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Cranfield, UK, 5th July 2024, Written by Jon Hill –
Over the last 12 months, Signify Research has tracked generative AI (gen AI) product announcements in healthcare to understand the most prevalent use cases and those lacking traction. These announcements have been captured in our product database, part of the Generative AI Market Intelligence Service. In the first edition released in early April, Signify Research captured over 300 applications, whereas in the second edition of the database, Signify Research has now captured 440 products either being developed or already deployed concerning generative AI in healthcare.
Regions
The healthcare-focused gen AI landscape is notably dominated by North America, which is underpinned by several key factors:
- The United States’ outsized role in global healthcare spending – accounting for over half of worldwide expenditures
- The U.S. hosts an unparalleled concentration of health IT companies. This dense ecosystem of firms specialising in healthcare technology further cements the nation’s status as the primary hub for health IT products that harness generative AI capabilities.
- The prevalence of proprietary AI platforms in the U.S., such as AWS Bedrock, Google Vertex AI, and Azure Cloud, offers a significant advantage. These platforms facilitate smoother integration of models from industry leaders like OpenAI, Anthropic, and MedLM, bypassing the technical complexities often associated with open-source alternatives.
Given these factors, it comes as no surprise that North America continues to dominate (in terms of location of active vendors), with the highest number of products released in our Q2 edition. This ongoing trend underscores the region’s sustained leadership in healthcare-focused generative AI product development.
Product Types
Most products are focused on Digital Health and EMR, followed by Pharma and Life Sciences and Medical and Clinical IT. “Big Tech” signifies cross-adaptable applications such as foundation models and LLMops provided by technology companies, not necessarily field-specific.
Big Tech companies have pivoted towards a partnership-centric approach in the healthcare AI space. Rather than developing direct healthcare products, they’re positioning themselves as technology stack providers, collaborating with health tech vendors eager to adopt gen AI solutions. This strategy has resulted in fewer healthcare-specific product announcements from big tech firms themselves. For instance, HCA Healthcare has joined forces with MEDITECH and Google to develop targeted generative AI solutions for healthcare challenges.
However, this landscape is poised for potential shifts. As open-source models gain traction among health tech vendors for their flexibility and customisation potential, big tech’s value proposition may face challenges. The advancing sophistication of AI technology, coupled with increasingly user-friendly integration processes, could erode the perceived benefits of proprietary platforms, however, security remains a major concern. The changing landscape in AI technology is challenging the sustainability of major tech companies’ existing approaches. As AI becomes more accessible, healthcare technology providers are increasingly likely to adopt open-source models. Consequently, it’s becoming crucial for cloud service providers to offer open-source AI options on their platforms. This strategy is essential to maintain the interest and engagement of healthcare technology companies.
The coming years will likely reveal whether big tech can adapt to these changes, perhaps by offering unique value-added services, or if they’ll need to significantly alter their approach to remain competitive in this rapidly evolving sector. Smaller organisations in the past have struggled to leverage open-source AI models due to the significant technical expertise and resources required for deployment. However, with Snowflake vendors are now able to deploy Llama 3 with ease in a no-code-low-code environment. In contrast, proprietary models from large tech firms offer more user-friendly support and infrastructure, albeit at higher cost and less flexibility.
End Users
“Clinicians” are the primary beneficiaries of generative AI, as tech optimises their burdensome workflows. This is followed by “providers”, which refers to the broader healthcare organisations (rather than at the individual level) leveraging AI for population health, analytics, and admin tasks. The Life Sciences sector has seen many recent generative AI announcements, signalling growth potential in the coming years. This trend is driven by the sector’s research-centric ethos and relatively less stringent regulatory environment for product adoption. The flexibility in this space allows for more rapid development and implementation of AI-driven solutions.
As the intersection of AI and Life Sciences continues to evolve, we anticipate seeing applications across drug discovery, personalised medicine, and biotechnology. To provide deeper insights into this dynamic field, we will be releasing a comprehensive report on the adoption of generative AI in Pharma & Life Sciences in October 2024. This report will explore current trends, emerging use cases, and the potential impact of AI on reshaping the future of life sciences research and development.
Use Case Clusters
Clinical Workflow Support emerged as the most advanced use case cluster, closely followed by Operational Support. The latter’s high rating can be attributed to its inclusion of customer support and professional services, which face lower regulatory hurdles. It’s worth noting that the total number of applications in our analysis exceeds the sum shown in the use cases diagram, as some applications span multiple categories, such as Patient Engagement and Operational Support, or both Workflow and Decision Support.
