New Horizons: Pioneering Pharmaceutical R&D with Generative AI from lab
to the clinic -- an industry perspective
- URL: http://arxiv.org/abs/2312.12482v1
- Date: Tue, 19 Dec 2023 16:04:07 GMT
- Title: New Horizons: Pioneering Pharmaceutical R&D with Generative AI from lab
to the clinic -- an industry perspective
- Authors: Guy Doron, Sam Genway, Mark Roberts and Sai Jasti
- Abstract summary: The rapid advance of generative AI is reshaping the strategic vision for R&D across industries.
Pharmaceutical R&D will see applications of generative AI deliver value along the entire value chain from early discovery to regulatory approval.
This perspective reviews these challenges and takes a three-horizon approach to explore the generative AI applications already delivering impact.
- Score: 0.1843404256219181
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The rapid advance of generative AI is reshaping the strategic vision for R&D
across industries. The unique challenges of pharmaceutical R&D will see
applications of generative AI deliver value along the entire value chain from
early discovery to regulatory approval. This perspective reviews these
challenges and takes a three-horizon approach to explore the generative AI
applications already delivering impact, the disruptive opportunities which are
just around the corner, and the longer-term transformation which will shape the
future of the industry. Selected applications are reviewed for their potential
to drive increase productivity, accelerate timelines, improve the quality of
research, data and decision making, and support a sustainable future for the
industry. Recommendations are given for Pharma R&D leaders developing a
generative AI strategy today which will lay the groundwork for getting real
value from the technology and safeguarding future growth. Generative AI is
today providing new, efficient routes to accessing and combining organisational
data to drive productivity. Next, this impact will reach clinical development,
enhancing the patient experience, driving operational efficiency, and unlocking
digital innovation to better tackle the future burden of disease. Looking to
the furthest horizon, rapid acquisition of rich multi-omics data, which capture
the 'language of life', in combination with next generation AI technologies
will allow organisations to close the loop around phases of the pipeline
through rapid, automated generation and testing of hypotheses from bench to
bedside. This provides a vision for the future of R&D with sustainability at
the core, with reduced timescales and reduced dependency on resources, while
offering new hope to patients to treat the untreatable and ultimately cure
diseases.
Related papers
- Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report [12.204470166456561]
Generative AI shows significant potential in health economics and outcomes research (HEOR)
Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges.
Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration.
arXiv Detail & Related papers (2024-10-26T15:42:50Z) - Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations [12.73011921253]
This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA)
We explore their applications in four critical areas, synthesis evidence, evidence generation, clinical trials and economic modeling.
Despite their promise, these technologies, while rapidly improving, are still nascent and continued careful evaluation in their applications to HTA is required.
arXiv Detail & Related papers (2024-07-09T09:25:27Z) - Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education.
The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation.
This regulation is likely to put at risk the budding field of open-source generative AI.
arXiv Detail & Related papers (2024-05-14T13:37:36Z) - On the Challenges and Opportunities in Generative AI [135.2754367149689]
We argue that current large-scale generative AI models do not sufficiently address several fundamental issues that hinder their widespread adoption across domains.
In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability.
arXiv Detail & Related papers (2024-02-28T15:19:33Z) - Artificial Intelligence in Sustainable Vertical Farming [0.0]
The paper provides a comprehensive exploration of the role of AI in sustainable vertical farming.
The review synthesizes the current state of AI applications, encompassing machine learning, computer vision, the Internet of Things (IoT), and robotics.
The implications extend beyond efficiency gains, considering economic viability, reduced environmental impact, and increased food security.
arXiv Detail & Related papers (2023-11-17T22:15:41Z) - Exploration with Principles for Diverse AI Supervision [88.61687950039662]
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI.
While this generative AI approach has produced impressive results, it heavily leans on human supervision.
This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation.
We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
arXiv Detail & Related papers (2023-10-13T07:03:39Z) - Causal Reasoning: Charting a Revolutionary Course for Next-Generation
AI-Native Wireless Networks [63.246437631458356]
Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native.
This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning.
We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
arXiv Detail & Related papers (2023-09-23T00:05:39Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - ChatGPT-Like Large-Scale Foundation Models for Prognostics and Health
Management: A Survey and Roadmaps [8.62142522782743]
Prognostics and health management (PHM) technology plays a critical role in industrial production and equipment maintenance.
Large-scale foundation models (LSF-Models) such as ChatGPT and DALLE-E marks the entry of AI into a new era of AI-2.0.
This paper systematically expounds on the key components and latest developments of LSF-Models.
arXiv Detail & Related papers (2023-05-10T21:37:44Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - AI and the future of pharmaceutical research [0.0]
The paper argues that continued innovation in pharmaceutical AI will enable rapid development of safe and effective therapies for previously untreatable diseases.
The industry already reported results such as a 10-fold reduction in drug molecule discovery times.
The paper concludes that the focus on pharmaceutical AI put the industry on a trajectory towards another significant disruption: open data sharing and collaboration.
arXiv Detail & Related papers (2021-06-25T17:56:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.