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Blockchain for increased trust in observational studies.

Mehdi Benchoufi, Philippe Ravaud, Jordan Tarlet,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e762]

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Bias and privacy in AI's cough-based COVID-19 recognition.

Humberto Perez-Espinosa, Eva Timonet-Andreu, Javier Andreu-Perez,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e760]

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It's not easy being green.

The Lancet Digital Health,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e751]

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Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review.

Ania Syrowatka, Wenyu Song, Mary G Amato, Dinah Foer, Heba Edrees, Zoe Co, Masha Kuznetsova, Sevan Dulgarian, Diane L Seger, Aurélien Simona, Paul A Bain, Gretchen Purcell Jackson, Kyu Rhee, David W Bates,

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, :]

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Risk of acute respiratory infection and acute cardiovascular events following acute respiratory infection among adults with increased cardiovascular risk in England between 2008 and 2018: a retrospective, population-based cohort study.

Jennifer A Davidson, Amitava Banerjee, Liam Smeeth, Helen I McDonald, Daniel Grint, Emily Herrett, Harriet Forbes, Richard Pebody, Charlotte Warren-Gash,

<h4>Background</h4>Although acute respiratory infections can lead to cardiovascular complications, the effect of underlying cardiovascular risk on the incidence of acute respiratory infections and cardiovascular complications following acute respiratory infection in individuals without established cardiovascular disease is unknown. We aimed to investigate whether cardiovascular risk is associated with increased risk of ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e773-e783]

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Bias and privacy in AI's cough-based COVID-19 recognition - Authors' reply.

Harry Coppock, Lyn Jones, Ivan Kiskin, Björn Schuller,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e761]

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Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study.

Jesper Kers, Roman D Bülow, Barbara M Klinkhammer, Gerben E Breimer, Francesco Fontana, Adeyemi Adefidipe Abiola, Rianne Hofstraat, Garry L Corthals, Hessel Peters-Sengers, Sonja Djudjaj, Saskia von Stillfried, David L Hölscher, Tobias T Pieters, Arjan D van Zuilen, Frederike J Bemelman, Azam S Nurmohamed, Maarten Naesens, Joris J T H Roelofs, Sandrine Florquin, Jürgen Floege, Tri Q Nguyen, Jakob N Kather, Peter Boor,

<h4>Background</h4>Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, :]

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Characteristics of publicly available skin cancer image datasets: a systematic review.

David Wen, Saad M Khan, Antonio Ji Xu, Hussein Ibrahim, Luke Smith, Jose Caballero, Luis Zepeda, Carlos de Blas Perez, Alastair K Denniston, Xiaoxuan Liu, Rubeta N Matin,

Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and their respective content is currently unclear. This systematic review aimed to identify and evaluate all publicly available skin image datasets used for skin cancer diagnosis by ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, :]

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Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study.

Nishant Kishore, Aimee R Taylor, Pierre E Jacob, Navin Vembar, Ted Cohen, Caroline O Buckee, Nicolas A Menzies,

<h4>Background</h4>In early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, :]

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X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study.

Konstantin Willer, Alexander A Fingerle, Wolfgang Noichl, Fabio De Marco, Manuela Frank, Theresa Urban, Rafael Schick, Alex Gustschin, Bernhard Gleich, Julia Herzen, Thomas Koehler, Andre Yaroshenko, Thomas Pralow, Gregor S Zimmermann, Bernhard Renger, Andreas P Sauter, Daniela Pfeiffer, Marcus R Makowski, Ernst J Rummeny, Philippe A Grenier, Franz Pfeiffer,

<h4>Background</h4>Although advanced medical imaging technologies give detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection of respiratory diseases and follow-ups is still lacking. The novel method of x-ray dark-field chest imaging might fill this gap but has not yet been studied in living humans. Enabling the assessment ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(11):e733-e744]

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Another step (count) towards leveraging mobile health data for clinical prediction.

Andrew R Murphy, Carissa A Low,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(11):e687-e688]

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Forging the tools for a computer-aided workflow in transplant pathology.

Callum Arthurs, Candice Roufosse,

Lancet Digit Health (The Lancet. Digital health)
[2021, :]

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Correction to Lancet Digit Health 2021; 3: e697-706.

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(11):e696]

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Correction to Lancet Digit Health 2021; 3: e507-16.

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(11):e696]

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Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study.