Patient Engagement and Drug Discovery also ranked prominently among use case clusters. We anticipate a surge in Drug Discovery product announcements in the coming months, driven by the sector’s pro-innovation mindset, which aligns with the Life Sciences industry’s eagerness to leverage AI to accelerate research.
Patient Engagement solutions present intriguing opportunities for generative AI implementation. Global initiatives increasingly emphasise individual control over healthcare and personal health data; however, this domain faces more stringent regulations such as HIPAA and GDPR due to direct patient involvement and the sensitive nature of personal health information. Regulatory factors also significantly impact the clinical decision support sector, but in this case, the regulations primarily relate to Software as a Medical Device (SaMD) due to the clinical nature of these applications. As a result, we’ve observed fewer product announcements in this particular use case cluster.
Capabilities
Although not directly related to the use case clusters, the short description depicts the application’s primary characterisation and sums up the total number of applications in the product database. It is important to note that the Foundation Model figure is largely inflated, as domain-specific foundation models in pathology, including Aignostic, PaigeAI, and Owkin, have very specific applications yet are considered foundation models in the product database. This includes research initiatives that are not operational, such as AntGLM-Med, which is a research-based foundation model in China.
Drug development leads with gen AI solutions announced, driven by its less regulated nature and openness to innovation. We expect this trend to continue throughout the year. Drug Discovery is a prominent application, leveraging AI to accelerate processes, optimise molecular design, explore new chemical spaces, handle complex drug identifications, augment datasets, enable efficient virtual screening, and facilitate drug repurposing. At Signify Research, we have segmented the market drug commercialisation process into five key steps; drug discovery, preclinical testing, clinical trials, regulatory approval, and manufacturing & commercial.
Transcribing medical encounters ranks second, addressing the burdensome task of writing medical notes. Some vendors offer multiple solutions, like Nuance’s DAX Copilot and PowerScribe One for medical imaging.
Medical chatbots follow closely, serving various end-users with question-answering capabilities. Data analytics applications benefit from natural language queries, enhanced data exploration, and improved anomaly detection and personalized recommendations.
Document processing is another key use case, with AI summarizing lengthy documents, extracting relevant information, generating structured content, and even translating between languages while preserving context.
Although most applications are commercially available and can be consumed by end-users, a large proportion is still being developed. It is possible that some use cases with current traction may have an interesting use case but lack commercial viability, leading to them being dropped. The database is updated quarterly, and high-level insights will be shared once published.
Lessons Learned
There are a few conclusions that can be drawn from the research.
Open-source models are going to be a lot more relevant as the technology stack continues to develop.
Big Tech companies are central to generative AI’s advancement due to their vast computational resources, cutting-edge R&D, extensive data access, and established cloud platforms. Giants like Google, Microsoft, Amazon, and NVIDIA are uniquely positioned to drive innovation in this field. Their ability to develop, integrate, and deploy AI solutions at scale gives them a significant edge. As generative AI evolves, these companies are likely to continue shaping its trajectory, leveraging their technological prowess and market influence to set industry standards and push the boundaries of AI capabilities.
Currently, Big Tech companies maintain their competitive edge in generative AI through proprietary models. However, the increasing popularity of open-source alternatives could potentially erode this advantage. To stay relevant, major tech firms may need to adjust their approach, possibly by incorporating open-source models into their service offerings (such as AWS Bedrock) to keep health tech vendors engaged.
The AI platform landscape is likely to evolve, with smaller AI companies gravitating towards the infrastructure provided by Big Tech. This consolidation around major tech platforms could become the norm for smaller AI firms looking to deploy their technologies. However, this trend towards consolidation might face regulatory hurdles. Antitrust bodies like the FCC are already scrutinizing the AI field, and their interventions could potentially alter the trajectory of this market consolidation.
Despite Big Tech’s influence, healthcare technology vendors will play a crucial role in gen AI deployment within the industry. These vendors possess deep knowledge of healthcare’s unique requirements, regulations, and complexities. This expertise positions them to effectively tailor and implement AI solutions that address the specific needs of healthcare providers, patients, and regulatory bodies.
While Big Tech may drive the development of generative AI technologies, their successful adoption in healthcare will largely depend on the intermediary role of healthcare technology vendors. These specialised firms bridge the gap between cutting-edge AI and practical healthcare applications, ensuring solutions are not only technologically advanced but also compliant and contextually appropriate.
