The State of Tech in Healthcare: How AI is transforming the entire value chain

Below is a snippet of our State of Tech in Healthcare report. Read on to learn how about the key technologies, players, investments, use cases, and more in healthcare. The full Edge Insight spans the R&D, disease detection and diagnosis, prevention and wellness, treatment and therapy, and care delivery and management areas.

By
Albert Inningslee
on
January 2, 2025
Category:
Health & Wellness

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Below is a snippet of our State of Healthcare Tech report. You can download the full report here.

We have examined several product launches, partnerships, funding rounds, and M&A deals in the healthcare sector related to emerging technologies, such as AI, 3D printing, robotics,internet of medical things (IoMT), digital twins, XR, and genomics, that occurred in 2024. The following analysis highlights key technologies that have seen significant rates of adoption at different stages of the healthcare value chain.

Key takeaways

  • Classical AI—particularly machine learning (ML)—was the most widely used technology across almost all focus areas. ML alone was present in over 40% of activity, with classical AI as a whole (including computer vision and natural language processing [NLP]) accounting for around half of our database. In fact, USD 100 million+ fundraisings by Sword Health, Transcarent, Terray Therapeutics, and Caresyntax were to advance their ML capabilities. A notable partnership was that between Sanofi, OpenAI, and Formation Bio to accelerate drug development using AI. Big Techs have also been active, with Alphabet’s Verily Life Sciences pivoting toward AI integration in healthcare, and Microsoft expanding its healthcare AI initiatives by forming a new consumer AI health unit with former DeepMind colleagues. We believe that recent moves by the FDA and the European Medicines Agency (EMA) to accommodate drug applications that incorporate AI and ML components support increased adoption and investment in AI-based technologies.
  • The application of generative AI (GenAI) has accelerated, making it the technology with the second-highest adoption rate at nearly 25%. It is most widely used in R&D, particularly in drug discovery and development. Formation Bio, for example, raised USD 372 million to enhance its AI capabilities in drug development (e.g., recommending R&D decisions and better predicting drug toxicity, tolerability, and efficacy) and clinical trials (e.g., generating patient recruitment materials and adverse event reports). Other drug discovery startups using GenAI that launched during the year were Xaira Therapeutics (with USD 1 billion in funding) and EvolutionaryScale (with USD 142 million in funding). Abridge, meanwhile, uses GenAI in clinical documentation to generate structured clinical notes from patient-clinician conversations. The company secured USD 150 million in funding and entered several partnerships during the year to enhance its platform and integrate it into healthcare provider networks.
  • 3D printing activity was focused on use cases for surgical support and drug and therapy development. In surgery, it was used to develop orthopedic and dental implants (e.g., Restor3d), and anatomical models for surgical planning (e.g., Ricoh and Stratasys). In drug development, 3D organ models were developed to predict the human body's response to treatment (e.g., Sanofi), synthetic tissues and organs were developed for therapeutic application (e.g., Pandorum Technologies and FluidForm Bio), and 3D printing was used to personalize dosages and dosage forms (e.g., APL).
  • Robotics saw increased use in surgical procedures since its introduction decades ago. Several products received FDA clearance, including Intuitive Surgical for the latest version of its Da Vinci system, THINK Surgical for its TMINI miniature robotic system and Moon Surgical for its Maestro system. In addition, a number of healthcare providers have partnered with developers to integrate their robotic systems to enhance patient services.
  • XR was used primarily for wellness and treatment applications. The technology allows clinicians to treat patients remotely (e.g., XRHealth) and to study therapies, procedures, and clinical trial simulations (e.g., GE Healthcare, Osso VR, and Neo Medical). On the wellness front, major technology companies such as Meta and Samsung Electronics are using it to support mental well-being.
  • R&D saw the most technology adoption, particularly in the use cases of drug discovery, genetic testing, drug development, and clinical trials. Drug development is notoriously costly and complex, with a drug taking 10 to 15 years to advance from initial discovery to regulatory approval and costing up to USD 5.5 billion. What’s more, around 90% of drugs never reach the market. If emerging technologies can be used to improve the success of drug candidates and reduce development costs, their use is an obvious choice. For example, the AI drug discovery alliance between AstraZeneca and BenevolentAI has resulted in the advancement of four targets as of June 2024/

Technology adoption intensity

Source: SPEEDA Edge Research

R&D: AI-based technologies see continued and widespread adoption

Summary of current tech trend

R&D saw the most activity in the drug discovery and development space, with AI-based technologies playing a commanding role. This aligns with the results of a 2023 survey, where more than half of key opinion leaders in the pharmaceutical industry identified AI as the top technology for drug manufacturing over the next two years.

