By Padraic Hugh
As an ttopstart intern for the past 5 months, I’ve been helping the company adapt to AI – both from an in-house perspective as a tool to provide services to clients and to provide actionable insights on the project sustainability of big data and AI driven projects. This simultaneous macro and micro lens has been really insightful for me, especially as I am a person who naturally seeks to view the application of new technologies in as many different settings and environments as possible.
AI and Big Data driven projects at IMI/IHI level
Figure 1
The Innovative Health Initiative (IHI), a key funding instrument within the Horizon Europe framework, used a novel public-private partnerships model which includes industrial partners to drive healthcare innovation. Building on its predecessor, the Innovative Medicines Initiative (IMI), IHI aims to translate research into tangible patient outcomes. With a €2.4 billion budget for 2021-2027, it unites diverse healthcare sectors and industry associations.
In recent years, there’s been a notable surge in funding for AI and big data-driven projects in healthcare. Figure 1 illustrates this trend, showing increasing investments at both Horizon Europe and IMI/IHI levels. This reflects the growing recognition of AI and big data’s potential to revolutionize healthcare, from drug discovery to personalized medicine.
Not in the ecological sense, my research has focused on the challenges associated with project sustainability of digital assets (procured datasets, digital tools, developed algorithms that are generated as a result of large EU funded consortia projects) for projects under the Innovative Medicines Initiative (IMI) or the Innovative Health Initiative (IHI) funding instrument. This concerns the search for financial support and the mechanisms for the survival of assets and the continuation of the value of the project, post-funding period. The drop-dead date in IMI and IHI funding has been commonly cited as a major issue amongst project stakeholders and so, it is a great business opportunity to adapt and absorb domain-specific knowledge on major considerations when working with AI and big data related projects. Through a series of qualitative interviews with experienced IMI and IHI partners across a range of different therapeutic areas and technological areas, a host of challenges were identified.
- Lack of feasible sustainability planning from the project’s inception
- Challenges in data sharing and access, particularly for sharing across entire consortia
- The decision whether to sustain or not: a dedicated standalone infrastructure vs integrating into existing systems vs the continuation of the project via future IMI/IHI projects
- Adapting project sustainability plans to rapidly changing technological advancements in AI and data analytics – To sustain or not to sustain in such a fast changing world?
- Balancing large data gathering aspirations with specific scientific goals is a zero sum game
These findings have significant implications for ttopstart and our clients. It encourages a thematic focus for our consultants, as well as developing a comprehensive set of strategies to better serve project sustainability goals. The actions generated by this research incorporate sustainability planning into project proposals from the outset, addressing glaring flaws in the process of building effective data-sharing protocols at as early a stage as possible, in order to give consortia an accurate inventory of the assets at their disposal, which leads heavily into the discussion around what the most appropriate sustainability mechanisms should be. In addition, it relieves the time pressure commonly felt by IMI and IHI projects that seek to achieve a combination of operational (data gathering, data harmonisation) and scientific goals. Our goal is to ensure that these innovative AI and big data projects continue to contribute to healthcare advancements well beyond their initial funding cycles. By translating these insights into actionable strategies, this research aims to maximize the long-term impact and value of our clients’ research initiatives in the evolving landscape of healthcare innovation.
AI within ttopstart
While diving into the world of AI and Big Data at EU funding level, I’ve also been part of the ttopstart AI working group – exploring and characterizing how the company can leverage LLMs to improve the value proposition for clients.
While diving into the world of AI and Big Data at EU funding level, I’ve also been part of the ttopstart AI working group – exploring and characterizing how the company can leverage LLMs to improve the value proposition for clients.
ttopstart is very much at the start of the journey of integrating LLMs into service offerings, across grant proposal, business strategy, market research and project management consultancy. What is known however, is that the business of science communication is ultimately a humanistic one that requires nuanced understanding, empathy and a deep awareness of the context around client requests and their field of expertise, which is not something that can be replicated by AI in it’s current form. Colleagues equipped with AI are on track to replace those without it (if they haven’t already). Subscribing to this perspective, ttopstart is trialling a wide range of AI tools across services and teams. Some of the most notable tools currently used in-house, in additional to many other useful tools recommended by experts within the EU funding space are included below in Table 1. Gaining hands on experience with developing and constructive prompts has so far been equal parts fruitful and challenging. Ttopstart is developing an in-house syllabus which will act as a starting point for integrating LLMs into various workflows, ensuring the team is equipped with a foundational knowledge and an awareness of the true power of large language models.