Lancet

• 1. Dennis JM, Young KG, Cardoso P, Güdemann LM, McGovern AP, Farmer A, Holman RR, Sattar N, McKinley TJ, Pearson ER, Jones AG. A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study. The Lancet. 2025 Mar 1;405(10480):701-14. link 

  2. Chan K, Wahome E, Tsiachristas A, Antonopoulos AS, Patel P, Lyasheva M, Kingham L, West H, Oikonomou EK, Volpe L, Mavrogiannis MC. Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study. The Lancet. 2024 May 29. link 

  3. Taylor C, Dodwell D, McGale P, Hills RK, Berry R, Bradley R, Braybrooke J, Clarke M, Gray R, Holt F, Liu Z. Radiotherapy to regional nodes in early breast cancer: an individual patient data meta-analysis of 14 324 women in 16 trials. The Lancet. 2023 Nov 25;402(10416):1991-2003. link 

  4. Forrest IS, Petrazzini BO, Duffy Á, Park JK, Marquez-Luna C, Jordan DM, Rocheleau G, Cho JH, Rosenson RS, Narula J, Nadkarni GN. Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts. The Lancet. 2023 Jan 21;401(10372):215-25. link 

  5. Ni X, Li Z, Li X, Zhang X, Bai G, Liu Y, Zheng R, Zhang Y, Xu X, Liu Y, Jia C. Socioeconomic inequalities in cancer incidence and access to health services among children and adolescents in China: a cross-sectional study. The Lancet. 2022 Sep 24;400(10357):1020-32. link 

  6. Karwath A, Bunting KV, Gill SK, Tica O, Pendleton S, Aziz F, Barsky AD, Chernbumroong S, Duan J, Mobley AR, Cardoso VR. Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. The Lancet. 2021 Oct 16;398(10309):1427-35. link 

  7. D'Ascenzo F, De Filippo O, Gallone G, Mittone G, Deriu MA, Iannaccone M, Ariza-Solé A, Liebetrau C, Manzano-Fernández S, Quadri G, Kinnaird T. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. The Lancet. 2021 Jan 16;397(10270):199-207. link 

  8. Takahashi K, Serruys PW, Fuster V, Farkouh ME, Spertus JA, Cohen DJ, Park SJ, Park DW, Ahn JM, Kappetein AP, Head SJ. Redevelopment and validation of the SYNTAX score II to individualise decision making between percutaneous and surgical revascularisation in patients with complex coronary artery disease: secondary analysis of the multicentre randomised controlled SYNTAXES trial with external cohort validation. The Lancet. 2020 Oct 31;396(10260):1399-412. link 

  9. Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet. 2019 Sep 7;394(10201):861-7. link 

  10. Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, Mahajan V, Rao P, Warier P. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet. 2018 Dec 1;392(10162):2388-96. link 

  11. Coles CE, Earl H, Anderson BO, Barrios CH, Bienz M, Bliss JM, Cameron DA, Cardoso F, Cui W, Francis PA, Jagsi R. The lancet breast cancer commission. The Lancet. 2024 May 11;403(10439):1895-950. link 

  12. Delaloge S, Khan SA, Wesseling J, Whelan T. Ductal carcinoma in situ of the breast: finding the balance between overtreatment and undertreatment. The Lancet. 2024 May 9. link 

  13. Marrazzo J, Neuzil K, Bertagnolli M. How the US National Institutes of Health is confronting health threats in a changing world. The Lancet. 2024 Dec 7;404(10469):2224-6. link 

  14. Sengupta PP, Kluin J, Lee SP, Oh JK, Smits AI. The future of valvular heart disease assessment and therapy. The Lancet. 2024 Apr 20;403(10436):1590-602. link 

  15. James ND, Tannock I, N'Dow J, Feng F, Gillessen S, Ali SA, Trujillo B, Al-Lazikani B, Attard G, Bray F, Compérat E. The Lancet Commission on prostate cancer: planning for the surge in cases. The Lancet. 2024 Apr 27;403(10437):1683-722. link 