The most disruptive technologies in drug manufacturing in the next two years, 2023

Among AI technologies, applying traditional AI—mainly ML—dominated our analysis. For instance, in 2019, AstraZeneca and BenevolentAI entered a multi-year partnership to identify novel targets for idiopathic pulmonary fibrosis (IPF) and chronic kidney disease (CKD). The partnership leverages BenevolentAI’s proprietary AI-enabled drug discovery platform, which applies ML and natural language processing (NLP), and AstraZeneca’s disease expertise. The collaboration was extended to include additional therapeutic areas—heart failure and systemic lupus erythematosus (SLE)—in 2022. Novel targets have been added to the discovery portfolio since then, with the most recent addition in June 2024. The alliance has resulted in AstraZeneca progressing four CKD and IPF targets out of the five initially selected.

In the clinical trials space, Sanofi, Formation Bio, and OpenAI collaborated to develop "Muse," an AI-powered tool to optimize patient recruitment for clinical trials. Muse aims to address low clinical trial participation by developing tailored strategies for diverse patient populations, with Sanofi planning to initially deploy Muse in Phase III multiple sclerosis trials.

In the case of GenAI, which can be applied to multiple functions in healthcare, a survey conducted by McKinsey & Company indicated it has the greatest potential value in improving clinical productivity, patient engagement and experience, and administrative efficiency.

Areas believed to benefit most from GenAI

Nevertheless, in our analysis of emerging tech-related activities in 2024, GenAI recorded the second-highest number of activities within pharma R&D, with a concentration in drug discovery.

For instance, Xaira Therapeutics raised USD 1 billion in funding for its market entry. The funding was also to develop a drug discovery and development platform that combines ML advancements, including GenAI, data generation, and therapeutic product development technologies, to advance multiple drug programs across various disease areas.

Additionally, in 2024, several companies in the drug discovery space, including Enveda Biosciences, Recursion Pharmaceuticals, and Exscientia, partnered with Big Techs, such as Microsoft, Amazon, Alphabet, and NVIDIA, to develop and upgrade models with advanced AI capabilities, including GenAI. What's more, Big Pharma such as Novartis, Bristol Myers Squibb, and Merck collaborated with startups to leverage their capabilities in developing new targets and therapies, while Eli Lilly and Moderna incorporated OpenAI’s GenAI technology into their own drug discovery processes.

Among the Big Techs, Alphabet, Amazon, and Microsoft recorded the most activity, with Alphabet leading. A key event was the launch of AlphaFold3, a new version of the AlphaFold AI technology developed by Google DeepMind and Isomorphic Labs, which helps scientists understand the behavior of microscopic mechanisms that drive the cells in the human body.

Notable activity across R&D

Tech deep dives

ML: Continues to gain traction as regulatory bodies accept their role in R&D

ML approaches have been used in pharma R&D over the past couple of decades with increasing sophistication, and they are being applied across the R&D spectrum, spanning drug design, drug development, and clinical trials.

In drug design and development, ML is used to analyze vast datasets to find new drug candidates, optimize formulations, and predict potential drug targets—speeding up the process while saving time and resources. In clinical trials, ML is used to design and monitor trials and analyze their results, increasing efficiency and success.

While industry leaders have long recognized the vast potential of AI in pharma R&D, regulators have only more recently acknowledged the increasing use of AI and ML in drug development and started taking steps to accommodate them.

According to the FDA’s Center for Drug Evaluation and Research (CDER), the number of drug applications incorporating AI and ML components has “increased drastically in the past five years” from just three submissions in 2018 to 170 in 2022. These submissions span the drug product lifecycle, including the non-clinical, clinical, post-marketing, and manufacturing phases. In 2024, the CDER AI Council was established to provide oversight, coordination, and consolidation of CDER activities around AI use. The council also intends to advance innovative uses of AI and help the CDER meet policy and regulatory requirements.

Meanwhile, across the Atlantic, in December 2023, the EMA and the Heads of Medicines Agencies (HMAs) published an AI work plan up to 2028, outlining a strategy to maximize the benefits of AI to stakeholders while managing the risks.

 GenAI: Unlocks new levels of productivity across the drug development process

The potential for GenAI to accelerate timelines and reduce costs at every step in the drug discovery and development process is significant, given that it typically takes decades and costs USD 1 billion–2 billion per therapy.

 GenAI is used to analyze large datasets, rapidly identify promising candidates for clinical trials, optimize molecular structures, and predict potential side effects and interactions, resulting in an accelerated drug development process. GenAI can also predict the efficacy and safety of new drugs, leading to further time and cost savings.

This potential was validated by an EY survey of 15 senior R&D decision-makers at biopharmaceutical and biotech companies, who asserted that using GenAI reduces costs by over 50% over the long term across the entire drug development process.

Impact of GenAI on the cost of each step in drug discovery and early-stage drug development

 

And that ends our selection of our State of Tech in Healthcare selection from our report. You can download the full report here. To learn more about how SPEEDA Edge can help your company stay ahead of the healthtech curve, contact us for a personalized demo.

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Albert Inningslee
Research, Analysis, and content at SPEEDA Edge