivers valuable insights across various applications within your operations.

What every business using GAI should know about prompt engineering

Controlling the output

By designing precise prompts, you can direct the GAI to generate text, code, images, or other outputs that align with your specific needs, whether it’s summarizing a complex report, creating targeted marketing content, or generating design ideas.

Context is crucial

Prompts should include relevant context and background information to help the GAI understand the task at hand and generate accurate responses.

Iterative process

Effective, prompt engineering often involves trial and error, where you refine your prompts based on the initial outputs to achieve the optimal result.

Impact on operations

·      Efficiency gains: Well-crafted prompts can streamline workflows by automating repetitive tasks and providing quick access to relevant information.

·      Decision-making support: GAI can be used to analyze data and generate insights when prompted with targeted questions, aiding better decision-making.

·      Enhanced customer experience: Chatbots and virtual assistants can provide more accurate and personalized responses with proper prompt design.

Example scenarios where prompt engineering is vital:

· Analyzing customer feedback– Prompting a GAI to identify key themes and sentiments from customer reviews.

· Generating marketing copy– Providing specific guidelines on tone, style, and target audience within the prompt to create effective marketing materials.

· Developing technical documentation– Guiding the GAI to create clear and concise documentation for complex technical systems.

Do I Need a Prompt Engineer?

If your operation is heavily reliant on GAI for any or all of the scenarios listed above, then the answer is an absolute “Yes!”

While prompt engineering is a new and emerging field in AI, there are those who have already become experts in how to develop effective prompts to get the most out of your use of GAI and Large Language Model (LLM) driven tools. By crafting effective prompts, organizations can enhance customer experiences, streamline processes, and make data-driven decisions with greater precision. Hiring or working with a prompt engineer empowers businesses to leverage AI tools and systems effectively, enabling them to stay competitive in a rapidly evolving digital landscape.

How BigRio Helps Bring LLM and Advanced AI Solutions to All Businesses

At BigRio, we recognize the ever-changing landscape of Generative AI that is creating new challenges and new opportunities, such as prompt engineers.

That is why we have launched an AI Studio specifically for US-based Healthcare startups with GAI centricity. Our mission is to help AI startups scale and gear up to stay one step ahead of the pack and emerge as winners in their respective domains.

AI Startups face numerous challenges when it comes to demonstrating their value proposition, particularly when it comes to advanced AI solutions for pharma and healthcare. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs, which leads to a faster, more accurate output.

We provide:

  • Access to a top-level talent pool, including business executives, developers, data scientists, and data engineers.
  • Assistance in the development and testing of the MVP, Prototypes, and PoCs.
  • Professional services for implementation and support of Pilot projects
  • Sales and Marketing support and potential client introductions.
  • Access to private capital sources.

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.

Gen AI Workshops:https://bigr.io/genai-workshops-for-healthcare-providers/

Odyssey Gen AI Accelerator: https://bigr.io/bigrio-odyssey-accelerator/

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.

What Is “Data Literacy” And Why is it Important to Your Business?

In the Age of AI, data is the common language of business. Achieving data literacy in your organization is a key to success and requires a shared mindset, language, and skills.

Data literacy — the ability to read, write, and communicate data in context — is fast becoming a requirement for all employees in forward-looking organizations.

IDC, a global provider of market intelligence, forecasts a ten-fold increase in worldwide data by 2025. Increasingly, data-driven organizations will produce data-literate employees who contribute more to their roles and help businesses sharpen their competitive edge in an aggressive global economy.

In the current environment where AI and Generative AI tools can be key to getting a leg up on the competition, company leaders should prioritize data literacy for employees across departments and at all levels within their organization.

A data literacy program should start with a shared mindset, language, and skills, said Valerie Logan, CEO and founder of consultancy The Data Lodge. “It’s an intentional commitment to upskilling your workforce and culture” and an opportunity to grow and amplify an understanding of artificial intelligence in the organization, she said.

Speaking at the 2024 International Chief Data Officer and Information Quality Symposium, Logan and Veronica Vilski, content and engagement director at The Data Lodge, shared insights on what organizations can do today to help their employees become data literate.

The pair offered six steps to launching a successful data literacy program for any type of organization:

  1. Develop a clear, compelling case for change.
  2. Launch and sustain a practical program foundation with targeted pilots.
  3. Amplify and spotlight success stories.
  4. Connect, support, and inspire communities who might feel isolated.
  5. Leverage and connect data culture work and training resources across the organization.
  6. Deliver lasting data culture benefits.

A data literacy program is also a place to grow and amplify an understanding of artificial intelligence in an organization; Logan said, “If you have had a data literacy program in place and you have not included AI in the scope of it from the start, you did not know what you were solving for, and you missed the mark.”

How BigRio Helps Bring LLM and Advanced AI Solutions to All Markets

Like these two tech leaders at BigRio, we share their belief in the transformative impact that AI is having on all industries and how successful AI implementation goes hand in hand with data literacy.

