The Lancet Digital Health in conversation with
Rupa Sarkar, Editor-in-Chief, Diana Samuel, Deputy Editor, Lucy Dunbar, Senior Editor, and Gustavo Monnerat, Senior Editor at The Lancet Digital Health, in conversation with the journal’s authors, explore their latest research and its impact on people’s health, healthcare, and health policy.
A monthly audio companion to this open access journal, this podcast covers a broad range of topics, from using machine learning to predict mortality in prostate cancer and the need for feminist intersectionality in digital health, to how algorithms can predict a patient's race from medical data, and more.
- Update frequency
- every 48 days
- Average duration
- 17 minutes
- Episodes
- 31
- Years Active
- 2019 - 2025
Hugo Aerts and Ray Mak on FaceAge
Hugo Aerts and Ray Mak from Mass General Brigham join Lucy Dunbar to discuss FaceAge, a deep learning tool that estimates biological age from simple face photographs, and its implications in aiding c…
Mohamed Omar on pathology and generative AI
Mohamed Omar joins The Lancet Digital Health to discuss pathology and generative AI from a Digital Health Perspective. We explore expert journeys, AI applications in diagnostics, GPT-4's role, integr…
Judith Bonnes on detecting cardiac arrest using wearable technology
Developing and validating an algorithm for automated circulatory arrest detection with wrist-derived photoplethysmography with Judith Bonnes from the Department of Cardiology of the Radboud Universit…
Andrew Soltan on federated learning systems
Dr Andrew Soltan joins Dr Lucy Dunbar to discuss the development, testing and deployment of a federated learning system across four UK hospital groups.
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Mamatha Bhat on deep learning for predicting liver graft fibrosis
Dr Mamatha Bhat joins Dr Lucy Dunbar to discuss the development of deep learning algorithms in predicting risk of significant fibrosis after liver transplantation.
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Xiao Liu on AI-based clinical research studies
Xiao Liu joins Diana Samuel to discuss how to improve the reporting of artificial intelligence-based clinical research studies and the representativeness of health datasets.
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Ashleigh Myall on predicting hospital-onset COVID-19 infections
Ashleigh Myall joins Diana Samuel to discuss a new machine-learning framework that integrates dynamic patient-contact networks with patient clinical variables and contextual hospital variables to pre…
Reading race
Judy Gichoya, Leo Celi and Laleh Seyyed-Kalantari join Rupa Sarkar to discuss how algorithms can predict a patient's race from medical data.
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Caroline Figueroa on the need for feminist intersectionality in digital health
Dr Caroline Figueroa and Dr Rupa Sarkar talk about the social and economic fallout from COVID-19 which has further exacerbated gender inequities and the need for a feminist outlook in digital health.…
Mihaela van der Schaar and Vincent J Gnanapragasam on predicting mortality in prostate cancer
Mihaela van der Schaar and Vincent J Gnanapragasam join Diana Samuel to discuss a new machine learning-based prognostic model for prediction of 10-year mortality from non-metastatic prostate cancer.
R…
Deepti Gurdasani on health data, AI, and COVID-19
Deepti Gurdasani joins Rupa Sarkar to discuss bias and inequalities in health data and artificial intelligence and the impact of COVID-19.
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Vence Bonham on diversity and impact in genomic research
Vence Bonham joins Diana Samuel to discuss the impact of genomics and precision medicine on health disparities, and the need for greater diversity in genomic research studies.
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Maimuna S Majumder on COVID-19 misinformation online
Maimuna S Majumder joins Diana Samuel to discuss how misinformation on COVID-19 can influence the public’s online search interests, and the potential impact this can have on their health.
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Sara Gerke and Timo Minssen on AI in healthcare
Sara Gerke and Timo Minssen join Diana Samuel to discuss the European Commission’s white paper on artificial intelligence (published in February 2020), and the challenges of implementing the outlined…
Identifying and measuring brain lesions in patients with traumatic brain injury
Virginia Newcombe and Miguel Monteiro join Rupa Sarkar to discuss their work on a deep learning algorithm to quantify and distinguish between brain lesion types in head CT images of patients with tra…
The Lancet Digital Health turns one
To celebrate the journal’s anniversary, the Editors and International Advisory Board members of The Lancet Digital Health reflect on the major digital advances transforming medicine and healthcare in…
A real-time dashboard of clinical trials for COVID-19
Dr. Louis Dron and Dr Samuel talk about an online dashboard that collates all clinical trials investigating treatments for COVID-19, and discuss the broader implications of its use.
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Opportunistic value of fully automated CT-based biomarkers
Prof Perry Pickhardt and Diana Samuel talk about the prognostic abilities of a panel of automated CT-based biomarkers for predicting future risk of serious adverse events.
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Predicting the added benefit of adjuvant chemotherapy
Prof Anant Madabhushi and Dr Samuel talk about the utility of a radiomic risk score applied to CT scans of patients with early stage non-small cell lung carcinoma.
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Using Fitbit data to predict flu outbreaks
Dr Jennifer Radin and Dr Rupa Sarkar talk about using heart rate and sleep data from Fitbit devices to predict influenza-like illness activity in the US.
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