Literature DB >> 32198138

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness.

Sebastian Vollmer1,2, Bilal A Mateen1,3,4, Gergo Bohner1,2, Franz J Király1,5, Rayid Ghani6, Pall Jonsson7, Sarah Cumbers8, Adrian Jonas9, Katherine S L McAllister9, Puja Myles10, David Granger11, Mark Birse11, Richard Branson11, Karel G M Moons12, Gary S Collins13, John P A Ioannidis14, Chris Holmes15,16, Harry Hemingway17,18,19.   

Abstract

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Year:  2020        PMID： 32198138     DOI： 10.1136/bmj.l6927

Source DB:  PubMed          Journal:  BMJ        ISSN： 0959-8138


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  63 in total

Review 1.  Designing deep learning studies in cancer diagnostics.

Authors:  Andreas Kleppe; Ole-Johan Skrede; Sepp De Raedt; Knut Liestøl; David J Kerr; Håvard E Danielsen
Journal:  Nat Rev Cancer       Date:  2021-01-29       Impact factor: 60.716

Review 2.  Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease.

Authors:  Rachel F Buckley
Journal:  Neurotherapeutics       Date:  2021-03-29       Impact factor: 7.620

Review 3.  Artificial Intelligence: Review of Current and Future Applications in Medicine.

Authors:  L Brannon Thomas; Stephen M Mastorides; Narayan A Viswanadhan; Colleen E Jakey; Andrew A Borkowski
Journal:  Fed Pract       Date:  2021-11

4.  Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study.

Authors:  Ravi B Parikh; Christopher R Manz; Maria N Nelson; Chalanda N Evans; Susan H Regli; Nina O'Connor; Lynn M Schuchter; Lawrence N Shulman; Mitesh S Patel; Joanna Paladino; Judy A Shea
Journal:  Support Care Cancer       Date:  2022-01-30       Impact factor: 3.603

5.  Key considerations for the use of artificial intelligence in healthcare and clinical research.

Authors:  Christopher A Lovejoy; Anmol Arora; Varun Buch; Ittai Dayan
Journal:  Future Healthc J       Date:  2022-03

6.  Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.

Authors:  Caroline A Figueroa; Adrian Aguilera; Bibhas Chakraborty; Arghavan Modiri; Jai Aggarwal; Nina Deliu; Urmimala Sarkar; Joseph Jay Williams; Courtney R Lyles
Journal:  J Am Med Inform Assoc       Date:  2021-06-12       Impact factor: 4.497

7.  Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology.

Authors:  Jason D Morgenstern; Laura C Rosella; Andrew P Costa; Russell J de Souza; Laura N Anderson
Journal:  Adv Nutr       Date:  2021-06-01       Impact factor: 8.701

8.  Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research.

Authors:  Gabrielle Samuel; Jenn Chubb; Gemma Derrick
Journal:  J Empir Res Hum Res Ethics       Date:  2021-03-18       Impact factor: 1.742

9.  Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.

Authors:  Sridharan Raghavan; Kevin Josey; Gideon Bahn; Domenic Reda; Sanjay Basu; Seth A Berkowitz; Nicholas Emanuele; Peter Reaven; Debashis Ghosh
Journal:  Ann Epidemiol       Date:  2021-07-17       Impact factor: 3.797

10.  Predictive modeling for peri-implantitis by using machine learning techniques.

Authors:  Tomoaki Mameno; Masahiro Wada; Kazunori Nozaki; Toshihito Takahashi; Yoshitaka Tsujioka; Suzuna Akema; Daisuke Hasegawa; Kazunori Ikebe
Journal:  Sci Rep       Date:  2021-05-27       Impact factor: 4.379
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