Category: Generative AI

Welcome to the exciting world of Generative AI, where every day brings forth new technological wonders! As we journey through 2024, we find ourselves witnessing an AI renaissance, where the possibilities are endless.

You’ve probably heard about OpenAI’s ChatGPT and Google’s Gemini, right? These incredible advancements in generative AI have been captivating minds since OpenAI unveiled ChatGPT back in November 2022. It quickly became the talk of the tech world, with “GenAI” dominating discussions throughout 2023.

In early 2023, Microsoft joined the fray, backing OpenAI with a significant investment to propel AI technology forward. Shortly after, in March 2023, Google made its move with Bard, later unveiling an improved version named Gemini in February 2024.

Both ChatGPT and Gemini have sparked immense interest in generative AI. They use data from machine learning models to create text, images, and videos that seem remarkably human-like. OpenAI and Google are constantly refining these large language models (LLMs) behind ChatGPT and Gemini to make them even more advanced.

Now, let’s talk about a recent report from Accenture that shines a light on how these advancements in generative AI, along with other Language Learning Models (LLMs), are set to transform healthcare. Here’s a glimpse of what the report found:

• 98% of healthcare providers and 89% of healthcare payer executives believe that generative AI will revolutionize enterprise intelligence.
• It’s estimated that about 40% of working hours in healthcare could benefit from language-based AI.
• Healthcare organizations are gearing up to leverage generative AI into their roadmap, with more than half planning pilot cases this year.

That is fascinating, isn’t it? Together, let’s look into this innovative research in-depth and discover the amazing possibilities of generative AI and LLMs in the healthcare industry and beyond.

Generative AI for Pharma

For the pharmaceutical industry, the ideal Generative AI transcends the ordinary. For professionals working with data and information, it’s not just a tool; it’s a strategic ally seamlessly integrating into existing workflows while catapulting the capabilities of healthcare professionals to unprecedented heights.

phamax is actively exploring the potential of generative AI within the pharmaceutical industry. Through recent discussions and thorough examination of use cases, we’ve identified numerous opportunities where generative AI can truly transform processes within the pharma sector.

From utilizing existing information sources like clinical trial data and research to generate value, to automating mundane tasks that consume time and efforts for Medical Affairs, and even creating marketing communication materials – that potential applications are vast.

The more we explore these options, the more obvious it is that generative AI is the secret to improved information access and streamlined processes. By leveraging the power of generative AI, phamax aims to drive innovation, enhance decision-making processes, and ultimately, improve outcomes for both patients and stakeholders within the pharmaceutical industry.

Accelerating Content generation with generative AI

The current process of content production is often unnecessarily complex and tedious. Adding to the complexity is the demand to meet compliance, demand for personalization, and the need to meet swift timelines. Content curation in the pharmaceutical industry holds immense potential for saving significant amounts of time and resources in generating content.

Moreover, content curation enables pharmaceutical companies to stay updated with the latest advancements, research findings, and regulatory changes in the industry. By curating relevant content from various sources, companies can maintain a comprehensive knowledge base that informs their decision-making processes and keeps them ahead of the curve in a rapidly evolving landscape.

Overall, content curation offers pharmaceutical companies a strategic advantage by optimizing time and resources, enhancing content quality, and facilitating informed decision-making, ultimately contributing to improved efficiency and effectiveness in the industry.

Querying Your Data Is Convenient with NLP

Generative AI emerges as a game-changer, liberating valuable time for professionals by revolutionizing the way they interact with data.

No longer bound by the complexities of intricate queries, team members can now engage in effortless conversations with their data. The introduction of natural language (NLP) queries transforms the process, allowing individuals to pose questions in plain language, unlocking insights with unprecedented ease. This not only streamlines the workflow but also democratizes data-driven decision-making, making valuable insights accessible to every member of the team.

The Shifting Role of Dashboards Today

In the realm of business intelligence, dashboards have been a steadfast companion for over two decades, offering leaders a visual gateway to invaluable data insights. However, despite their longstanding presence, the adoption of analytics has plateaued at a mere 30%. Shockingly, only 10% of executives believe their organizations have reached a level of analytics maturity, while a staggering 84% of frontline workers express dissatisfaction with their analytics experiences, yearning for more robust insights technology.