  16. Brugts JJ, Radhoe SP, Clephas PR, Aydin D, Van Gent MW, Szymanski MK, Rienstra M, Van den Heuvel MH, Da Fonseca CA, Linssen GC, Borleffs CJ. Remote haemodynamic monitoring of pulmonary artery pressures in patients with chronic heart failure (MONITOR-HF): a randomised clinical trial. The Lancet. 2023 Jun 24;401(10394):2113-23. link 

  17. Marijon E, Narayanan K, Smith K, Barra S, Basso C, Blom MT, Crotti L, D'Avila A, Deo R, Dumas F, Dzudie A. The Lancet Commission to reduce the global burden of sudden cardiac death: a call for multidisciplinary action. The Lancet. 2023 Sep 9;402(10405):883-936. link 

  18. Adams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. The Lancet. 2023 Feb 4;401(10374):390-408. link 

  19. Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, Gosse TA, Linn ZD, Deng Y, Yin J, Gersh BJ. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. The Lancet. 2022 Oct 8;400(10359):1206-12. link 

  20. Smits FJ, Henry AC, Besselink MG, Busch OR, van Eijck CH, Arntz M, Bollen TL, van Delden OM, van den Heuvel D, van der Leij C, van Lienden KP. Algorithm-based care versus usual care for the early recognition and management of complications after pancreatic resection in the Netherlands: an open-label, nationwide, stepped-wedge cluster-randomised trial. The Lancet. 2022 May 14;399(10338):1867-75. link 

  21. Chen X, Giles J, Yao Y, Yip W, Meng Q, Berkman L, Chen H, Chen X, Feng J, Feng Z, Glinskaya E. The path to healthy ageing in China: a Peking University–Lancet Commission. The Lancet. 2022 Dec 3;400(10367):1967-2006. link 

  22. Wijsenbeek M, Suzuki A, Maher TM. Interstitial lung diseases. The Lancet. 2022 Sep 3;400(10354):769-86. link 

  23. Peterson JC, Bourgin DD, Agrawal M, Reichman D, Griffiths TL. Using large-scale experiments and machine learning to discover theories of human decision-making. Science. 2021 Jun 11;372(6547):1209-14. link 

  24. Fleming KA, Horton S, Wilson ML, Atun R, DeStigter K, Flanigan J, Sayed S, Adam P, Aguilar B, Andronikou S, Boehme C. The Lancet Commission on diagnostics: transforming access to diagnostics. The Lancet. 2021 Nov 27;398(10315):1997-2050. link 

  25. Brouwers S, Sudano I, Kokubo Y, Sulaica EM. Arterial hypertension. The Lancet. 2021 Jul 17;398(10296):249-61. link 

  26. Chandra RA, Keane FK, Voncken FE, Thomas CR. Contemporary radiotherapy: present and future. The Lancet. 2021 Jul 10;398(10295):171-84. link 

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Lancet Digital Health

1. Sun J, Feng T, Wang B, Li F, Han B, Chu M, Gong F, Yi Q, Zhou X, Chen S, Sun X. Leveraging artificial intelligence for predicting spontaneous closure of perimembranous ventricular septal defect in children: a multicentre, retrospective study in China. The Lancet Digital Health. 2025 Jan 1;7(1):e44-53. link 

2. Marra C, Chico T, Alexandrow A, Dixon WG, Briffa N, Rainaldi E, Little MA, Size K, Tsanas A, Franklin JB, Kapur R. Addressing the challenges of integrating digital health technologies to measure patient-centred outcomes in clinical registries. The Lancet Digital Health. 2025 Mar 1;7(3):e225-31. link 

3. Sau A, Sieliwonczyk E, Patlatzoglou K, Pastika L, McGurk KA, Ribeiro AH, Ribeiro AL, Ho JE, Peters NS, Ware JS, Tayal U. Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study. The Lancet Digital Health. 2025 Mar 1;7(3):e184-94. link 

4. Hernström V, Josefsson V, Sartor H, Schmidt D, Larsson AM, Hofvind S, Andersson I, Rosso A, Hagberg O, Lång K. Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study. The Lancet Digital Health. 2025 Feb 3. link 

