Literature DB >> 33870475

Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies.

Gerard Letterie1.   

Abstract

Decision-making in fertility care is on the cusp of a significant frameshift. Online tools to integrate artificial intelligence into the decision-making process across all aspects of ART are rapidly emerging. These tools have the potential to improve outcomes and transition decision-making from one based on traditional provider centric assessments toward a hybrid triad of expertise, evidence, and algorithmic data analytics using AI. We can look forward to a time when AI will be the third part of a provider's tool box to complement expertise and medical literature to enable ever more accurate predictions and outcomes in ART. In their fully integrated format, these tools will be part of a digital fertility ecosystem of analytics embedded within an EMR. To date, the impact of AI on ART outcomes is inconclusive. No prospective studies have shown clear cut benefit or cost reductions over current practices, but we are very early in the process of developing and evaluating these tools. We owe it to ourselves to begin to examine these AI-driven analytics and develop a very clear idea about where we can and should go before we roll these tools into clinical care. Thoughtful scrutiny is essential lest we find ourselves in a position of trying to modulate and modify after entry of these tools into our clinics and patient care. The purpose of this commentary is to highlight the evolution and impact AI has had in other fields relevant to the fertility sector and describe a vision for applications within ART that could improve outcomes, reduce costs, and positively impact clinical care.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Artificial intelligence; Convolutional neural networks; Decision support systems; Deep learning; Electronic medical records; Feature engineering; Image analysis; Personalized medicine

Mesh:

Year:  2021        PMID: 33870475      PMCID: PMC8324699          DOI: 10.1007/s10815-021-02159-4

Source DB:  PubMed          Journal:  J Assist Reprod Genet        ISSN: 1058-0468            Impact factor:   3.357


  35 in total

Review 1.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

Authors:  Amit X Garg; Neill K J Adhikari; Heather McDonald; M Patricia Rosas-Arellano; P J Devereaux; Joseph Beyene; Justina Sam; R Brian Haynes
Journal:  JAMA       Date:  2005-03-09       Impact factor: 56.272

2.  Time-lapse systems for embryo incubation and assessment in assisted reproduction.

Authors:  Sarah Armstrong; Priya Bhide; Vanessa Jordan; Allan Pacey; Jane Marjoribanks; Cindy Farquhar
Journal:  Cochrane Database Syst Rev       Date:  2019-05-29

Review 3.  Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence.

Authors:  Mara Simopoulou; Konstantinos Sfakianoudis; Evangelos Maziotis; Nikolaos Antoniou; Anna Rapani; George Anifandis; Panagiotis Bakas; Stamatis Bolaris; Agni Pantou; Konstantinos Pantos; Michael Koutsilieris
Journal:  J Assist Reprod Genet       Date:  2018-07-27       Impact factor: 3.412

4.  A method for semi-automatic grading of human blastocyst microscope images.

Authors:  E Santos Filho; J A Noble; M Poli; T Griffiths; G Emerson; D Wells
Journal:  Hum Reprod       Date:  2012-06-26       Impact factor: 6.918

5.  Mobile devices and apps for health care professionals: uses and benefits.

Authors:  C Lee Ventola
Journal:  P T       Date:  2014-05

6.  Data sharing: using blockchain and decentralized data technologies to unlock the potential of artificial intelligence: What can assisted reproduction learn from other areas of medicine?

Authors:  Cristina Fontes Lindemann Hickman; Hoor Alshubbar; Jerome Chambost; Celine Jacques; Chris-Alexandre Pena; Andrew Drakeley; Thomas Freour
Journal:  Fertil Steril       Date:  2020-11       Impact factor: 7.329

7.  Artificial Intelligence in Health Care: A Report From the National Academy of Medicine.

Authors:  Michael E Matheny; Danielle Whicher; Sonoo Thadaney Israni
Journal:  JAMA       Date:  2019-12-17       Impact factor: 56.272

8.  Prediction Models - Development, Evaluation, and Clinical Application.

Authors:  Michael J Pencina; Benjamin A Goldstein; Ralph B D'Agostino
Journal:  N Engl J Med       Date:  2020-04-23       Impact factor: 91.245

9.  Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis.

Authors:  Pavithra I Dissanayake; Tiago K Colicchio; James J Cimino
Journal:  J Am Med Inform Assoc       Date:  2020-01-01       Impact factor: 4.497

10.  Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.

Authors:  Goran Medic; Melodi Kosaner Kließ; Louis Atallah; Jochen Weichert; Saswat Panda; Maarten Postma; Amer El-Kerdi
Journal:  F1000Res       Date:  2019-10-08
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