Beyond the Cutting-Edge: AI as the New Standard in Healthcare Innovation

Beyond the Cutting-Edge: AI as the New Standard in Healthcare Innovation === <div style="display: flex;justify-content: space-between;"><span>by Andy Tran</span><span>April 22, 2024 | Report</span></div> <br /> <p style="font-size: 1.5em;padding: 1.25em 0;">Exploring the AI-driven technologies and strategies poised to reshape the healthcare landscape.</p> <hr> <br /> State-of-the-art AI applications, capable of understanding complex medical language, will go far beyond their current capabilities to radically transform healthcare. Imagine a world where AI assistants effortlessly handle all aspects of medical documentation, allowing clinicians to be more present with their patients. Or a world where AI chatbots bridge the knowledge gap between doctors and patients, empowering patients to take charge of their own health journey. These are just glimpses of a future where AI is paving the way towards a more robust, accessible, and compassionate healthcare system. > “The AI healthcare market is projected to be worth $187 billion by 2030, with estimates that generative AI (GenAI) will add $150 billion to $260 billion in value to the healthcare industry per year.” Meanwhile, healthcare’s legacy systems - marked by data fragmentation, clinician burnout, and runaway costs - are reaching a breaking point. These challenges, paradoxically, have set the stage for AI to thrive. An immense opportunity now presents itself to innovators and business leaders: the AI healthcare market is projected to be worth $187 billion by 2030 [1], while McKinsey estimates that generative AI (GenAI) will add $150 billion to $260 billion in value to the healthcare industry per year [2]. Our report covers the future of AI-driven healthcare. It is divided into two parts: 1. We pinpoint the areas where AI will have the greatest impact, highlighting essential business opportunities and applications. 2. We explore the state-of-the-art AI technologies powering this change, providing a blueprint for healthcare's tech evolution. # Part I: Applications across the AI Healthcare Spectrum To explore the ever-growing opportunities at the intersection of AI and healthcare, we introduce a conceptual framework called the AI Healthcare Spectrum. Inspired by the [health care value chain](https://www.wiley.com/en-us/The+Health+Care+Value+Chain%3A+Producers%2C+Purchasers%2C+and+Providers-p-9780787960216), this framework focuses on healthcare delivery rather than production and classifies applications by their purpose and impact. The AI Healthcare Spectrum spans six major domains, ranging from high-level, system-centric priorities like public health and research, to low-level, patient-centric opportunities in the clinic and at home. This overview offers a comprehensive look at AI's transformative potential across the entire healthcare landscape.  ](https://docs.monadical.com/uploads/b5bc9572-3097-4dad-b161-96462ef7b820.png) Right now, healthcare’s imminent transformation starts with the reimagining of enterprise-level tasks. These jobs, encompassing the broad spectrum of care beyond direct medical services, are often labour-intensive, repetitive, and burdened by vast amounts of information [3]. This makes them particularly well-suited for disruption by emerging AI technologies, especially GenAI. With the USA spending over $300 billion every year on healthcare administration [4], it's no coincidence that many startup success stories have focused on these problems. Many enterprise healthcare tasks are concentrated around the *Clinical support* and *Healthcare operations* domains on the AI Healthcare Spectrum. ## Patient engagement From guiding pre-consultation inquiries to automating post-care communication, empowering patients to take the helm of their healthcare journey is at the heart of patient engagement. For example, large language models (LLMs) can demystify medical jargon, generating discharge summaries in plain language and answering follow-up questions at any time of day. Healthcare professionals can also take advantage of AI’s conversational ability to hone their communication skills - a critical yet often undervalued aspect of care that transcends textbook learning. There is a particularly exciting opportunity for AI in post-care adherence, a challenge that costs the American healthcare system over $100 billion annually [4]. Automating small but critical jobs like reminders and follow-up appointments can significantly improve patient outcomes while freeing up valuable time for nurses and care managers. For instance, imagine a computer vision-powered tool that not only reminds patients to take their medication, but can verify that they do, too. By streamlining provider-patient interactions and guiding patients through a confusing healthcare system, the entire patient experience stands to become more engaging, integrated, and holistic.  ## Clinical support In the clinic, AI agents will serve as powerful allies for physicians. They excel at analyzing unstructured data, from medical images to genetic sequences to even a patient’s own medical records, empowering clinicians to make more accurate diagnoses and personalized treatment plans. For jobs that are prone to bottlenecks in the healthcare system, like patient triage, AI has the potential to inform and accelerate decision-making. > “AI-powered systems are like tireless virtual assistants, complementing, rather than replacing, the clinician's expertise.” In addition, AI can significantly reduce the time clinicians spend on information management. For example, an AI assistant could transcribe conversations, summarize visits, and then update the appropriate electronic health record for every consultation. Instead of being preoccupied with clipboards and computer monitors, these tools free providers to focus on what matters most: building relationships and providing exceptional care. AI's clinical impact extends beyond the doctor’s office, enabling proactive care through remote patient monitoring and real-time data analysis. By flagging changes in vital signs and providing early alerts, AI agents support timely interventions or discharges, improving health outcomes and patient flow. Whether by streamlining routine tasks or uncovering insights within complex medical data, AI-powered systems are like tireless virtual assistants, complementing, rather than replacing, the clinician's expertise.  <div class="case-study"> #### Case study: Optimizing the use of hospital beds with AI-powered insights Signal 1 develops a digital platform to provide clinicians with real-time patient insights, working closely with hospital staff to integrate their technology into existing clinical workflows. They created CHARTwatch, an AI-based early warning system designed to predict patient risk of clinical deterioration. Deployed at St. Michael's Hospital, Toronto, from 2020 to 2022, the system demonstrated strong model performance by accurately notifying clinicians when patients were at risk of deterioration the majority of the time (AUC 0.76). It also demonstrated adherence to the Good Machine Learning Practices (GMLP), a document providing 10 principles to address deployment of healthcare algorithms jointly endorsed by the governments of Canada, the United Kingdom, and the USA [5] </div> ## Healthcare operations Ensuring that patient care is delivered efficiently and effectively, operational and administrative tasks encompass a wide range of activities, from front-desk operations to back-end administrative work. AI can dramatically cut down the time support staff spend on manual labour by turning data into actionable insights, optimizing workflows, and managing resources more efficiently. The automation of these processes not only elevates productivity, but also improves the allocation of human and financial capital within healthcare organizations. Moreover, AI's role extends to the educational realm, where it serves as an on-demand training tool. By simulating real-world scenarios and distilling the latest medical literature, AI enables continuous learning and skill enhancement for healthcare professionals, ensuring they remain adept at employing cutting-edge knowledge in practical, patient-focused applications.  ## Financing At every turn, before or after a medical encounter, patients and providers both have to grapple with the questions: Who will pay for this? How much? And why? The complexity of healthcare billing creates friction for patients and administrative burdens for providers. Conversational AI agents equipped with powerful knowledge retrieval systems offer solutions to these challenges. These agents help patients navigate their benefits, providing clear and up-to-date answers to their questions about coverage and costs. By automating the tedious processes of claim submissions, denials, and prior authorizations, these systems can also streamline operations for providers and payers, cutting down the time spent on back-and-forth negotiations and enhancing user satisfaction across the board.  ## Research & innovation Medical research has long been propelled by machine learning and data science, and now, generative AI is poised to push the field even further. Besides distilling scientific literature and enhancing research documentation, LLMs are well equipped to manage clinical trial planning. For example, by sifting through electronic health records and understanding research requirements, they can conduct patient-trial matching while also respecting patient privacy [6]. GenAI’s role also extends to unraveling complex biological mechanisms, such as protein folding and genetic sequencing. A landmark study from MIT researchers showcased a diffusion model’s capacity to imagine new protein structures, marking a significant leap forward in our ability to comprehend and manipulate biological systems for medical advancement [7]. By harnessing the power of big data, GenAI will not only accelerate the pace of innovation, but also ensure that research outcomes align more closely with patient needs and safety standards.  ## Public health In the realm of public health, AI's role is rapidly expanding, offering new capabilities in disease surveillance and the analysis of social determinants of health. By drawing on vast data sets and predictive modelling, AI provides public health officials with the tools needed for proactive, evidence-based policy-making and community health interventions. Effective public health policy can reduce the burden on healthcare systems, prevent or control the spread of disease, and ensure healthcare is delivered equitably and fairly to everyone regardless of their background.  # Part II: State-of-the-art technologies pushing the frontier of medicine Innovators in AI have thrived by standing on the shoulders of giants. Each technological breakthrough inspires waves of researchers and creators to develop new specialized models, frameworks, and tooling, which not only unlocks greater possibilities in healthcare but also tests the limits of what can be achieved. > “The biggest winners in this space will find a way to combine the best of both predictive and generative AI.” While the frontier of AI research has been dominated by GenAI, predictive AI (also called “traditional machine learning”) remains a cornerstone technology in healthcare. In contrast to GenAI, which focuses on creating content, predictive AI attempts to forecast the future, playing a critical role in areas like disease detection and cancer screening. The biggest winners in this space will find a way to combine the best of both predictive and generative AI. For instance, imagine a platform that can anticipate patient needs, predict disease progression, and then design uniquely tailored therapies for them. In this section, we explore state-of-the-art AI technologies poised to transform healthcare. Our selection reflects current trends in research publications and venture capital investment, highlighting areas of rapid innovation. This list is non-exhaustive, as the rate of new developments in the field continues to grow at breakneck speed.  ## Knowledge augmentation will be a key step in the AI stack Knowledge augmentation techniques, like retrieval-augmented generation (RAG), address a major limitation of LLMs - their reliance on static or “old” data. While RAG is the dominant architectural approach, other techniques like few-shot learning, model composition, and prompt engineering are also widely used. RAG can incorporate the latest medical research or clinical guidelines into a model’s context window or “thought process”, generating more informed and accurate responses. This ensures that healthcare decisions are informed by the latest evidence, enhancing patient outcomes. Out of the box, foundation models like [GPT-4](https://openai.com/research/gpt-4) demonstrate expert proficiency on the United States Medical Licensing Examination (USMLE), which is a three-step exam used to assess clinical competency in the USA [8]. With advanced prompt engineering techniques, these models can even outperform fine-tuned medical models like Google’s [Med-PaLM 2](https://sites.research.google/med-palm/) [9]. ## Multimodal LLMs are the next generation of innovation The proliferation of multimodal LLMs (MLLMs) marks a significant shift in AI research and development. Galvanized by the unveiling of Google’s Gemini in 2023, these powerful models are trained on a range of modalities including images, gene sequences, audio waveforms, and other types of unlabeled data. MLLMs have been shown to be more robust than unimodal models in areas like patient assessment, risk prediction, and pathology diagnosis [10]. Beyond training on multimodal data, the potential for MLLMs to produce enriched, dynamic content - from annotated radiology reports to interactive educational materials - opens the door towards more engaging and informative patient care [11]. We are only scratching the surface of what’s possible as researchers continue to build different frameworks for integrating various modalities into existing LLM architectures. ## AI agents can tackle healthcare’s hardest problems LLMs excel in basic queries and conversations, but struggle with complex deductive reasoning. AI-powered agents are systems that are capable of acting autonomously to solve complex problems. They do this through a technique called “Chain-of-Thought”, which breaks down large problems into multiple smaller ones. Agents are composed of different modules that enable them to use external tools (e.g web browsers), hold information in memory, and create actionable items - all thanks to the LLM “brain” that is orchestrating all of the above. > “LLM agents show great promise for addressing health care’s hardest problems. Companies that understand how to build and leverage sophisticated agent systems will gain significant tailwinds in this space.” Imagine a triage agent that is capable of handling patient intake, assessing needs and priorities, and then referring patients to the right department, or a learning agent that assists medical students in becoming better surgeons. In fact, that is one of the use cases researchers explored with ChatGPT, where it was shown to be adept at scientific writing, doctor-patient communication, diagnostic imaging, and managing patients’ peri-operative care. [12] Although LLM agents have not been widely used in healthcare yet, they show great promise for addressing some of healthcare’s hardest problems. Companies that understand how to build and leverage sophisticated agent systems will gain significant tailwinds in this space. ## Predictive AI remains a cornerstone of mission-critical medical applications Predictive AI continues to anchor mission-critical medical applications, such as analyzing diagnostic images and identifying disease markers. These applications have traditionally relied on classic algorithms like decision trees and K-means clustering. Recent research has shown that LLMs fine-tuned on specific medical datasets, like Med-Palm 2, are able to achieve human-like accuracy in diagnosing psychiatric disorders including depression, anxiety, and PTSD [13]. This presents a compelling opportunity for LLM-powered talk therapy tools to play a key role in mental health screening and support. ## Addressing ethics and safety Although foundation models hold immense