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On April 29th, we had the privilege of hosting the second edition of our Enterprise AI Series breakfast sessions, focusing on the rapidly evolving landscape of Engineering Intelligence. This expert-led discussion convened founders, investors, operators and industry leaders, all united by a commitment to shaping the next frontier of industrial innovation.
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đź’ˇ Summary
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- The cost of bringing a drug to market is $1Bn on average.
- That 60% of those costs can be attributed to the pre-clinical stage ($600M).
- That 50% of pre-clinical stage costs is R&D related ($300M). And finally that AI could yield up to a 50% cost-saving in this stage.
đź’ˇ Summary
Notably, the dialogue echoed several key insights uncovered by Forestay during our due diligence of Neural Concept, reinforcing the alignment between market trends and the innovations driving leading AI startups in the engineering space. Together, these insights offer a robust perspective on where the field stands today and where it is headed. Below, we elaborate on five pivotal themes that emerged, providing broader context and actionable implications for organizations navigating this transformative domain
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Key Takeaways:
- The integration of Large Language Models (LLMs) in healthcare presents significant opportunities, particularly given the exponential growth of electronic health records (EHRs), medical literature, and patient-generated data. These tools could significantly enhance healthcare professionals’ ability to extract insights and make informed decisions.
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However, the complexity of healthcare data extends beyond traditional text and numerical formats, encompassing extensive imagery from radiology and non-standard data structures like ECG chart results. In Europe, initial adoption was somewhat constrained by the regulatory environment and relatively limited collaborative efforts, though research has continued steadily. The NHS in the UK has made progress in this area, granting closed access to certain developers for model training and developing the NHS-LLM.
A significant development occurred in April 2024 when Hugging Face, in partnership with Open Life Science AI and the University of Edinburgh, launched the Open Medical-LLM Leaderboard. This platform tracks, ranks, and evaluates LLMs’ performance in answering medical questions based on established medical datasets.
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Product Manager, Revolut
The high costs associated with genomic testing and companion diagnostics.
The lack of expertise among healthcare professionals to interpret test results.
Poorly integrated EHR (Electronic Health Record) systems with a lack of data and privacy standardisation.
The high costs associated with genomic testing and companion diagnostics.
The lack of expertise among healthcare professionals to interpret test results.
Poorly integrated EHR (Electronic Health Record) systems with a lack of data and privacy standardisation.
The high costs associated with genomic testing and companion diagnostics.
The lack of expertise among healthcare professionals to interpret test results.
Poorly integrated EHR (Electronic Health Record) systems with a lack of data and privacy standardisation.
The high costs associated with genomic testing and companion diagnostics.
The lack of expertise among healthcare professionals to interpret test results.
Poorly integrated EHR (Electronic Health Record) systems with a lack of data and privacy standardisation.
đź’ˇ Summary
Mauris velit est, molestie sit amet magna eu, congue lobortis ipsum. Vivamus a tortor quis sapien elementum hendrerit sed id nunc. Phasellus suscipit nulla in massa faucibus sagittis. Morbi mattis hendrerit enim sed porta. Curabitur eleifend tortor at velit efficitur ultrices quis sed neque. Morbi feugiat, odio non molestie lacinia, tellus augue cursus lectus, nec accumsan felis odio at est.
- The cost of bringing a drug to market is $1Bn on average.
- That 60% of those costs can be attributed to the pre-clinical stage ($600M).
- That 50% of pre-clinical stage costs is R&D related ($300M).
- And finally that AI could yield up to a 50% cost-saving in this stage.
