Due to the potential to enhance the efficiency, innovation, and effectiveness of healthcare delivery, foundation models, large language models (LLMs), and generative artificial intelligence (AI) have captured the interest of healthcare providers, biopharmaceutical companies, payers, and investors in the past year. While traditional scientific AI has been utilized in healthcare for an extended period, conceptual AI distinguishes itself by its ability to “reason and plan,” enabling tasks such as generating new content, summarizing existing content, and translating information.
Here are some examples of the applications of these systems:
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Conceptual AI offers the potential to streamline the documentation of physician visits and communication related to reimbursements, reducing administrative costs and physician burnout. Companies like HCA Healthcare are exploring these opportunities through partnerships with technology firms like Google, leveraging tools to facilitate interactions with healthcare providers effectively.
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Generative AI is driving innovation in medical research and drug development, exemplified by the collaboration between Sanofi and BioMap to streamline the drug discovery process using BioMap’s IoT platform. Additionally, LLMs tailored for chemical biology are aiding in predicting protein structures and target-binding affinity, further advancing drug development.
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Insurers and payers are increasingly adopting conceptual AI for tasks such as representative navigation and patient communication, as seen in UnitedHealth Group’s use of virtual assistants. This technology enhances data analytics, claims management, quality control, and risk management in the healthcare industry.
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Medical technology companies are investing in cutting-edge diagnostic tools powered by AI, medical robots, and intelligent remote monitoring devices to improve disease identification and patient care. For instance, Philips is collaborating with Amazon Web Services to implement conceptual AI in PACS image processing, enhancing radiology workflows and patient care.
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Across the healthcare value chain, conceptual AI presents opportunities for various applications, including supply chain management and back-office functions, as foundational designs and computer vision technologies mature.
In 2023, there was a notable increase in news coverage surrounding the integration of conceptual AI in healthcare.
A preliminary funding level
Healthcare organizations have recently begun investing in relationalAI .
To leverage conceptual AI tools, technology companies are partnering with major healthcare institutions. For example, Microsoft and Epic have collaborated to streamline doctors’ documentation processes and email responses. Google is working with Bayer to facilitate the creation of clinical trial contracts in multiple languages, while also collaborating with iCad to integrate AI tools for breast cancer detection. IBM is working with Microsoft Azure to analyze complex medical records efficiently. Care information technology vendors are at the forefront of integrating relational AI into their extensive networks of users and providers.
Venture capital and growth equity funds are investing in companies specializing in conceptualAI. For instance, General Catalyst and Andreessen Horowitz co-led a funding round that raised \(50 million for HippocraticAI, a healthcare-focused LLM company. Genesis Therapeutics, a drug discovery platform utilizing generative AI, raised \)200 million in a series B round with participation from Andreessen Horowitz, Fidelity, and BlackRock.
Mature private equity (PE)-backed companies are also investing in LLMs to drive operational enhancements, such as improved clinician and patient engagement. For example, Syneos Health partnered with Microsoft to utilize OpenAI’s ChatGPT in medical research, while Iodine Software integrated LLMs into its AwareCDI suite with support from Advent.
The impact on new investments and portfolio companies
Private equity investors need to assess the implications of relational AI on their portfolio companies to stay ahead in this rapidly evolving landscape. Evaluating the exposure of portfolio companies to conceptual AI disruption is crucial, considering potential threats and opportunities. Bain has developed a framework to measure disruption risk, considering factors like labor intensity, task complexity, frequency of interactions, and virtual pattern recognition value.
Processes like content creation, labor-intensive operations, and call center tasks pose significant risks and opportunities due to relational AI. While it may streamline operations in low-skilled labor-intensive processes, it presents less disruption risk in areas requiring professional expertise, like healthcare. Investors should develop a strategic plan focusing on leveraging existing conceptual AI tools, developing specialized software for competitive advantage, and establishing governance frameworks to ensure ethical AI use.
Investors should also evaluate the impact of relational AI on new acquisitions, considering its potential to disrupt operations and create value.
The long-term potential of conceptual AI
Many tasks in healthcare require human judgment due to the complexity of individual cases. AI models have had limited impact in areas requiring discretion due to small training datasets. However, relational AI shows promise in addressing these challenges and is expected to drive significant efficiency gains, financial improvements, and better healthcare outcomes.
Anticipated advancements in focused use cases will likely transform healthcare processes, software, and workflows. Investors can capitalize on this technological evolution by understanding how conceptual AI impacts their portfolio companies and new investments, driving returns and catalyzing healthcare sector transformation.