Here’s what we’ve come to realize:

• Dashboards, once hailed as the pinnacle of data communication, now find themselves amidst an ever-changing landscape where agility and adaptability reign supreme.
• While still relevant, dashboards are no longer the sole conduit for disseminating vital information across the business.
• Newer analytics features have emerged, complementing dashboards to cater to the evolving needs of modern business users.

This epiphany marks a pivotal moment in our journey:

Imagine:

• Seamlessly navigating complex datasets with ease, empowered to make informed decisions at the blink of an eye.
• Embracing a dynamic, interactive realm where raw data morphs into actionable insights, driving impactful actions across our organization.

Generative AI doesn’t just change reporting; it revolutionizes it, adapting to the dynamic needs of our professionals and catalyzing our journey from insight to impact. It’s more than data exploration; it’s a transformative voyage that reshapes the very essence of how we operate and lead in the ever-evolving landscape of business intelligence.

Centralized Information Access with AI

Generative AI revolutionizes information access for professionals in the pharmaceutical industry, providing a centralized platform where critical data and insights are readily available. By harnessing the power of AI, professionals can effortlessly navigate through vast repositories of information, ensuring swift access to the latest research findings, regulatory updates, and market trends. It’s not just about having data at your fingertips; it’s about empowering informed decision-making and driving strategic initiatives with confidence.

Fostering Collaboration with AI

Generative AI promotes collaboration among professionals in the pharmaceutical industry, breaking down barriers and encouraging cross-functional teamwork. It transcends mere process optimization, creating an atmosphere where diverse perspectives merge to spur innovative solutions. Generative AI serves as the impetus for brainstorming sessions, strategy meetings, and collaborative analyses. It’s not just a tool; it’s the catalyst for nurturing a culture of ongoing improvement among pharmaceutical professionals.

From Burdens to Benefits: Ariya for the Medical Affairs Team

Now, let’s weave the magic together – how Ariya leverages Generative AI to elevate burdens into a carnival of benefits for the Medical Affairs team

Time Optimization

The extensive volume of data and information required for medical content creation and communication demands meticulous processing and analysis efforts. Ensuring accessibility for both internal and external users adds another layer of complexity. However, the substantial commercial and scientific value derived from these internal repositories can significantly improve efficiency and leverage existing investments.

The implementation of AI-powered search capabilities swiftly establishes a comprehensive and easily accessible catalog of medical assets and specific pathologies, streamlining information processing and management. With the assistance of Generative AI, Medical Affairs.

With the assistance of Generative AI, Medical Affairs professionals can expedite data analysis, reducing the time required to review dense scientific documents, technical papers, and data summaries by up to 60%. Leveraging this technology simplifies the document review and analysis process, offering a more effective and time-saving approach compared to traditional manual methods.

Intuitive Decision-Making

Bid farewell to the era of wrestling with complex data queries. Ariya’s natural language interface becomes your partner in a dance with data, extracting insights effortlessly. It’s not just about saving time; it’s about democratizing data-driven decisions, and empowering every team member. Ariya transforms your team from analysts to artists, creating a masterpiece of collaborative healthcare management.

Seamless Integration

Ariya doesn’t disrupt; it enhances. The seamless integration with MS Teams ensures that incorporating Generative AI into your workflow is a smooth, harmonious process. Your existing PowerBI dashboard becomes a living entity, evolving with your needs. It’s not just integration; it’s a harmonious blend of technology with tradition, creating a symphony of enhanced workflow efficiency.

Conclusion

Today, Generative AI has emerged as the ally your Medical Affairs team needs. From optimizing time and making intuitive decisions to seamlessly integrating into existing workflows and fostering collaborative innovation, Generative AI propels your team from burdens to a myriad of benefits. Embrace the transformation; your success story starts now, as you embark on an epic journey with Ariya leading the way toward a future where Medical Affairs not only adapts but thrives in the ever-evolving healthcare landscape.