promise for healthcare, they suffer from the biases and prejudices found in the real-world. One study demonstrated that GPT-3 would mirror biases found in its training data, including tendencies toward stereotype consistency, negative information, and social or threat-related content [14]. If disease metrics or patient demographics are not representative of the communities they serve, AI insights may be skewed, further perpetuating population health disparities. Furthermore, integrating LLMs into clinical workflows raises serious concerns around HIPAA compliance, data privacy, and who holds liability when things go wrong. These challenges demand careful attention to ensure AI aligns with the core ethical principles of healthcare. Fortunately, these issues have fuelled innovation in the open-source community and underscored the need for collaboration between technology experts and healthcare professionals. By training models on patient-centred clinical data, specialized medical models are being developed with enhanced accuracy, safety, and inclusivity. Their focus on delivering actionable, evidence-based insights improves healthcare decision-making while prioritizing patient safety, trust, and the integrity of medical advice. <div class="case-study"> #### Case study: A safety-first LLM will become the go-to model for healthcare innovators General Catalyst and Andreessen Horowitz (a16z) led a $50 million seed round for Hippocratic AI in 2023, a company of visionaries building a state-of-the-art, safety-first medical LLM [15]. Not only does their LLM score better than commercial models (like GPT-4) on over 100 medical certifications - ranging from pharmacy and psychiatry to bedside manners and policy - Hippocratic AI is remarkable for being a truly interdisciplinary team composed of physicians, hospital administrators, policy experts, and AI researchers. Their rigorous attention to safety, compliance and privacy earned them one of the largest seed cheques a16z has ever written. </div> The imperative to balance innovation with equity, privacy, and patient welfare presents a compelling challenge - one that invites all stakeholders to collaborate in a shared future where AI is a beacon of excellence and ethics in healthcare. # Conclusion The untapped potential of AI within healthcare represents a multi-billion-dollar opportunity with tangible payoffs in quality, efficiency, and patient outcomes. From patient engagement and clinical support to research and public health, AI is poised to reshape every facet of the healthcare landscape. Healthcare leaders should prioritize early adoption of proven AI applications, focusing initially on automating time-consuming administrative tasks and augmenting clinical decision-making with large language models. However, realizing the full potential of AI in healthcare requires more than just technological advancement. It demands a collaborative effort from all stakeholders – innovators, healthcare professionals, policymakers, and patients themselves. We must work together to address the ethical challenges, ensure equitable access, and prioritize patient safety and privacy. The journey towards AI-powered healthcare is not without its challenges, but the rewards are immeasurable. By harnessing the power of AI, we have the opportunity to create a healthcare system that is more efficient, effective, and compassionate. A system that empowers patients, supports clinicians, and drives innovation. A system that delivers better outcomes for all. --- This report was made possible by the support of Monadical, a leading edge software consultancy. If you are a founder or company working on new ways to utilize AI in healthcare, please reach out to hello@monadical.com. --- Special thanks to Elliot Goldman, Hanna Jodrey, Jordan Steiner, and Gokul Mohanarangan for their contributions to this report. # Glossary State-of-the-art (SOTA): The most advanced and highest performing technology or method currently available in a particular field. Generative AI (GenAI): A subset of artificial intelligence that focuses on creating new content, such as text, images, or music, based on learned patterns from existing data. Foundational model: A large, pre-trained AI model that can be adapted and fine-tuned for various downstream tasks, serving as a foundation for more specialized applications. Large language model (LLM): An AI model trained on vast amounts of text data, capable of understanding and generating human-like language for tasks such as translation, summarization, and question-answering. Computer vision: A field of artificial intelligence that focuses on enabling computers to interpret and understand visual information from the world, such as images and videos. Agent: An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve specific goals or solve complex problems. # References 1. Stewart C. AI in healthcare market size worldwide 2030. Statista. Published September 28, 2023. Accessed February 12, 2024. https://www.statista.com/statistics/1334826/ai-in-healthcare-market-size-worldwide 2. Chui M, Hazan E, Roberts R, et al. The economic potential of generative AI: The next productivity frontier. McKinsey & Company. Published June 14, 2023. Accessed February 12, 2024. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier 3. Rughani J, Wolf D, Pande V, Yoo J. Commercializing AI in healthcare: The jobs to be done. Andreessen Horowitz. 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