The Lancet : Digital health

ISSNs: 2589-7500

Additional searchable ISSN (electronic): 2589-7500

Elsevier, Oxford [u.a.], United Kingdom

Scopus rating (2024): CiteScore 38.4 SJR 6.838 SNIP 5.393

Indexed in DOAJ

Journal

Titles	The Lancet : Digital health(2019 → …) The Lancet Digital Health
Additional searchable titles	Lancet Digit Health
ISSNs	2589-7500
Additional searchable ISSN (electronic)	2589-7500
Publisher	Elsevier, Oxford [u.a.]
Country/Territory	United Kingdom
ZDB-ID	2972368-1

Keywords

ASJC Scopus subject areas

Library keywords

610 Medicine and health

Assessing genotype−phenotype correlations in colorectal cancer with deep learning: a multicentre cohort study

Gustav, M., van Treeck, M., Reitsam, N. G., Carrero, Z. I., Loeffler, C. M. L., Rabasco Meneghetti, A. & Märkl, B. & 15 others, Boardman, L. A., French, A. J., Goode, E. L., Gsur, A., Brezina, S., Gunter, M. J., Murphy, N., Hönscheid, P., Sperling, C., Foersch, S., Steinfelder, R., Harrison, T., Peters, U., Phipps, A. & Kather, J. N., 18 Aug 2025, (E-pub ahead of print) In: The Lancet Digital Health. 7, 8, 100891.

Research output: Contribution to journal › Research article › Contributed › peer-review

A future role for health applications of large language models depends on regulators enforcing safety standards

Freyer, O., Wiest, I. C., Kather, J. N. & Gilbert, S., Sept 2024, In: The Lancet Digital Health. 6, 9, p. e662-e672

Research output: Contribution to journal › Review article › Contributed › peer-review

A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts

NIHR Global Health Research Unit on Global Surgery & STARSurg Collaborative, Jul 2024, In: The Lancet : Digital health. 6, 7, p. e507-e519

Research output: Contribution to journal › Research article › Contributed › peer-review

Is predicting metastatic phaeochromocytoma and paraganglioma still effective without methoxytyramine? – Authors' reply

Pamporaki, C., Filippatos, A. & Eisenhofer, G., Mar 2024, In: The Lancet Digital Health. 6, 3, p. e155

Research output: Contribution to journal › Letter › Contributed › peer-review

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

Jiang, X., Hoffmeister, M., Brenner, H., Muti, H. S., Yuan, T., Foersch, S. & West, N. P. & 12 others, Brobeil, A., Jonnagaddala, J., Hawkins, N., Ward, R. L., Brinker, T. J., Saldanha, O. L., Ke, J., Müller, W., Grabsch, H. I., Quirke, P., Truhn, D. & Kather, J. N., Jan 2024, In: The Lancet Digital Health. 6, 1, p. e33-e43

Research output: Contribution to journal › Research article › Contributed › peer-review

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Research Portal of the TU Dresden

The Lancet : Digital health

Keywords

ASJC Scopus subject areas

Library keywords

Related content

Assessing genotype−phenotype correlations in colorectal cancer with deep learning: a multicentre cohort study

A future role for health applications of large language models depends on regulators enforcing safety standards

A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts

Is predicting metastatic phaeochromocytoma and paraganglioma still effective without methoxytyramine? – Authors' reply

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study