Imagine having a dedicated ally like Ariya at your side, ready to meet all your needs effortlessly. With our domain-trained AI, you’re not just getting a tool – you’re gaining an innovation partner. And now, with our Trial Launchpad, testing the power of generative AI in Pharma has never been easier. It’s about more than just convenience; it’s about empowering innovators like yourself to explore, discover, and unlock new possibilities. Trust us, with Ariya’s intuitive use cases, you’ll wonder how you ever managed without it.

To explore in-depth details about Ariya and its generative AI use cases, click here!

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Generative AI Has Arrived, But Are We Ready?

When Generative Artificial Intelligence (GAI) arrived almost overnight, it revolutionised how we create content. Everyone can now generate fresh, original multimedia content using GAI algorithms, including text, audio, video, and more.

From the outset, it’s essential to understand how GAI differs from conversational AI systems. Rather than generate content, this technology aims to identify patterns and make predictions from specific pre-defined data.

In this blog, we will take a rounded view of GAI and how it can help us while acknowledging some of its limitations.

With only a cursory news media review, it’s clear that GAI will impact almost every aspect of our lives. However, there are inherent risks and uncertainties, with some (warranted) disquiet expressed by key influencers in the tech field. 

The term “generative artificial intelligence” hit the mainstream in November 2022. After many years of research by software engineers, apps, including ChatGPT appeared seemingly from nowhere. Early adopters immediately saw the fantastic things GAI could do. We now find ourselves at an AI tipping point. Finally, we have AI tools to help businesses generate a vast array of creative content or enable large corporates or governments to develop GAI-enabled products and services.

The potential of GAI is exciting, but at this stage, the casual user doesn’t fully know what these tools are capable of or how they work.

What is Generative AI?

GAI is a technology capable of independently generating original content, including audio, code, graphics, text, simulations, and videos. The technology achieves this by referencing the data on which it has been ‘trained’. For example, the popular OpenAI GPT tool, ChatGPT, is trained on a base large language model called GPT-3 that enables users to create unlimited content based on this data.

As important for this groundbreaking technology are the simple, intuitive interfaces used to request content creation that put the skills of writers, graphic designers, musicians or coders into everyone’s hands.

So, How does Generative AI work?

Central to GAI is the concept of training. This is the stock of data and information a GAI algorithm references and manipulates to create new multimedia content. The data sets of generic mass-market GAIs, like ChatGPT, Bard or MidJourney, can be extensive (GPT-3, for example, was trained on 45 terabytes of text data).

When used in a specific or regulated corporate setting like scientific or legal environments, the data generated must always comply with and follow strict regulatory guidelines. In these circumstances, the AI and its training data undergo a rigorous conditioning process, including contextual understanding, bias reduction, expert knowledge integration, quality control, and the incorporation of user feedback. This ongoing process ensures that all outputs are scrupulously accurate and compliant.

Underpinning all GAIs are Generative Adversarial Networks (GANs), a subset of deep learning technology. GANs are the product of two neural networks: a generator that renders new data and a discriminator that assesses the resulting outcomes. Working together, the generator and discriminator produce the requested content. In addition, the GANS continuously provide each other with feedback that helps to fine-tune the outputs it develops.

The other factor in promoting the originality of generative results comes from the amount and quality of data used to train a GAI. Clearly, in every use case, this exceeds the referencing capabilities of human creators by a considerable margin.

Finally, generic GAIs will have randomisation components, enabling them to generate various outputs from a single input request, giving the impression that they are genuinely ‘creative’.

Benefits of Generative AI

Even at this early stage, the potential of GAI is obvious. The range of uses for this technology is growing by the day. Notwithstanding the (already proven) ability to interpret and generate defined content, developers are investigating how generative AI can rationalise workflows. Examples of how GAI will help us in the future include:

1. Consolidating complex knowledge into a more easily digestible format

2. Automating the response to specialised technical questions

3. Content creation in any given medium

4. Acting as a co-creator for specialist designers or academics to work more effectively and with more significant impact.

The overall economic benefits of GAI are seemingly without limit. For example, GAI’s ability to work with data will eliminate repetitive, time-consuming work such as data entry, financial consolidation or document reviews. In addition, GAI frees up human bandwidth to pursue more strategic, creative and value-added work by eliminating mundane, repetitive activities.

Naturally, this technology may also displace a significant chunk of human economic activity, particularly in the creative sector and areas such as customer service or wholesale data entry.