5. Kehayias CE, Bontempi D, Quirk S, Friesen S, Bredfeldt J, Kosak T, Kearney M, Tishler R, Pashtan I, Huynh MA, Aerts HJ. A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy. The Lancet Digital Health. 2025 Jan 1;7(1):e13-22. link 

6. Cuartero CT, Carnegie AC, Cucunuba ZM, Cori A, Hollis SM, Van Gaalen RD, Baidjoe AY, Spina AF, Lees JA, Cauchemez S, Santos M. From the 100 Day Mission to 100 lines of software development: how to improve early outbreak analytics. The Lancet Digital Health. 2025 Feb 1;7(2):e161-6. link 

7. Hautz WE, Marcin T, Hautz SC, Schauber SK, Krummrey G, Müller M, Sauter TC, Lambrigger C, Schwappach D, Nendaz M, Lindner G. Diagnoses supported by a computerised diagnostic decision support system versus conventional diagnoses in emergency patients (DDX-BRO): a multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial. The Lancet Digital Health. 2025 Feb 1;7(2):e136-44. link 

8. Oikonomou EK, Vaid A, Holste G, Coppi A, McNamara RL, Baloescu C, Krumholz HM, Wang Z, Apakama DJ, Nadkarni GN, Khera R. Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: A multi-center study. medRxiv. 2024 Jun 29. link 

9. Ouyang D, Theurer J, Stein NR, Hughes JW, Elias P, He B, Yuan N, Duffy G, Sandhu RK, Ebinger J, Botting P. Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study. The Lancet Digital Health. 2024 Jan 1;6(1):e70-8. link 

10. Cid YD, Macpherson M, Gervais-Andre L, Zhu Y, Franco G, Santeramo R, Lim C, Selby I, Muthuswamy K, Amlani A, Hopewell H. Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study. The Lancet Digital Health. 2024 Jan 1;6(1):e44-57. link 

11. Jiang X, Hoffmeister M, Brenner H, Muti HS, Yuan T, Foersch S, West NP, Brobeil A, Jonnagaddala J, Hawkins N, Ward RL. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study. The Lancet Digital Health. 2024 Jan 1;6(1):e33-43. link 

12. Greenwood SA, Young HM, Briggs J, Castle EM, Walklin C, Haggis L, Balkin C, Asgari E, Bhandari S, Burton JO, Billany RE. Evaluating the effect of a digital health intervention to enhance physical activity in people with chronic kidney disease (Kidney BEAM): a multicentre, randomised controlled trial in the UK. The Lancet Digital Health. 2024 Jan 1;6(1):e23-32. link 

13. Giddings R, Joseph A, Callender T, Janes SM, Van der Schaar M, Sheringham J, Navani N. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. The Lancet Digital Health. 2024 Feb 1;6(2):e131-44. link 

14. Häggström I, Leithner D, Alvén J, Campanella G, Abusamra M, Zhang H, Chhabra S, Beer L, Haug A, Salles G, Raderer M. Deep learning for [18F] fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis. The Lancet Digital Health. 2024 Feb 1;6(2):e114-25. link 

15. Edgar R, Scholte NT, Ebrahimkheil K, Brouwer MA, Beukema RJ, Mafi-Rad M, Vernooy K, Yap SC, Ronner E, van Mieghem N, Boersma E. Automated cardiac arrest detection using a photoplethysmography wristband: algorithm development and validation in patients with induced circulatory arrest in the DETECT-1 study. The Lancet Digital Health. 2024 Mar 1;6(3):e201-10. link 

16. Villamor MA, Alonso-Sanz M, López-Izquierdo R, Benito JF, del Pozo Vegas C, Torres SL, Soriano JB, Martín-Conty JL, Sanz-García A, Martín-Rodríguez F. Comparison of eight prehospital early warning scores in life-threatening acute respiratory distress: a prospective, observational, multicentre, ambulance-based, external validation study. The Lancet Digital Health. 2024 Mar 1;6(3):e166-75. link 

