The impact of artificial intelligence on biomarker discovery
Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure. Here, we provide an overview of the AI toolkit available for biomarker discovery, and we discuss exciting examples of AI-powered biomarkers across therapeutic areas. Finally, we address the challenges ahead of us to ensure that these technologies reach patients and users globally and unlock a new era of fast innovation for precision medicine.
Authors
- Benjamin B. Sun (ORCID: https://orcid.org/0000-0001-6347-2281)
- Paul G. O’Reilly (ORCID: https://orcid.org/0000-0002-3437-6167)
- Constantin Cezar Petrescu (ORCID: https://orcid.org/0009-0003-4359-2894)
- Jack Monahan (ORCID: https://orcid.org/0000-0002-0635-0015)
- Daniel E. Martin‐Herranz (ORCID: https://orcid.org/0000-0002-2285-3317)
- Mustafa Al Lail (ORCID: https://orcid.org/0009-0000-0326-6363)
- Thomas M. Stubbs (ORCID: https://orcid.org/0000-0002-2990-7381)
- HAMMAD JAVAID (ORCID: https://orcid.org/0000-0003-1719-0210)
- Dimitrios Vitsios (ORCID: https://orcid.org/0000-0002-8939-5445)
- Mahmoud Khasawneh (ORCID: https://orcid.org/0000-0001-7849-7932)
- Deepak Ganta (ORCID: https://orcid.org/0000-0001-8486-5463)
- Manik Garg (ORCID: https://orcid.org/0000-0003-0453-2058)
- Lisa J. Schmunk (ORCID: https://orcid.org/0009-0009-0879-462X)
- Leona McGirr
Institutions
- Texas A&M International University (US)
- Bristol-Myers Squibb (Germany) (DE)
- Trinity College (CA)
- Queens University (BD)
- AstraZeneca (Australia) (AU)
- Bristol-Myers Squibb (Sweden) (SE)
- Analytics Engines (United Kingdom) (GB)
- Biotronics 3D (United Kingdom) (GB)
Publication Details
- Journal
- Emerging Topics in Life Sciences
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1042/etls20243003
- Citations
- 4
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 3.22