The Limitations Of Generative AI

For a technology with so much potential, it’s important to remember that it remains an emerging technology. Even at this early stage, issues are developing, and a degree of risk remains, as highlighted in apocalyptic terms by Elon Musk:

“AI has the potential of civilisation destruction.”

Even if we take this dystopian idea with a pinch of salt, we already know mass-market GAI, like ChatGPT, Midjourney, Bard and the like, have inherent issues:

1. Sometimes the outputs generated are plain wrong. Worse, it can be disturbingly discriminative based as it is on the biases expressed online by human creators.

2. Bad actors could use GAI outputs to excuse unethical or illegal conduct, especially when taken at face value.

3. It does not always identify the origin of the content generated.

4. There are growing concerns about privacy, security and copyright contravention.

All these challenges must be addressed if GAI is to become a core part of how we live and work.

Conclusion

Today GAI is a largely unexplored field, and we are only beginning to understand what this technology will offer. Additional applications of this tech will emerge rapidly, with new uses likely to drop with surprising regularity. The AI snowball is only just getting going, but we’ll see GAI plays an increasing role in business, society, and our personal lives.

In parallel, as we learn and experience more of what it can do, society’s attitudes to the advantages and risks GAI poses will shift alongside how we exploit and mitigate its potential. In that regard, we can expect the emergence of new regulatory regimes and legal interventions to help us make GAI a force for good.

Contact our team to schedule a demo or to discuss a free trial of Ariya, phamax’s leading AI digital assistant designed exclusively for the healthcare sector.

Digital assistants and conversational AIs are a common part of our daily lives. Industries like banking, insurance, telecoms, and retail sectors all use AI to interact with clients, not to mention in emerging areas like autonomous vehicles.

Of course, we have all seen movies where artificial intelligence systems develop a sense of self and begin to devastate the planet. The AI will happily serve our needs one moment, and then send red-eyed cyborgs through time tunnels to end existence as we know it the next. Even though we are aware that these dystopian stories are the stuff of blockbuster films, it is also true to say that these dramas are, in some ways, influencing how we perceive what AI might be able to accomplish in the future.

In this blog, we will get to know

· What is meant by “responsible AI”

· The significance of developing responsible AI

· The guiding principles of Responsible AI

· A Responsible AI for the healthcare sector

What Is Responsible AI?

Responsible AI means designing, developing, and deploying AI solutions with the core principles of empowering employees and businesses. In addition, the AI’s design should equitably impact customers and society, allowing companies to engender trust and scale their AI confidently.

Importance of making responsible AI

As the tech environment evolves from text input requests to voice-enabled AI-powered chatbots, how much thought do users give to the ethics that underpin how companies build their digital assistants?

Consider, for example, the findings from Accenture’s 2022 Tech Vision research, which showed that only 35% of consumers worldwide expressed confidence in how businesses implement AI. And roughly 77% of those surveyed believe that organisations should be held fully accountable for any misuse of AI.

Sundar Pichai, the CEO of Google, echoed this idea when he said, “There is no question in my mind that artificial intelligence needs to be regulated. The question is how best to approach this.” The EU is one organisation looking to answer Pichai’s question with its working framework called ‘Ethics Guidelines For Trustworthy AI.’

While the EU consultation is still a work in progress, the message is clear. As AI use accelerates, legal frameworks will compel developers to build their AI products based on ethical policies that belay users’ and society’s understandable concerns.

Guiding principles of Responsible AI

AI and the machine learning models that support it should be comprehensive, explainable, ethical, efficient, and built on well-documented principles.

Comprehensiveness — an AI has clearly defined testing and governance criteria to prevent machine learning from being hacked.

Explainable — ensuring AIs are programmed so we can describe their purpose, rationale and decision-making processes in a way the average end user can understand.

Ethical — AI initiatives have processes that seek out and eliminate bias in machine learning models to avoid undesirable distortions in outputs or intentions.

Efficient — AI can run continually and respond quickly to changes in the operating environment while minimising environmental impacts.

Responsible AI is vital in a Digital Assistant

AI has the potential to revolutionise how we lead our lives. It will increasingly change how we interact with business and make complex decisions on our behalf as the tech becomes increasingly sophisticated.