17. Kraljevic Z, Bean D, Shek A, Bendayan R, Hemingway H, Yeung JA, Deng A, Balston A, Ross J, Idowu E, Teo JT. Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study. The Lancet Digital Health. 2024 Apr 1;6(4):e281-90. link 

18. Hu B, Shi Z, Lu L, Miao Z, Wang H, Zhou Z, Zhang F, Wang R, Luo X, Xu F, Li S. A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study. The Lancet Digital Health. 2024 Apr 1;6(4):e261-71. link 

19. Spielvogel CP, Haberl D, Mascherbauer K, Ning J, Kluge K, Traub-Weidinger T, Davies RH, Pierce I, Patel K, Nakuz T, Göllner A. Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicentre, cross-tracer development and validation study. The Lancet Digital Health. 2024 Apr 1;6(4):e251-60. link 

20. Wei Q, Mease PJ, Chiorean M, Iles-Shih L, Matos WF, Baumgartner A, Molani S, Hwang YM, Belhu B, Ralevski A, Hadlock J. Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system. The Lancet Digital Health. 2024 May 1;6(5):e309-22. link 

21. Lehmann DH, Gomes B, Vetter N, Braun O, Amr A, Hilbel T, Müller J, Köthe U, Reich C, Kayvanpour E, Sedaghat-Hamedani F. Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data. The Lancet Digital Health. 2024 Jun 1;6(6):e407-17. link 

22. Jung AW, Holm PC, Gaurav K, Hjaltelin JX, Placido D, Mortensen LH, Birney E, Gerstung M. Multi-cancer risk stratification based on national health data: a retrospective modelling and validation study. The Lancet Digital Health. 2024 Jun 1;6(6):e396-406. link 

23. Daniel R, Jones H, Gregory JW, Shetty A, Francis N, Paranjothy S, Townson J. Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm. The Lancet Digital Health. 2024 Jun 1;6(6):e386-95. link 

24. Lopez-Ayala P, Boeddinghaus J, Nestelberger T, Koechlin L, Zimmermann T, Bima P, Glaeser J, Spagnuolo CC, Champetier A, Miro O, Martin-Sanchez FJ. External validation of the myocardial-ischaemic-injury-index machine learning algorithm for the early diagnosis of myocardial infarction: a multicentre cohort study. The Lancet Digital Health. 2024 Jul 1;6(7):e480-8. link 

25. Sau A, Pastika L, Sieliwonczyk E, Patlatzoglou K, Ribeiro AH, McGurk KA, Zeidaabadi B, Zhang H, Macierzanka K, Mandic D, Sabino E. Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study. The Lancet Digital Health. 2024 Nov 1;6(11):e791-802. link 

26. Fisches ZV, Ball M, Mukama T, Štih V, Payne NR, Hickman SE, Gilbert FJ, Bunk S, Leibig C. Strategies for integrating artificial intelligence into mammography screening programmes: a retrospective simulation analysis. The Lancet Digital Health. 2024 Nov 1;6(11):e803-14. link 

27. Ma J, Zhang Y, Gu S, Ge C, Mae S, Young A, Zhu C, Yang X, Meng K, Huang Z, Zhang F. Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge. The Lancet Digital Health. 2024 Nov 1;6(11):e815-26. link 

28. Alderman JE, Charalambides M, Sachdeva G, Laws E, Palmer J, Lee E, Menon V, Malik Q, Vadera S, Calvert M, Ghassemi M. Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development—a systematic review. The Lancet Digital Health. 2024 Nov 1;6(11):e827-47. link 

29. Toprak B, Solleder H, Di Carluccio E, Greenslade JH, Parsonage WA, Schulz K, Cullen L, Apple FS, Ziegler A, Blankenberg S, Stephensen L. Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: A retrospective study. The Lancet Digital Health. 2024 Oct 1;6(10):e729-38. link 

30. Sengupta PP, Dey D, Davies RH, Duchateau N, Yanamala N. Challenges for augmenting intelligence in cardiac imaging. The Lancet Digital Health. 2024 Oct 1;6(10):e739-48. link 