This dynamic creates a symbiotic relationship: Big Tech provides the technological foundation, while healthcare technology vendors offer the sector-specific expertise necessary for effective implementation. As the landscape evolves, collaboration between these entities will likely intensify, fostering innovation that aligns with healthcare’s stringent demands and unique challenges.
Symbiotic relationships are being formed through a partnership-centric approach between big tech, health vendors and small start-ups.
In examining potential partnerships in the healthcare AI space, we can broadly categorize them into two groups: collaborations between Big Tech and established healthcare vendors, and those between Big Tech and smaller vendors.
Historically, Big Tech companies have struggled to directly penetrate the healthcare market with proprietary solutions, often serving instead as technology enablers through their cloud, AI, and ML platforms. This can be attributed partly to their lack of specialised clinical expertise, which is more prevalent among healthcare vendors and niche-focused startups.
In the first category, directly challenging healthcare giants like Siemens Healthineers, GE HealthCare, Epic, and Philips presents a daunting task for Big Tech. Instead, forming alliances with these established players has proven more fruitful, as evidenced by collaborations such as GE-NVIDIA and Philips-AWS. We anticipate this trend to continue, with potential partnerships like Microsoft-Siemens on the horizon. Alternatively, strategic acquisitions, such as Microsoft’s purchase of Nuance or Oracle’s acquisition of Cerner, offer. Big Tech companies a faster route to establishing a significant presence in healthcare applications.
The second category presents a different dynamic. While Big Tech could theoretically compete with startups by developing similar solutions, the question arises: is it worth their effort? Startups typically target specific niches with focused solutions that may be too narrow for Big Tech’s broad scope. Instead, Big Tech companies have several strategic options:
- Investment and Acquisition: This approach allows Big Tech to quickly access innovative technologies, skilled teams, and new market opportunities. For instance, Google invested in Rad AI, which recently integrated Google’s MedLM into its offerings.
- Strategic Partnerships: Collaborations with emerging companies can be mutually beneficial. Paige AI’s partnership with Azure, for example, has enabled cost-effective digitisation of pathology slides, solidifying its position as a leading digital pathology vendor.
- Accelerator Programs: By adopting a venture capital-style approach, Big Tech can nurture multiple startups simultaneously. This strategy allows for diversified investments and early identification of promising innovations. AWS’s generative AI accelerator exemplifies this approach, providing startups with resources to innovate and scale.
This evolving landscape underscores the complex interplay between Big Tech, established healthcare players, and innovative startups. As the boundaries between these sectors continue to blur, we can expect to see more diverse and creative collaborations emerging, each aiming to leverage the unique strengths of different players to advance healthcare AI solutions.
Ambient Listening space will become consolidated in the mid-term .
The ambient listening technology landscape is experiencing significant shifts, challenging the long-standing dominance of Nuance. Despite its impressive 80% market penetration among healthcare providers, Nuance now faces fierce competition from innovative startups like Abridge, Nabla, DeepScribe, Suki, and numerous others listed in our product database.
This disruption stems from the democratisation of advanced ambient technology. Emerging companies can now offer comparable solutions at various price points, a factor that resonates strongly with healthcare decision-makers. In the coming year, we anticipate that several top-tier ambient listening solutions will emerge as strong challengers to Nuance’s market position. As the underlying technologies become more homogeneous, key differentiators will likely revolve around seamless integration capabilities, particularly native EMR integration. The ability to effortlessly exchange information with EHRs without requiring separate application integrations will be crucial.
Nuance currently leads in this sector, leveraging its tight integration with Epic, with collaborations with other key EHR companies. To preserve its market position, Nuance would benefit from reinforcing and broadening these integrations. Furthermore, Nuance’s affiliation with Microsoft adds another dimension to its market strategy. This partnership is likely to result in comprehensive enterprise packages for Accountable Care Organizations (ACOs) and Integrated Care Networks (ICNs). These bundles are expected to combine Azure Cloud services, Office applications, and DAX, offering a compelling, all-in-one solution for healthcare organizations.
As the market matures, we may see consolidation, with larger tech firms or EHR vendors potentially acquiring promising startups to enhance their offerings. This dynamic environment underscores the importance of continuous innovation and strategic partnerships in maintaining a competitive edge in the healthcare AI space.
Epic, a major player in the EHR space, might consider developing its own ambient listening technology, following Oracle Cerner’s recent introduction of its Digital Assistant tool. Such native integrations could significantly influence healthcare providers’ choices, as seamless compatibility with existing EHR platforms could streamline workflows and enhance clinician efficiency.