To this end, we have launched an AI Studio specifically for US-based startups with GAI centricity. Our mission is to help AI startups scale and gear up to stay one step ahead of the pack and emerge as winners in their respective domains.

AI Startups face numerous challenges when it comes to demonstrating their value proposition, particularly when it comes to advanced AI solutions for pharma and healthcare. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs, which leads to a faster, more accurate output.

We provide:

  • Access to a top-level talent pool, including business executives, developers, data scientists, and data engineers.
  • Assistance in the development and testing of the MVP, Prototypes, and POCs.
  • Professional services for implementation and support of Pilot projects
  • Sales and Marketing support and potential client introductions.
  • Access to private capital sources.

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.

And we are delivering two Gen AI Workshops in September, 2024, one at Dallas and another in Boston:

https://bigr.io/genai-workshop-2024-boston/

and

https://bigr.io/genai-workshop-september-2024-dallas/

You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book

https://www.amazon.com/dp/1642255548/ref=tsm_1_fb_lk

It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

What Is the Current Status of GAI Adoption By Healthcare?

McKinsey & Company recently took a survey to get a feel for the state of GAI adoption in healthcare. Their “check-up” yielded some interesting results.

Generative AI (GAI) has come on the scene like a whirlwind. Can you believe that only two years ago, few people even heard of ChatGPT? Businesses in all industries are scrambling to see how ChatGPT and dozens of other GAI tools like it can improve productivity and give them a leg up on their competition.

As we have mentioned many times on these pages, if there was any industry that seemed tailor-made for the benefits that could be delivered by GAI, it is healthcare. But at the same time, due to privacy issues, integration with legacy systems and a host of other concerns, it is also the industry that faces the greatest challenges to large-scale GAI adoption. Such challenges have caused the healthcare industry to lag behind many others when it comes to GAI acceptance and implementation.

With all that in mind, global market analytics firm McKinsey & Company took a survey to get a feel for the state of GAI adoption in healthcare. Like a medical exam, their “check-up” yielded some interesting results.

McKinsey took healthcare’s pulse at two points in time—the fourth quarter of 2023 for baseline and the first quarter of 2024 for trend detection. The firm surveyed 100 representative U.S. healthcare leaders for each.

In a report on the project released last week, McKinsey analysts lay out five sets of results and observations. Here are the top five excerpts.

1. Most healthcare organizations are at least pursuing GAI proofs of concept.

In Q4 2023, 25% of respondents said they had already implemented gen AI. The count grew to 29% as of Q1 2024.

“Despite U.S. healthcare’s general interest in using AI, a substantial portion of respondents are still operating without any plans to pursue gen AI or still maintaining a wait-and-see approach.”

2. Healthcare organizations that are already implementing GAI do so primarily through building partnerships.

McKinsey’s Q1 2023 survey found 59 of 100 orgs partnering with third-party vendors to develop customized solutions. That number dropped to 42 by Q1 2024, but the count of orgs procuring GAI products that require limited customization swelled from 17 to 41.

“Among those who haven’t yet implemented gen AI, 41% say they intend to buy gen AI products. This behavior may be driven by this population’s concerns with risk (57% are not pursuing gen AI because of risk considerations) and technology needs (29%).”

3. Among early GAI implementers, few have quantified the technology’s impact.

However, 58% believe it is producing a positive ROI.

“As with any investment, it’s critical for stakeholders to be able to realize the value that gen AI promises. A measurable positive impact serves as strong reinforcement for continued and expanded use and investment.”

4. Surveyed healthcare leaders believe GAI’s greatest value will come on two fronts.

By name, the two are: “boosted clinical productivity” and “patient engagement.”

“Expectations are also high around gen AI’s potential to improve administrative efficiency and care quality.”

5. The No. 1 challenge for healthcare organizations pursuing GAI is risk.

Not far behind are insufficient capability, data and tech infrastructure, and proof of value.

“This demonstrates healthcare organizations’ limited tech readiness to deploy gen AI solutions and also to validate its capabilities.”

The report’s authors comment that, as GAI deployment progresses, healthcare organizations will likely focus on using the technology to support more “clinically adjacent” applications.

You can read the full report by following this link.

https://www.mckinsey.com/industries/healthcare/our-insights/generative-ai-in-healthcare-adoption-trends-and-whats-next

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

It’s interesting that two out of the top five concerns reported by the healthcare professionals that McKinsey surveyed had to do with trust and lack of customization as stumbling blocks to GAI implementation.

Such concerns were echoed in a recent report produced by the AMA that pointed out the many challenges that remain before the medical industry can embrace GAI on a major scale. Most of the concerns the AMA identified are a problem when healthcare organizations and clinicians turn to “commercial off the shelf” GAI solutions such as those created by big tech companies like Google, Microsoft, or OpenAI.