In short, we must trust that an AI digital assistant works in our best interests using optimised, unbiased data and in a scrupulously controlled data architecture. Key processes we must create are AI digital assistants that are interpretable, fair, safe and respectful of user privacy.

Responsible AI, therefore, is an aspiration aimed at doing precisely these and should include a focus on the following:

Design Criteria

A digital assistant’s functionality should consistently meet user needs. The principles of human-centered design and comprehensive user input in the build stage are crucial to this criterion.

At launch, implementing feedback loops, system metrics, confidence scores, and continuous learning ensure that the assistant continues to deliver against its core purpose.

When we at phamax began with Ariya as a pilot, it was all thanks to the innovators who partnered for trial. We were able to discover how crucial the user journey is. In addition to simplicity, the interface should take care of ease of access.

Compliance Frameworks

The criteria for any digital assistant must account for competing compliance frameworks, especially in complex areas like healthcare, where standards differ between geographies. This will inevitably lead to trade-offs.

With the increasing demand for information in digital space, it becomes inevitable to take care of sensitive information. Especially in channels and webpages that are patient-centric. For example, in some countries, users should be able to get information approved by their national regulatory bodies; therefore, a digital assistant’s design must consider these variations.

The process of content approvals and application logic to ensure the right content delivery may be difficult but is necessary when designing the embedded Ariya version.

Data Responsibility

Data warehouses are varied and businesses prefer their own data management systems. Especially in healthcare sectors where data sensitivity is more, organisations tend to resist any external systems that can breach privacy. Hence, digital assistants should be able to account for these challenges and should be able to connect to any format of client business data sources, including external datasets. A secure architecture is, therefore, an essential prerequisite to data integrity. Furthermore, the source data must be used objectively.

Ariya is designed to leverage existing data sources and can be hosted in client-preferred environments and channels. The recent introduction of Ariya on MS Teams has proved to be a boon as organisations can limit their access to internal as well as access and connect to their own ERP systems

Data Privacy

Ensuring AI digital assistant providers inform users of their rights to privacy in data storage and usage, including in improving performance and user experience. Unfortunately, the potential for a poorly designed digital assistant to contravene these rules are legion, so robust privacy protections is paramount. These rights are outlined in the EU’s General Data Protection Regulation, and failure to comply with it can result in severe financial penalties.

For example, what measures must be taken to protect people’s (employees, patients, and stakeholders like KOL/HCPs) privacy, given that ML models might recall or disclose details of the data they have been exposed to? What steps are needed to ensure users have adequate transparency and control of their data?

Fortunately, by applying several strategies in a precise, principled manner, the likelihood that ML models reveal underlying facts can be reduced.

Controlled Access & Administration

The rigorous control of access to a conversational AI across broad user populations is essential. Sales managers, for example, should be able to view all user data, but a sales rep, for example, cannot view data of other representatives or their territory.

Also, interactions and communications in the digital space have gained popularity amongst KOL and HCPs. It is crucial to distribute region-specific information. This process might include implementing a secure login for accessing patient data, such as DocCheck, that restricts access only to registered healthcare practitioners.

Clear Parameters

Communicating to stakeholders about the digital assistant’s limitations to users. The data models a chatbot uses should broadly reflect the patterns within the data used to train the AI. As a result, tech firms should be able to effectively communicate their data model’s scope and coverage, clarifying both its capabilities and limitations where required This will undoubtedly be a typical “moving feast” as new data enters the systems. Therefore, suitable levels of ongoing review and governance will also ensure a digital assistant performs reliably, maintains its objectivity and meets its core functionality.

A Responsible AI For the Healthcare Sector

As AI technology becomes more prevalent, we’ve learnt that the ethics of responsible AI should always stay front and centre on all things AI. phamax being at the forefront of conversational AI use in the healthcare sector, takes these concerns seriously and has ensured these principles remain a priority as Ariya, our AI-powered digital assistant, is being developed and upgraded.

If you’d like to learn more about our approach to Ariya’s responsible AI or would like more information on phamax’s data solutions, please complete our contact form or write to us at ask@ariya.ai.