However, this trend doesn’t necessarily spell doom for independent ambient listening solution providers. Companies like Suki may maintain a competitive edge by offering vendor-agnostic solutions compatible with multiple EHR platforms, including Oracle, Epic, Meditech and Athenahealth. This versatility could prove attractive to healthcare organisations seeking flexibility.
The healthcare industry’s broader push towards interoperability has simplified the integration process, making it easier to implement third-party solutions alongside EHR systems. This is evident in Epic’s marketplace, which now features 12 different ambient listening technologies for primary care. We can expect similar developments from other EHR vendors.
Ultimately, the success of these technologies will likely depend on their ability to deliver superior performance, user experience, and adaptability across various healthcare settings. As the market matures, we may see a mix of EHR-native and independent solutions coexisting, each catering to different organizational needs and preferences.
Epic Marketplace Ambient Listening Solutions
Integration of generative AI is an expensive deployment and vendors are unsure how the integration will be commercialised.
The gen AI ecosystem presents a complex commercial landscape, particularly in the healthcare sector. The development of LLMs requires substantial investment, positioning big tech vendors as crucial providers of the underlying technology stack. However, this is just the beginning of the cost consideration chain.
From a health tech vendor’s perspective, the deployment and integration of gen AI solutions represent a significant expense. The crucial question remains: Who will bear these costs? Several scenarios present themselves, each with its implications:
- One possibility is that healthcare providers will shoulder the expense, anticipating increased patient throughput and, consequently, higher revenue. However, this approach may inadvertently exacerbate the issue of clinician burnout. If the technology’s ROI is predicated on seeing more patients per day, clinicians might still face long working hours, potentially negating the burnout-reduction benefits of AI assistance.
- Transferring costs to patients is another option, but it raises concerns about healthcare equity. If given a choice, financially constrained patients might opt for lower-cost, non-AI consultations, like how AI-assisted radiology readings cost more than standard ones. This could create a two-tiered system where advanced AI care is accessible mainly to those with greater financial means, potentially widening healthcare disparities.
Healthcare IT vendors will likely bear the initial costs associated with implementing these technologies. These vendors will then make strategic decisions on whether to absorb these expenses or pass them on to healthcare providers. This positioning gives IT vendors significant influence over the cost structure and adoption rates of new technologies in the healthcare sector.
Most gen AI solutions, function as features of existing systems rather than standalone products. This classification complicates pricing strategies. How does one effectively monetise a feature? This question is especially pertinent for solutions that enhance existing workflows without necessarily creating new billable services.
As the industry moves forward, finding sustainable economic models that balance cost, accessibility, and quality of care will be crucial. This challenge extends beyond mere technological integration, touching on fundamental aspects of healthcare delivery and economics.
Our ongoing research aims to address these critical questions, providing insights and potential strategies for industry stakeholders. By exploring various deployment models, pricing strategies, and their impacts on healthcare delivery, we hope to contribute to the development of sustainable and equitable AI-enhanced healthcare systems.
This Insight offers a high-level summary of our findings on these applications. For a more detailed exploration, feel free to connect with the Signify team. It’s crucial to acknowledge that the gen AI product landscape is rapidly evolving, with new products surfacing regularly. Please note that our database exclusively tracks applications utilising gen AI; products without generative AI capabilities, such as non-generative NLP solutions, are not part of the scope.
About The Author
Jon joined Signify Research in 2023 as a Market Analyst in the Digital Health team. He holds a BSc in Pharmacology having graduated from the University of Bath in 2023.
About the Digital Health Team
Signify Research’s Digital Health team provides market intelligence and detailed insights on numerous digital health markets. Our areas of coverage include electronic medical records, telehealth & virtual care, remote patient monitoring, high-acuity clinical information systems, patient engagement IT, health information exchanges and integrated care & value-based care IT. Our reports provide a data-centric and global outlook of each market with granular country-level insights. Our research process blends primary data collected from in-depth interviews with healthcare professionals and technology vendors, to provide a balanced and objective view of the market.
About Signify Research
Signify Research provides health tech market intelligence powered by data that you can trust. We blend insights collected from in-depth interviews with technology vendors and healthcare professionals with sales data reported to us by leading vendors to provide a complete and balanced view of the market trends. Our coverage areas are Medical Imaging, Clinical Care, Digital Health, Diagnostic and Lifesciences and Healthcare IT.
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