Generative AI: What Is It, Tools, Models, Applications and Use Cases

Six AI Applications To Transform Your Clinical Operations

Artificial intelligence will radically transform the healthcare industry in the coming years. As AI and machine learning capabilities advance, new emerging applications promise to enhance clinical outcomes, expand access to care, and improve the patient experience.

The cautious integration of these sophisticated AI applications promises to enhance healthcare efficiency, accuracy, and personalization worldwide. Automating routine tasks with intelligent systems allows human expertise and compassion to shine in direct patient care activities. Continued development could yield far-reaching effects like earlier disease detection, reduced error rates, optimized resource allocation, and more cost-effective solutions.

While challenges remain around transparency, data access, and potential human over-reliance on technology, steady progress in AI validation lays the foundation for new standards of evidence-based medicine. Blending clinical wisdom with machine insights will likely define healthcare's future and establish new frontiers of innovation for improving lives.

Below, we explore six different ways artificial intelligence could transform clinical operations.

Rewriting Medical Language

Large language models allow us to change the vocabulary in discharge instructions to reflect the vocabulary of a young patient, with some companies transforming discharge instructions into a coloring book. At the other end of the spectrum, the models can translate legal and medical language connected to medical lawsuits and legal battles. The lawyer user can upload the legal case, and the doctor can do the same with any medical information. The LLM is used for the medical case, and all the data lives at the lawyer's server or hospital.

Virtual Nursing Assistants

Virtual assistants utilizing natural language processing are helping to optimize nursing workflows. Chatbots and voice assistants can perform basic triage, review patient records, answer common questions, and schedule appointments. This allows human nurses, nurse practitioners, and physician assistants to focus their expertise on more complex care needs. Pioneers in this area include the Mayo Clinic, which piloted a chatbot that efficiently screened over 25,000 patients for COVID-19 symptoms. As language models advance, virtual nurses may one day take on expanded assessment and guidance roles, especially for common chronic conditions managed in primary care. This could help address nursing shortages and expand access in underserved communities.

Medical Imaging Analysis

AI has proven exceptionally skilled at analyzing complex medical images like X-rays, CTs, MRIs, mammograms, and skin lesion photos. Companies have achieved human-level or even super-human accuracy in detecting anomalies, rare diseases, and cancers. This unlocks radiologists' time to focus on the most difficult-to-diagnose cases while getting second opinions faster. For general practitioners and urgent care facilities, automated image analysis tools integrated into electronic health records could one day be a frontline diagnostic aid. The FDA has approved nearly 400 AI imaging algorithms, highlighting the technology's maturity and ability to manage the enormous data from over 3.6 billion radiology exams annually. This enhances the detection of treatable conditions and contributes to improved patient outcomes.

Virtual Clinical Assistants

AI assistants are being developed to augment clinicians during patient visits. These tools can provide real-time diagnostic and treatment suggestions by listening in on exams and referencing up-to-date medical evidence. Others summarize records and prompt providers to address important preventative care gaps like screenings or lifestyle modifications. While access to large, high-quality training datasets remains challenging, assistants promise to reduce cognitive loads on clinicians and aid clinical decision-making. As models are refined, they may help improve guideline compliance and deliver evidence-based best practices.

Predictive Analytics and Outreach

Machine learning offers healthcare organizations an unparalleled perspective on patient risk factors and population trends. These advanced models analyze vast amounts of data from electronic health records (EHRs), claims, and other sources, identifying individuals at high risk for emergent or costly conditions early on. This analytical capability allows for a proactive approach to healthcare, shifting from reactive to predictive care delivery. These insights enable targeted outreach programs, connecting at-risk patients with primary care, counseling, chronic disease management, and other supportive services. This not only improves patient outcomes but also reduces healthcare costs over time. The models' ability to personalize care plans and predict patient needs further enhances the efficacy of these outreach efforts. By leveraging the predictive power of these AI models, healthcare organizations can more effectively anticipate and meet the needs of their populations, marking a paradigm shift towards a more proactive, efficient, and personalized healthcare system.

