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Cranfield, UK, 30 September 2024 –
As we move into the latter half of 2024, the integration of generative AI into healthcare IT systems is accelerating at an unprecedented pace. Earlier this month, Signify Research published its Q3 2024 Product Database as part of its broader Generative AI Market Intelligence Service. This comprehensive database, which now tracks more than 550 hundred announced products, offers valuable insights into the rapidly evolving landscape of generative AI in healthcare IT. By analysing this data, we can identify key trends, challenges, and opportunities in this dynamic field.
The product database has been refreshed from the previous iterations with new segmentations to better reflect the nature of the healthcare IT market. The following Insight shares a high-level overview and examines the current state of generative AI in healthcare by exploring its applications. Over the next few weeks we’ll be provided more detailed analysis on the five application groups highlighted in this insight within our SPI: Generative AI service. Please contact us if you’d like to have access to this analysis.
Signify View
The generative AI ecosystem in healthcare consists of five distinct layers that can be seen in the graphic below. While all layers play crucial roles, this article focuses primarily on the application layer, where the most immediate and visible impact on healthcare delivery and operations can be observed. Within the full report Signify Research analyses the AI Platforms and Foundations Models layers.
Within the application level there are several segments that have been identified, namely:
- Data: Announcements related to the generation, processing, and management of data using gen AI. It includes innovations in data collection, analysis, and utilization.
- Digital Health & EMR: This segment includes announcements about generative AI applications within digital health technologies and electronic medical records (EMR) systems. It covers advancements aimed at improving patient engagement, clinical documentation, and the integration of AI tools to optimize the functionality of digital health platforms such as ambient listening and EMR systems.
- Medical & Clinical IT: This segment pertains to the use of generative AI in medical and clinical information technology. It involves developments that support clinical workflows, decision-making processes, and the management of medical IT infrastructure to enhance patient care and operational efficiency within clinical settings.
- It’s important to note that the Medical & Clinical IT category currently focuses solely on medical imaging applications, as no clinical generative AI applications have been identified at the time of writing. This is largely due to the heightened regulatory scrutiny and the hardware-centric nature of the market, where software is typically certified alongside the hardware. However, clinically collected data is being utilized in generative AI applications, which fall under the Digital Health & EMR category
- Pharma & Diagnostics: This segment focuses on the role of generative AI in the pharmaceutical and diagnostic industries. It includes advancements in drug discovery, clinical research, personalized medicine, and other areas where AI is used to drive innovation and improve outcomes in pharma and diagnostics.
- Operational Support: This segment covers the application of generative AI to support operational functions within healthcare organizations. It includes tools and solutions designed to streamline administrative tasks, optimize resource management, and enhance overall organizational efficiency in healthcare settings.
The Low Hanging Fruit
Generative AI can have a transformative impact on data ingestion, processing and analysis. In particular, its impact on data processing and analytics stands out. This is due to the fact that generative AI enables search across different data types, structured & unstructured, and also facilitates a conversational approach to data manipulation. This democratizes data analysis, allowing non-technical users to interact with and interpret data—tasks previously limited to specialised SQL professionals. As a result, generative AI ‘de-skills’ data analysis, making it accessible to a broader audience. Functional data visualisation will likely be the next step for application developers, perhaps feeding the summarised data into Power BI, Qlik or other analytics platforms to enable easy to use, interactive dashboards.
Digital Health & EMR applications represent the largest segment of application announcement tracked. The most prominent sub categories include Ambient Listening, Patient Engagement and Revenue Cycle Management.
- Ambient Listening is a great area for the use of generative AI. The prevalence of this use case can also be attributed to generative AI’s capability to summarise unstructured data. Signify Research has delved into the topic of Ambient Listening in more detail in a free Insight that can be accessed here.
- Revenue Cycle Management has the second highest number of announcements. This is a broad topic and includes coding, prior authorisation, claim management and denial etc. High numbers of applications in this sub-category can be attributed to the fact that generative AI can excel at turning unstructured data formats into structured ones (coding) and facilitating fast information assimilation (prior auth).
- Patient Engagement is another category that benefits significantly from the input from generative AI. This is due to software’s capability to offer personalisation, which is extremely important when dealing with the diverse patient population.
The use cases mentioned above have become the primary focus for vendors due to their immediate impact and relatively low barriers to entry, as these applications do not require Software as a Medical Device (SaMD) clearance. In contrast, medical imaging has the lowest share of generative AI applications, primarily centring on automated reporting solutions from companies like Nuance, Rad AI, and Smart Reporting. Other potential use cases are still in the development or concept testing stages and are generally unavailable due to the regulatory scrutiny surrounding SaMD regulations. However, significant research is being conducted in the medical imaging space, with the development of new foundation models. Key players in this area include HOPPR, Harrison AI, Microsoft, and Google.