31. Mihan A, Pandey A, Van Spall HG. Mitigating the risk of artificial intelligence bias in cardiovascular care. The Lancet Digital Health. 2024 Oct 1;6(10):e749-54. link 

32. Myhre PL, Tromp J, Ouwerkerk W, Ting DS, Docherty KF, Gibson CM, Lam CS. Digital tools in heart failure: addressing unmet needs. The Lancet Digital Health. 2024 Oct 1;6(10):e755-66. link 

33. Quer G, Topol EJ. The potential for large language models to transform cardiovascular medicine. The Lancet Digital Health. 2024 Oct 1;6(10):e767-71. link 

34. Qin ZZ, Van der Walt M, Moyo S, Ismail F, Maribe P, Denkinger CM, Zaidi S, Barrett R, Mvusi L, Mkhondo N, Zuma K. Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software. The Lancet Digital Health. 2024 Sep 1;6(9):e605-13. link 

35. Feng X, Goodley P, Alcala K, Guida F, Kaaks R, Vermeulen R, Downward GS, Bonet C, Colorado-Yohar SM, Albanes D, Weinstein SJ. Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis. The Lancet Digital Health. 2024 Sep 1;6(9):e614-24. link 

36. Ueda D, Matsumoto T, Yamamoto A, Walston SL, Mitsuyama Y, Takita H, Asai K, Watanabe T, Abo K, Kimura T, Fukumoto S. A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan. The Lancet Digital Health. 2024 Aug 1;6(8):e580-8. link 

37. Levine DM, Tuwani R, Kompa B, Varma A, Finlayson SG, Mehrotra A, Beam A. The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study. The Lancet Digital Health. 2024 Aug 1;6(8):e555-61. link 

38. Smith LA, Oakden-Rayner L, Bird A, Zeng M, To MS, Mukherjee S, Palmer LJ. Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. The Lancet Digital Health. 2023 Dec 1;5(12):e872-81. link 

39. Beaulieu-Jones BK, Villamar MF, Scordis P, Bartmann AP, Ali W, Wissel BD, Alsentzer E, de Jong J, Patra A, Kohane I. Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study. The Lancet Digital Health. 2023 Dec 1;5(12):e882-94. link 

40. Wang Z, Bian H, Li J, Xu J, Fan H, Wu X, Cao Y, Guo B, Xu X, Wang H, Zhang L. Detection and subtyping of hepatic echinococcosis from plain CT images with deep learning: a retrospective, multicentre study. The Lancet Digital Health. 2023 Nov 1;5(11):e754-62. link 

41. Reinier K, Dizon B, Chugh H, Bhanji Z, Seifer M, Sargsyan A, Uy-Evanado A, Norby FL, Nakamura K, Hadduck K, Shepherd D. Warning symptoms associated with imminent sudden cardiac arrest: a population-based case-control study with external validation. The Lancet Digital Health. 2023 Nov 1;5(11):e763-73. link 

42. He J, Wang B, Tao J, Liu Q, Peng M, Xiong S, Li J, Cheng B, Li C, Jiang S, Qiu X. Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study. The Lancet Digital Health. 2023 Oct 1;5(10):e647-56. link 

43. Saux P, Bauvin P, Raverdy V, Teigny J, Verkindt H, Soumphonphakdy T, Debert M, Jacobs A, Jacobs D, Monpellier V, Lee PC. Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study. The Lancet Digital Health. 2023 Oct 1;5(10):e692-702. link 

44. Dembrower K, Crippa A, Colón E, Eklund M, Strand F. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. The Lancet Digital Health. 2023 Oct 1;5(10):e703-11. link 

45. Clift AK, Collins GS, Lord S, Petrou S, Dodwell D, Brady M, Hippisley-Cox J. Predicting 10-year breast cancer mortality risk in the general female population in England: a model development and validation study. The Lancet Digital Health. 2023 Sep 1;5(9):e571-81. link 