Personalized Treatment Matching

By leveraging real-world outcomes data from millions of patient profiles, startups are developing tools to recommend the treatments and care pathways most likely to benefit each unique individual. Rather than one-size-fits-all guidelines, these algorithms incorporate a patient's values, preferences, lifestyle, genetics, and other factors. Still an emerging area, personalized matching holds promise for complex conditions with many interdependent treatment variables. It gives providers another layer of evidence to consider as they partner with patients on shared medical decisions.

Dr. Harvey Castro is a keynote speaker, GPT advisor, emergency physician, AI in Health expert, former health system CEO, author, and CEO of Medical Intelligence Ops, which is pioneering the use of a large language model in healthcare.

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The Future Of Work: How Artificial Intelligence Training Is Shaping Job Markets

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The advent of Artificial Intelligence (AI) has ushered in a new era in the job market, transforming the way we work and reshaping traditional employment landscapes. As AI technology continues to advance, the demand for skilled professionals who can navigate this digital frontier is skyrocketing. In this exploration of the future of work, we delve into the profound impact of Artificial Intelligence training on job markets worldwide.

The Rise of AI-Powered Jobs:

The integration of AI into various industries has given rise to a new category of jobs that didn't exist a decade ago. Roles such as AI trainers, data scientists, and machine learning engineers are becoming increasingly crucial. As businesses leverage AI to enhance efficiency and decision-making, the demand for professionals with AI training is skyrocketing.

Automation and Job Displacement:

While AI creates new opportunities, it also brings concerns about job displacement due to automation. Routine tasks that were once performed by humans are now being handled by AI systems, leading to a reevaluation of job roles and skill requirements.

The Imperative of Artificial Intelligence Training: 1. Skill Gap Challenges:

As industries adopt AI technologies, a significant challenge emerges—the widening gap between the skills demanded by employers and the skills possessed by the workforce. Artificial Intelligence training becomes imperative to bridge this gap, enabling individuals to stay relevant in an evolving job market.

2. Reskilling and Upskilling Initiatives:

Governments, educational institutions, and businesses are recognizing the need for reskilling and upskilling initiatives to prepare the workforce for the AI-dominated future. These programs aim to equip individuals with the necessary skills to thrive in roles that require expertise in AI technologies.

AI Training: Catalyst for Innovation and Efficiency: 1. Streamlining Business Operations:

Artificial Intelligence training is not just about preparing individuals for specific job roles; it's a catalyst for innovation and efficiency across industries. Businesses are leveraging AI to streamline operations, automate mundane tasks, and enhance overall productivity.

2. AI as a Decision-Making Partner:

Incorporating AI into decision-making processes is becoming the norm. AI systems analyze vast amounts of data at speeds unattainable by humans, providing valuable insights that inform strategic decisions. This collaboration between humans and AI marks a significant shift in how work is approached.

Navigating the AI-Driven Job Market: 1. Emerging Job Roles in AI:

Understanding the evolving job market involves recognizing the emergence of new and exciting roles. AI ethicists, AI trainers, and AI solution architects are among the positions gaining prominence, reflecting the diverse applications of AI in different sectors.

2. Importance of Soft Skills:

While technical proficiency in AI is essential, the importance of soft skills cannot be overstated. Collaboration, communication, and adaptability are becoming increasingly valuable as professionals work alongside AI systems and diverse teams.

Challenges and Ethical Considerations: 1. Ethical Use of AI in the Workplace:

As AI becomes deeply integrated into the workplace, ethical considerations arise. Ensuring that AI is used ethically and responsibly involves addressing issues like bias in algorithms, privacy concerns, and the potential misuse of AI-powered tools.

2. Job Displacement Concerns:

The fear of job displacement due to automation is a legitimate concern. However, many experts argue that AI will create more jobs than it eliminates, albeit with different skill requirements. Preparing the workforce for these changes through comprehensive AI training is crucial for mitigating displacement challenges.

