Literature DB >> 35372779

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

Christopher A Lovejoy1, Anmol Arora2, Varun Buch3, Ittai Dayan4.   

Abstract

Interest in artificial intelligence (AI) has grown exponentially in recent years, attracting sensational headlines and speculation. While there is considerable potential for AI to augment clinical practice, there remain numerous practical implications that must be considered when exploring AI solutions. These range from ethical concerns about algorithmic bias to legislative concerns in an uncertain regulatory environment. In the absence of established protocols and examples of best practice, there is a growing need for clear guidance both for innovators and early adopters. Broadly, there are three stages to the innovation process: invention, development and implementation. In this paper, we present key considerations for innovators at each stage and offer suggestions along the AI development pipeline, from bench to bedside. © Royal College of Physicians 2022. All rights reserved.

Entities:  

Keywords:  algorithms; data; innovation; machine learning; technology

Year:  2022        PMID: 35372779      PMCID: PMC8966801          DOI: 10.7861/fhj.2021-0128

Source DB:  PubMed          Journal:  Future Healthc J        ISSN: 2514-6645


  18 in total

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

Authors:  Sebastian Vollmer; Bilal A Mateen; Gergo Bohner; Franz J Király; Rayid Ghani; Pall Jonsson; Sarah Cumbers; Adrian Jonas; Katherine S L McAllister; Puja Myles; David Granger; Mark Birse; Richard Branson; Karel G M Moons; Gary S Collins; John P A Ioannidis; Chris Holmes; Harry Hemingway
Journal:  BMJ       Date:  2020-03-20

2.  Prediction of gestational diabetes based on nationwide electronic health records.

Authors:  Nitzan Shalom Artzi; Smadar Shilo; Eran Hadar; Hagai Rossman; Shiri Barbash-Hazan; Avi Ben-Haroush; Ran D Balicer; Becca Feldman; Arnon Wiznitzer; Eran Segal
Journal:  Nat Med       Date:  2020-01-13       Impact factor: 53.440

3.  Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation.

Authors:  Sébastien Marcel; José Del R Millán
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2007-04       Impact factor: 6.226

4.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.

Authors:  Ryan Poplin; Avinash V Varadarajan; Katy Blumer; Yun Liu; Michael V McConnell; Greg S Corrado; Lily Peng; Dale R Webster
Journal:  Nat Biomed Eng       Date:  2018-02-19       Impact factor: 25.671

5.  A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.

Authors:  Xiaoxuan Liu; Livia Faes; Aditya U Kale; Siegfried K Wagner; Dun Jack Fu; Alice Bruynseels; Thushika Mahendiran; Gabriella Moraes; Mohith Shamdas; Christoph Kern; Joseph R Ledsam; Martin K Schmid; Konstantinos Balaskas; Eric J Topol; Lucas M Bachmann; Pearse A Keane; Alastair K Denniston
Journal:  Lancet Digit Health       Date:  2019-09-25

6.  Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.

Authors:  Karel G M Moons; Douglas G Altman; Johannes B Reitsma; John P A Ioannidis; Petra Macaskill; Ewout W Steyerberg; Andrew J Vickers; David F Ransohoff; Gary S Collins
Journal:  Ann Intern Med       Date:  2015-01-06       Impact factor: 25.391

7.  Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.

Authors:  Rishi J Desai; Shirley V Wang; Muthiah Vaduganathan; Thomas Evers; Sebastian Schneeweiss
Journal:  JAMA Netw Open       Date:  2020-01-03

8.  Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Authors:  Todd C Hollon; Balaji Pandian; Arjun R Adapa; Esteban Urias; Akshay V Save; Siri Sahib S Khalsa; Daniel G Eichberg; Randy S D'Amico; Zia U Farooq; Spencer Lewis; Petros D Petridis; Tamara Marie; Ashish H Shah; Hugh J L Garton; Cormac O Maher; Jason A Heth; Erin L McKean; Stephen E Sullivan; Shawn L Hervey-Jumper; Parag G Patil; B Gregory Thompson; Oren Sagher; Guy M McKhann; Ricardo J Komotar; Michael E Ivan; Matija Snuderl; Marc L Otten; Timothy D Johnson; Michael B Sisti; Jeffrey N Bruce; Karin M Muraszko; Jay Trautman; Christian W Freudiger; Peter Canoll; Honglak Lee; Sandra Camelo-Piragua; Daniel A Orringer
Journal:  Nat Med       Date:  2020-01-06       Impact factor: 53.440

Review 9.  Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.

Authors:  Xiaoxuan Liu; Samantha Cruz Rivera; David Moher; Melanie J Calvert; Alastair K Denniston
Journal:  Nat Med       Date:  2020-09-09       Impact factor: 87.241

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