Another area prime for disruption by generative AI is the R&D space within the pharma value chain. R&D is inherently complex, requiring the integration of data from diverse sources—tasks that are challenging for humans but ideally suited to generative AI. Unlike traditional AI, which may struggle with multimodal data, generative AI can seamlessly handle different formats such as images, text, audio, and genome sequences, all stored within the same vector database. This enables models to draw insights from a vast and varied range of inputs, accelerating the discovery process and enhancing decision-making. Moreover, generative AI’s ability to recognize and synthesize intricate patterns that may not be immediately intuitive to humans makes it ideal for structure prediction. A prime example is AlphaFold 3, which goes beyond individual protein structure prediction to address more complex tasks such as predicting protein-protein interactions, protein folding dynamics, and multi-domain assemblies. This capability offers new potential for breakthroughs in drug discovery and biotechnology.
One of the most accessible opportunities for generative AI lies in operational support, particularly in call centres and FAQs. These areas are prime for disruption, where generative AI can replace large numbers of onshore and offshore workers, offering faster, more efficient access to information. However, this is a broad, horizontal application, making specialization in this niche challenging. Generic generative AI-powered customer service solutions and FAQ bots, such as those built on OpenAI models with organizational databases for Retrieval Augmented Generation (RAG), can perform almost as well as healthcare-specific offerings. One standout in this field is Hippocratic AI, which recently secured additional funding from NVentures, NVIDIA’s venture capital arm. Signify Research’s Chief Editor, Alan Stoddart, wrote an insightful piece earlier this year, diving into what sets Hippocratic AI apart from other competitors. You can access the full article here.
Conclusion
From the analysis, generative AI proves highly valuable in healthcare for several key reasons:
- Natural language for data interrogation and manipulation, allowing users to interact with complex datasets more intuitively.
- Summarizing and indexing documentation, streamlining workflows and reducing administrative burdens.
- Converting unstructured data into structured formats, enhancing data accessibility and usability.
- Enabling personalization, offering tailored insights and solutions for patients and providers alike.”
While the potential of generative AI in healthcare is immense, several challenges must be addressed:
Results from the Vendor Sentiment Index
Looking ahead, we can expect greater integration of generative AI across all aspects of healthcare delivery and management, supported by increasingly sophisticated, multimodal AI models capable of processing diverse medical data types. There will be a growing focus on explainable AI to ensure transparency and trust in AI-driven healthcare decisions. At the same time, regulatory frameworks will continue to evolve to keep pace with the rapid advancements in AI innovation within the healthcare sector.
The graphic below illustrates how many management consultants approach generative AI use cases. While straightforward, it effectively highlights key considerations. Generative AI is a powerful tool that vendors should be integrating, particularly for high-value, low-friction applications. For organizations seeking to build a long-term competitive advantage, high-risk, high-reward use cases are also worth exploring—though these require careful consideration. At Signify Research, we extensively track the generative AI market and can help prioritize the right use cases for your business, avoiding the trap of implementing AI just for the sake of it. By strategically integrating generative AI, healthcare organizations can stay ahead of the curve, improving patient outcomes while delivering more efficient and effective care.
If you’re interested in discovering how Signify Research can guide you through the complexities of the generative AI market and unlock new opportunities, feel free to reach out to our lead analyst at Vlad.Kozynchenko@signifyresearch.net. We would be delighted to explore how our insights can support your business. For more information about our services, click the link below to download a brochure.
Related Research
Generative AI Market Intelligence Service
This Market Intelligence Service delivers data, insights, and thorough analysis of the worldwide market potential for vendors leveraging Generative AI in healthtech. The Service encompasses Medical/Clinical IT, EMR & Digital Health, Pharma & Life Sciences, and Big Tech vendors, exploring their opportunities and strategies in the realm of generative AI
About The Author
Vlad joined Signify Research in 2023 as a Senior Market Analyst in the Digital Health team. He brings several years of experience in the consulting industry, having undertaken strategy, planning, and due diligence assignments for governments, operators, and service providers. Vlad holds an MSc degree with distinction in Business with Consulting from the University of Warwick.
About the AI in Healthcare Team
Signify Research’s AI in Healthcare team delivers in-depth market intelligence and insights across a breadth of healthcare technology sectors. Our areas of coverage include medical imaging analysis, clinical IT systems, pharmaceutical and life sciences applications, as well as electronic medical records and broader digital health solutions. 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 healthtech 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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