Global Perspectives on AI Training: 1. Varied Approaches to AI Education:

Around the globe, countries are adopting different approaches to AI education. Some nations are integrating AI training into their school curricula, while others are focusing on workforce development programs. The goal is consistent – to equip individuals with the skills needed to thrive in an AI-driven future.

2. Collaborative Efforts for Global Competence:

Recognizing the global nature of the AI revolution, collaborative efforts between countries, industries, and educational institutions are essential. Sharing knowledge and resources can accelerate the development of a globally competent workforce ready to tackle the challenges and opportunities presented by AI.

Preparing for the Future: A Call to Action: 1. Lifelong Learning Mindset:

In the face of rapid technological advancements, adopting a lifelong learning mindset is paramount. Professionals must be willing to continuously update their skills, embracing AI training as a means of staying competitive in the ever-changing job market.

2. Embracing AI as a Tool for Empowerment:

Rather than fearing the rise of AI, individuals and organizations should view it as a tool for empowerment. AI has the potential to augment human capabilities, opening up new avenues for creativity, problem-solving, and innovation.

Conclusion:

The future of work is undeniably intertwined with the advancements in Artificial Intelligence. As we navigate this transformative journey, the role of AI training in shaping job markets becomes increasingly evident. Embracing the opportunities presented by AI, addressing challenges through ethical considerations, and fostering a culture of continuous learning are essential steps toward a future where humans and AI collaborate harmoniously in the workplace. By staying informed, adapting skill sets, and championing ethical AI practices, individuals can position themselves for success in the dynamic landscape of the AI-driven job market.

Will Artificial Intelligence Cause An Emissions Crisis?

With the rise of deep learning artificial intelligence (AI) and large language models (LLMs), the topic of excess energy usage, as well as greenhouse gas (GHG) emissions, has become increasingly significant for some investors. We believe these concerns are valid, however factors including improvements in technology, the use of clean energy sources, the proliferation of hyper-specific AI applications, and AI-generated industry efficiencies, should off-set the industry's growing energy demands. ­­­

AI and Energy consumption

LLM AI models like ChatGPT, Bard, and Llama, are trained using variations of GPUs. These devices are extremely energy intensive, with the industry standard H100 chipset pulling up to 700W per hour – which, assuming 61% utilisation over a year, is more than the average US household.(1) Searches performed on ChatGPT, as well as other generative AI, are also estimated to cost more than 10x as much energy as a normal Google search.(2) In a 2023 study, analysts estimated that ChatGPT was consuming around 564 megawatt hours of electricity per day (at peak popularity), roughly equivalent to 19,000 households.

Furthermore, as AI models have advanced, the amount of data that is required to be processed has also increased exponentially. GPT 1 was trained on a dataset of 11,000 unpublished books. GPT 2, was trained using 1.5 billion parameters. GPT 3.5, which was released early last year, used around 175 billion parameters.(3) Finally, GPT 4, the most advanced iteration, has been trained on an estimated 1.8 trillion parameters.(4) Naturally, an increase in the size of datasets subsequently requires an increase in GPU power and usage – further contributing to emission fears.

Technology advancement

While the above metrics may heighten concerns over the growth of AI and its consequence on energy consumption and emissions, it is key to note that a version of this exact concern has already played out in the datacentre industry.

Over the past decades, increasing internet usage and needed infrastructure invited similar fears of excessive energy consumption by datacentres. Between 2015 and 2022, internet users almost doubled, and global internet traffic increased 8x – requiring significant infrastructure development for global firms to keep up.(5) Despite this, datacentre energy use over the period only increased by roughly 20-70%, implying a relatively muted CAGR that ranges from 2.6-7.8%.(6) This was largely thanks to efficiency improvements in technology, increased usage of cheaper and cleaner energies, and the consolidation of less-efficient, small-scale data warehouses into more efficient, hyperscaler datacentres.

The trajectory of AI's megatrend is not likely to be all-too different from that of the internet. Similar to how datacentres were quickly optimised to account for exponential growth at minimal energy costs, companies will seek to improve efficiency in AI as there is an economic incentive to do so.

