Digital twins and Big AI: the future of truly individualised healthcare
The integration of physics-based digital twins with data-driven artificial intelligence—termed “Big AI”—can advance truly personalised medicine. While digital twins offer individual ‘healthcasts,’ accuracy and interpretability, and AI delivers speed and flexibility, each has limitations. Big AI comb...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01874-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849761123970056192 |
|---|---|
| author | Peter Coveney Roger Highfield Eric Stahlberg Mariano Vázquez |
| author_facet | Peter Coveney Roger Highfield Eric Stahlberg Mariano Vázquez |
| author_sort | Peter Coveney |
| collection | DOAJ |
| description | The integration of physics-based digital twins with data-driven artificial intelligence—termed “Big AI”—can advance truly personalised medicine. While digital twins offer individual ‘healthcasts,’ accuracy and interpretability, and AI delivers speed and flexibility, each has limitations. Big AI combines their strengths, enabling faster, more reliable and individualised predictions, with applications from diagnostics to drug discovery. Above all, Big AI restores mechanistic insights to AI and complies with the scientific method. |
| format | Article |
| id | doaj-art-9518624bcaf5427fa475a38ff3e1d70f |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-9518624bcaf5427fa475a38ff3e1d70f2025-08-20T03:06:08ZengNature Portfolionpj Digital Medicine2398-63522025-08-01811310.1038/s41746-025-01874-xDigital twins and Big AI: the future of truly individualised healthcarePeter Coveney0Roger Highfield1Eric Stahlberg2Mariano Vázquez3Centre for Computational Science, Department of Chemistry, University College LondonScience Museum, Exhibition RoadThe Institute of Data Science in Oncology, The University of Texas MD Anderson Cancer CenterELEM Biotech & Barcelona Supercomputing Center ESThe integration of physics-based digital twins with data-driven artificial intelligence—termed “Big AI”—can advance truly personalised medicine. While digital twins offer individual ‘healthcasts,’ accuracy and interpretability, and AI delivers speed and flexibility, each has limitations. Big AI combines their strengths, enabling faster, more reliable and individualised predictions, with applications from diagnostics to drug discovery. Above all, Big AI restores mechanistic insights to AI and complies with the scientific method.https://doi.org/10.1038/s41746-025-01874-x |
| spellingShingle | Peter Coveney Roger Highfield Eric Stahlberg Mariano Vázquez Digital twins and Big AI: the future of truly individualised healthcare npj Digital Medicine |
| title | Digital twins and Big AI: the future of truly individualised healthcare |
| title_full | Digital twins and Big AI: the future of truly individualised healthcare |
| title_fullStr | Digital twins and Big AI: the future of truly individualised healthcare |
| title_full_unstemmed | Digital twins and Big AI: the future of truly individualised healthcare |
| title_short | Digital twins and Big AI: the future of truly individualised healthcare |
| title_sort | digital twins and big ai the future of truly individualised healthcare |
| url | https://doi.org/10.1038/s41746-025-01874-x |
| work_keys_str_mv | AT petercoveney digitaltwinsandbigaithefutureoftrulyindividualisedhealthcare AT rogerhighfield digitaltwinsandbigaithefutureoftrulyindividualisedhealthcare AT ericstahlberg digitaltwinsandbigaithefutureoftrulyindividualisedhealthcare AT marianovazquez digitaltwinsandbigaithefutureoftrulyindividualisedhealthcare |