46. Muehlematter UJ, Bluethgen C, Vokinger KN. FDA-cleared artificial intelligence and machine learning-based medical devices and their 510 (k) predicate networks. The Lancet Digital Health. 2023 Sep 1;5(9):e618-26. link 

47. Yang Y, Cairang Y, Jiang TA, Zhou J, Zhang L, Qi B, Ma S, Tang L, Xu D, Bu L, Bu R. Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study. The Lancet Digital Health. 2023 Aug 1;5(8):e503-14. link 

48. Ueda D, Matsumoto T, Ehara S, Yamamoto A, Walston SL, Ito A, Shimono T, Shiba M, Takeshita T, Fukuda D, Miki Y. Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study. The Lancet Digital Health. 2023 Aug 1;5(8):e525-33. link 

49. Saad MB, Hong L, Aminu M, Vokes NI, Chen P, Salehjahromi M, Qin K, Sujit SJ, Lu X, Young E, Al-Tashi Q. Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study. The Lancet Digital Health. 2023 Jul 1;5(7):e404-20. link 

50. Kwong JC, Khondker A, Meng E, Taylor N, Kuk C, Perlis N, Kulkarni GS, Hamilton RJ, Fleshner NE, Finelli A, van der Kwast TH. Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study. The Lancet Digital Health. 2023 Jul 1;5(7):e435-45. link 

51. Williams GJ, Al-Baraikan A, Rademakers FE, Ciravegna F, van de Vosse FN, Lawrie A, Rothman A, Ashley EA, Wilkins MR, Lawford PV, Omholt SW. Wearable technology and the cardiovascular system: the future of patient assessment. The Lancet Digital Health. 2023 Jul 1;5(7):e467-76. link 

52. Wagner SK, Liefers B, Radia M, Zhang G, Struyven R, Faes L, Than J, Balal S, Hennings C, Kilduff C, Pooprasert P. Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study. The Lancet Digital Health. 2023 Jun 1;5(6):e340-9. link 

53. Jiang R, Noble S, Sui J, Yoo K, Rosenblatt M, Horien C, Qi S, Liang Q, Sun H, Calhoun VD, Scheinost D. Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank. The Lancet Digital Health. 2023 Jun 1;5(6):e350-9. link 

54. Kann BH, Likitlersuang J, Bontempi D, Ye Z, Aneja S, Bakst R, Kelly HR, Juliano AF, Payabvash S, Guenette JP, Uppaluri R. Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial. The Lancet Digital Health. 2023 Jun 1;5(6):e360-9. link 

55. Banerjee A, Dashtban A, Chen S, Pasea L, Thygesen JH, Fatemifar G, Tyl B, Dyszynski T, Asselbergs FW, Lund LH, Lumbers T. Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study. The Lancet Digital Health. 2023 Jun 1;5(6):e370-9. link 

56. Babenko B, Traynis I, Chen C, Singh P, Uddin A, Cuadros J, Daskivich LP, Maa AY, Kim R, Kang EY, Matias Y. A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study. The Lancet Digital Health. 2023 May 1;5(5):e257-64. link 

57. Alberto IR, Alberto NR, Ghosh AK, Jain B, Jayakumar S, Martinez-Martin N, McCague N, Moukheiber D, Moukheiber L, Moukheiber M, Moukheiber S. The impact of commercial health datasets on medical research and health-care algorithms. The Lancet Digital Health. 2023 May 1;5(5):e288-94. link

58. Moschonis G, Siopis G, Jung J, Eweka E, Willems R, Kwasnicka D, Asare BY, Kodithuwakku V, Verhaeghe N, Vedanthan R, Annemans L. Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials. The Lancet Digital Health. 2023 Mar 1;5(3):e125-43. link 

59. Siopis G, Moschonis G, Eweka E, Jung J, Kwasnicka D, Asare BY, Kodithuwakku V, Willems R, Verhaeghe N, Annemans L, Vedanthan R. Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with hypertension: a systematic review and meta-analysis of randomised controlled trials. The Lancet Digital Health. 2023 Mar 1;5(3):e144-59. link 

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