Jensen Huang, the CEO of Nvidia, recently stated in an interview that while he believed AI datacentre capacity will likely double within the next 5 years, "you can't assume that (companies) will just buy more computers… you have to also assume computers are going to become much faster".(7) The same can also be said about energy consumption. GPU architecture of the future is unlikely to be defined by 'how much power it can consume', but rather how efficiently it can use that power.

Hyper-Specific AI models

Increasingly efficient hardware is not the end of the story. How AI software is utilised will also play a significant role in preventing an emissions explosion. A recent study by AI app-builder Hugging Face, found that AI models built on smaller datasets can operate just as well as AI models built on much larger datasets when optimised for specific tasks.(8) The study also revealed that these task-focused AI tools had carbon footprints up to 30x less than that of a larger generalised model when performing the specified task.(9)

These results have significant implications for the future implementations of deep-learning AI. It is our view that while there may be market share for one or two truly generalised AI tools for retail audiences in the future, not all companies will employ such tools in their workflow. Task-specific (and thus resource-efficient) AI will likely become the main use case for the majority of institutions.

Using clean energy

It is without doubt that the training and deployment of AI will require an immense amount of energy over the next decade, and with energy comes emissions. But that relationship can be severed with the use of renewable and clean energy sources.

The deployment of AI hardware is occurring mostly in existing datacentre infrastructure, and almost all of the major datacentre operators of the world are committed to clean energy solutions. Microsoft and Google have pledged to reach 100% clean energy usage for their datacentres by 2030.(10) Amazon is aiming to do the same by 2025.(11) These three companies alone account for more than 50% of the world's large-scale datacentre capacity.(12) By coincidence, these three companies are also major proponents of AI software, with Microsoft having acquired a major stake in ChatGPT owner OpenAI, Google developing LLMs such as Bard and PaLM, and Amazon's increasing AI implementation in AWS. On a global scale, America dominates the datacentre industry with 40% of the world's datacentres located in the country, China follows with 8%, then Japan with 6%.(13) In this context, the overall decarbonisation of electricity grids will be key in keeping emissions in check.

It is key to note that datacentres have had a good track record of minimising emissions. As of 2023, roughly 1.5% of global energy consumption could be attributed to datacentres, but they only accounted for 0.6% of global GHG emissions.(14)  The AI industry itself, while nascent, is also unlikely to contribute excessive emissions outside of hardware implementation. The below chart shows the average Bloomberg ESG environment pillar score across multiple investment universes, among which AI does not significantly deviate nor underperform.(15)

AI-Generated efficiencies

Due to AI's ability to effectively analyse large amounts of data that would otherwise be impossible, AI can help generate significant efficiencies across multiple industries which may assist in combating climate change or reducing emissions.

Climate-friendly use cases of AI include analysing weather patterns to reduce water needs in agriculture, creating new materials that avoid resource wastage, increasing efficiency in energy grids, forecasting GHG emissions, monitoring deforestation, and much more. A recent report by McKinsey estimates that AI-driven technologies could reduce CO2 emissions in companies by 10% and reduce energy use by up to 20%.(16) Another study estimates that AI will reduce global CO2 emissions by 20% by 2030, and that these technologies can save 9.7x more emissions than they generate.(17)

These forecasts assume a rapid growth of efficient AI integration across numerous industries. Nonetheless, it paints an optimistic picture of how AI could be beneficial, not detrimental, to the environment.

AI Growth will limit environmental impact

AI hit an inflection point in 2022 when ChatGPT showed the world the technology's true potential. However, it is important to recognise that the industry remains extremely nascent and the technology supporting it today is far from optimal or complete. It is our view that, like with other technologies before it, the incredible costs that loom over AI today will be chased out in the pursuit of efficiency over time. The datacentre industry, which will be impacted most by the proliferation of AI, has strong clean energy commitments, and AI itself has the potential to reduce emissions in many fields. Overall, the proliferation of AI over the next decade should not represent a significant risk to the climate, especially when compared to legacy industries that remain in the process of transformation.

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