But what if you could surmount privacy, bias, and other challenges by building a GAI-LLM model for your healthcare organization’s unique targets and needs? You can, with BigRio’s Help!

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, customer relationship management, EMR integration and so much more.

We are proud to have recently launched our proprietary LLM-driven tool, Odyssey Accelerator. It is a first-of-its-kind solution that enables businesses of all sizes who want to use GAI to transform the way they search or query enterprise data, design workflows, derive enterprise analytics, report generation, dashboards, and more, to build a proof of concept before scaling it enterprise-wide.

And, BigRio offers Generative AI Workshops for Accelerating Innovation in Healthcare with Generative AI. These are Customized Onsite, In-Person, Healthcare-focused, Learning & Ideation Workshops designed to Accelerate Your GenAI Journey.

We believe that Generative AI will be most powerful when it is used to enhance, not substitute, human knowledge and creativity.

Learn more at:

https://bigr.io/genai-workshops-for-healthcare-providers/

You can read much more about how AI is redefining healthcare delivery and drug discovery in my 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.

https://www.amazon.com/dp/1642255548/ref=tsm_1_fb_lk

Massive Investments Are Driving AI Adoption in Healthcare

There has been a boon in the acceptance and implementation of AI and, more recently, Generative AI (GAI) in healthcare. While much of AI revolution in healthcare is being driven by a need to streamline operations it is also largely being propelled by billions and billons of dollars being invested in AI strictly for healthcare.

For example, in recent weeks, Spring Health’s $3.3 billion valuation, CytoReason’s $80 million funding round, and the AWS-GE HealthCare collaboration all highlight the growing confidence in AI’s potential to transform mental health services, accelerate drug discovery, and revolutionize patient care delivery.

Let’s take a closer look at these three recent major investments in AI for healthcare.

A $100M Boost in AI Tech for Mental Health

Spring Health, a New York-based mental health platform, recently secured a $100 million Series E funding round, pushing its valuation to a lofty $3.3 billion. The company’s platform uses artificial intelligence to match patients with appropriate care providers and treatment plans.

This latest financial injection, led by Generation Investment Management, underscores the growing appetite for innovative mental health solutions by the investment community and by corporate CEOs. Its “Precision Mental Healthcare” approach aims to reduce the time it takes for patients to find effective treatment, a selling point that has resonated with employers and investors.

The company boasts a client roster that includes tech giant Microsoft, retailer Target, and financial powerhouse J.P. Morgan Chase.

An $80M Boost for AI Drug Discovery

CytoReason has raised $80 million for its AI-powered platform for disease modeling and drug discovery. The startup, founded in 2016, has caught the eye of industry giants, with Nvidia, Pfizer, OurCrowd, and Thermo Fisher Scientific all chipping in to fuel its growth.

CytoReason launched an AI platform that creates computational disease models, giving researchers a powerful tool to predict and develop new therapeutics. By simulating human diseases at the cellular level, the platform supposedly allows scientists to observe how potential treatments interact with the body, potentially fast-tracking drugs to patients.

The fresh injection of millions in capital will be used to expand the application of CytoReason’s computational models and grow its proprietary database of molecular and clinical data. The company is also planning to expand to the U.S., with plans to open an office in Cambridge, Massachusetts, later this year.

AWS and GE HealthCare Partner

Industry powerhouses, such as AWS and GE HealthCare, have announced a collaboration to harness AI to improve patient care. The partnership aims to develop AI models and applications that enhance the standard of care in the healthcare sector.

Currently, nearly a third of all digital information comes from the healthcare sector, yet 97% of this data is inaccessible to physicians due to its unstructured nature and confinement in data silos, according to the companies. AWS and GE HealthCare’s partnership seeks to unlock this data using AI foundation models (FMs) and innovative applications.

GE Healthcare plans to train and deploy clinical FMs on AWS’ machine learning and generative AI technologies. These tools are designed to help healthcare providers improve existing protocols and workflows and develop new approaches to patient care.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

We completely understand the investment community’s excitement about the transformative impact of AI, and particularly, GAI applications like those that will come out of the partnership between GE and AWS.

To this end, we have launched an AI Studio specifically for US-based Healthcare startups with GAI centricity. Our mission is to help AI startups scale and gear up to stay one step ahead of the pack and emerge as winners in their respective domains.

AI Startups face numerous challenges when it comes to demonstrating their value proposition, particularly when it comes to advanced AI solutions for pharma and healthcare. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs, which leads to a faster, more accurate output.

We provide:

  • Access to a top-level talent pool, including business executives, developers, data scientists, and data engineers.
  • Assistance in the development and testing of the MVP, Prototypes, and POCs.
  • Professional services for implementation and support of Pilot projects
  • Sales and Marketing support and potential client introductions.
  • Access to private capital sources.

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.

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.

And, we are offering Gen AI Workshops for Healthcare, on-site, in-person, 4 hours, 30 participants at $5000.00 : https://bigr.io/genai-workshops-for-healthcare-providers/