Marc Raynaud, Olivier Aubert, Gillian Divard, Peter P Reese, Nassim Kamar, Daniel Yoo, Chen-Shan Chin, Élodie Bailly, Matthias Buchler, Marc Ladrière, Moglie Le Quintrec, Michel Delahousse, Ivana Juric, Nikolina Basic-Jukic, Marta Crespo, Helio Tedesco Silva, Kamilla Linhares, Maria Cristina Ribeiro de Castro, Gervasio Soler Pujol, Jean-Philippe Empana, Camilo Ulloa, Enver Akalin, Georg Böhmig, Edmund Huang, Mark D Stegall, Andrew J Bentall, Robert A Montgomery, Stanley C Jordan, Rainer Oberbauer, Dorry L Segev, John J Friedewald, Xavier Jouven, Christophe Legendre, Carmen Lefaucheur, Alexandre Loupy,

<h4>Background</h4>Kidney allograft failure is a common cause of end-stage renal disease. We aimed to develop a dynamic artificial intelligence approach to enhance risk stratification for kidney transplant recipients by generating continuously refined predictions of survival using updates of clinical data.<h4>Methods</h4>In this observational study, we used data from adult recipients of ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e795-e805]

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The false hope of current approaches to explainable artificial intelligence in health care.

Marzyeh Ghassemi, Luke Oakden-Rayner, Andrew L Beam,

The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has been argued that explainable AI will engender trust with the health-care workforce, provide transparency into the AI decision making process, and ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(11):e745-e750]

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X-ray dark field imaging: a tool for early diagnosis of emphysema in chronic obstructive pulmonary disease?

Sundaresh Ram, MeiLan K Han,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(11):e691-e692]

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Towards computationally efficient prediction of molecular signatures from routine histology images.

Maxime W Lafarge, Viktor H Koelzer,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e752-e753]

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Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational study.

Jessica R Golbus, Nicole A Pescatore, Brahmajee K Nallamothu, Nirav Shah, Sachin Kheterpal,

<h4>Background</h4>Wearable technology has rapidly entered consumer markets and has health-care potential; however, wearable device data for diverse populations are scarce. We therefore aimed to describe and compare key wearable signals (ie, heart rate, step count, and home blood pressure measurements) across age, sex, race, ethnicity, and clinical phenotypes.<h4>Methods</h4>In the Michigan ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(11):e707-e715]

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Digital technologies: a new determinant of health.

The Lancet Digital Health,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(11):e684]

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Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.

Mohsin Bilal, Shan E Ahmed Raza, Ayesha Azam, Simon Graham, Mohammad Ilyas, Ian A Cree, David Snead, Fayyaz Minhas, Nasir M Rajpoot,

<h4>Background</h4>Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e763-e772]

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Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.

Chandrakanth Jayachandran Preetha, Hagen Meredig, Gianluca Brugnara, Mustafa A Mahmutoglu, Martha Foltyn, Fabian Isensee, Tobias Kessler, Irada Pflüger, Marianne Schell, Ulf Neuberger, Jens Petersen, Antje Wick, Sabine Heiland, Jürgen Debus, Michael Platten, Ahmed Idbaih, Alba A Brandes, Frank Winkler, Martin J van den Bent, Burt Nabors, Roger Stupp, Klaus H Maier-Hein, Thierry Gorlia, Jörg-Christian Tonn, Michael Weller, Wolfgang Wick, Martin Bendszus, Philipp Vollmuth,

<h4>Background</h4>Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e784-e794]

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Seeing more with less: virtual gadolinium-enhanced glioma imaging.

Alexandros Ferles, Frederik Barkhof,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e754-e755]

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Are digital devices a new risk factor for myopia?

James Loughman, Daniel Ian Flitcroft,

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e756-e757]

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Blockchain applications in health care for COVID-19 and beyond: a systematic review.

Wei Yan Ng, Tien-En Tan, Prasanth V H Movva, Andrew Hao Sen Fang, Khung-Keong Yeo, Dean Ho, Fuji Shyy San Foo, Zhe Xiao, Kai Sun, Tien Yin Wong, Alex Tiong-Heng Sia, Daniel Shu Wei Ting,

The COVID-19 pandemic has had a substantial and global impact on health care, and has greatly accelerated the adoption of digital technology. One of these emerging digital technologies, blockchain, has unique characteristics (eg, immutability, decentralisation, and transparency) that can be useful in multiple domains (eg, management of electronic medical records ... Read more >>

Lancet Digit Health (The Lancet. Digital health)
[2021, 3(12):e819-e829]

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