Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency by Cutillo, Christine M., Sharma, Karlie R., Foschini, Luca, Kundu, Shinjini, Mackintosh, Maxine, Mandl, Kenneth D.

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Developing and adopting safe and effective digital biomarkers to improve patient outcomes by Coravos, Andrea, Khozin, Sean, Mandl, Kenneth D.

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Photoplethysmography based atrial fibrillation detection: a review by Pereira, Tania, Tran, Nate, Gadhoumi, Kais, Pelter, Michele M., Do, Duc H., Lee, Randall J., Colorado, Rene, Meisel, Karl, Hu, Xiao

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The need for a system view to regulate artificial intelligence/machine learning-based software as medical device by Gerke, Sara, Babic, Boris, Evgeniou, Theodoros, Cohen, I. Glenn

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The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field by Coorey, Genevieve, Figtree, Gemma A., Fletcher, David F., Snelson, Victoria J., Vernon, Stephen Thomas, Winlaw, David, Grieve, Stuart M., McEwan, Alistair, Yang, Jean Yee Hwa, Qian, Pierre, O’Brien, Kieran, Orchard, Jessica, Kim, Jinman, Patel, Sanjay, Redfern, Julie

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Impact of a deep learning assistant on the histopathologic classification of liver cancer by Kiani, Amirhossein, Uyumazturk, Bora, Rajpurkar, Pranav, Wang, Alex, Gao, Rebecca, Jones, Erik, Yu, Yifan, Langlotz, Curtis P., Ball, Robyn L., Montine, Thomas J., Martin, Brock A., Berry, Gerald J., Ozawa, Michael G., Hazard, Florette K., Brown, Ryanne A., Chen, Simon B., Wood, Mona, Allard, Libby S., Ylagan, Lourdes, Ng, Andrew Y., Shen, Jeanne

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Deep learning predicts hip fracture using confounding patient and healthcare variables by Badgeley, Marcus A., Zech, John R., Oakden-Rayner, Luke, Glicksberg, Benjamin S., Liu, Manway, Gale, William, McConnell, Michael V., Percha, Bethany, Snyder, Thomas M., Dudley, Joel T.

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A systematic review of smartphone-based human activity recognition methods for health research by Straczkiewicz, Marcin, James, Peter, Onnela, Jukka-Pekka

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Human–machine partnership with artificial intelligence for chest radiograph diagnosis by Patel, Bhavik N., Rosenberg, Louis, Willcox, Gregg, Baltaxe, David, Lyons, Mimi, Irvin, Jeremy, Rajpurkar, Pranav, Amrhein, Timothy, Gupta, Rajan, Halabi, Safwan, Langlotz, Curtis, Lo, Edward, Mammarappallil, Joseph, Mariano, A. J., Riley, Geoffrey, Seekins, Jayne, Shen, Luyao, Zucker, Evan, Lungren, Matthew P.

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Chatbots in the fight against the COVID-19 pandemic by Miner, Adam S., Laranjo, Liliana, Kocaballi, A. Baki

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Interpretable survival prediction for colorectal cancer using deep learning by Wulczyn, Ellery, Steiner, David F., Moran, Melissa, Plass, Markus, Reihs, Robert, Tan, Fraser, Flament-Auvigne, Isabelle, Brown, Trissia, Regitnig, Peter, Chen, Po-Hsuan Cameron, Hegde, Narayan, Sadhwani, Apaar, MacDonald, Robert, Ayalew, Benny, Corrado, Greg S., Peng, Lily H., Tse, Daniel, Müller, Heimo, Xu, Zhaoyang, Liu, Yun, Stumpe, Martin C., Zatloukal, Kurt, Mermel, Craig H.

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Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles by Bull, Jonathan R., Rowland, Simon P., Scherwitzl, Elina Berglund, Scherwitzl, Raoul, Danielsson, Kristina Gemzell, Harper, Joyce

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The future of sleep health: a data-driven revolution in sleep science and medicine by Perez-Pozuelo, Ignacio, Zhai, Bing, Palotti, Joao, Mall, Raghvendra, Aupetit, Michaël, Garcia-Gomez, Juan M., Taheri, Shahrad, Guan, Yu, Fernandez-Luque, Luis

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Challenges for the evaluation of digital health solutions—A call for innovative evidence generation approaches by Guo, Chaohui, Ashrafian, Hutan, Ghafur, Saira, Fontana, Gianluca, Gardner, Clarissa, Prime, Matthew

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Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease by Madani, Ali, Ong, Jia Rui, Tibrewal, Anshul, Mofrad, Mohammad R. K.

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Privacy-first health research with federated learning by Sadilek, Adam, Liu, Luyang, Nguyen, Dung, Kamruzzaman, Methun, Serghiou, Stylianos, Rader, Benjamin, Ingerman, Alex, Mellem, Stefan, Kairouz, Peter, Nsoesie, Elaine O., MacFarlane, Jamie, Vullikanti, Anil, Marathe, Madhav, Eastham, Paul, Brownstein, John S., Arcas, Blaise Aguera y., Howell, Michael D., Hernandez, John

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Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery by Korngiebel, Diane M., Mooney, Sean D.

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Respiration rate and volume measurements using wearable strain sensors by Chu, Michael, Nguyen, Thao, Pandey, Vaibhav, Zhou, Yongxiao, Pham, Hoang N., Bar-Yoseph, Ronen, Radom-Aizik, Shlomit, Jain, Ramesh, Cooper, Dan M., Khine, Michelle

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Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program by Ruamviboonsuk, Paisan, Krause, Jonathan, Chotcomwongse, Peranut, Sayres, Rory, Raman, Rajiv, Widner, Kasumi, Campana, Bilson J. L., Phene, Sonia, Hemarat, Kornwipa, Tadarati, Mongkol, Silpa-Archa, Sukhum, Limwattanayingyong, Jirawut, Rao, Chetan, Kuruvilla, Oscar, Jung, Jesse, Tan, Jeffrey, Orprayoon, Surapong, Kangwanwongpaisan, Chawawat, Sukumalpaiboon, Ramase, Luengchaichawang, Chainarong, Fuangkaew, Jitumporn, Kongsap, Pipat, Chualinpha, Lamyong, Saree, Sarawuth, Kawinpanitan, Srirut, Mitvongsa, Korntip, Lawanasakol, Siriporn, Thepchatri, Chaiyasit, Wongpichedchai, Lalita, Corrado, Greg S., Peng, Lily, Webster, Dale R.

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Explainable artificial intelligence for mental health through transparency and interpretability for understandability by Joyce, Dan W., Kormilitzin, Andrey, Smith, Katharine A., Cipriani, Andrea

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