Literature DB >> 31277839

The Missing Pieces of Artificial Intelligence in Medicine.

Coryandar Gilvary1, Neel Madhukar2, Jamal Elkhader3, Olivier Elemento4.   

Abstract

Stakeholders across the entire healthcare chain are looking to incorporate artificial intelligence (AI) into their decision-making process. From early-stage drug discovery to clinical decision support systems, we have seen examples of how AI can improve efficiency and decrease costs. In this Opinion, we discuss some of the key factors that should be prioritized to enable the successful integration of AI across the healthcare value chain. In particular, we believe a focus on model interpretability is crucial to obtain a deeper understanding of the underlying biological mechanisms and guide further investigations. Additionally, we discuss the importance of integrating diverse types of data within any AI framework to limit bias, increase accuracy, and model the interdisciplinary nature of medicine. We believe that widespread adoption of these practices will help accelerate the continued integration of AI into our current healthcare framework.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Keywords:  artificial intelligence; drug development; machine learning; model interpretability

Mesh:

Year:  2019        PMID: 31277839     DOI: 10.1016/j.tips.2019.06.001

Source DB:  PubMed          Journal:  Trends Pharmacol Sci        ISSN: 0165-6147            Impact factor:   14.819


  7 in total

1.  An Overview of Zebrafish Modeling Methods in Drug Discovery and Development.

Authors:  Bagher Larijani; Shayesteh Kokabi Hamidpour; Akram Tayanloo-Beik; Ainaz Shahbazbadr; Hanieh Yavari; Nazli Namazi; Mahmood Biglar; Babak Arjmand
Journal:  Adv Exp Med Biol       Date:  2022       Impact factor: 3.650

2.  Artificial intelligence and medical education: A global mixed-methods study of medical students' perspectives.

Authors:  Hamza Ejaz; Hari McGrath; Brian Lh Wong; Andrew Guise; Tom Vercauteren; Jonathan Shapey
Journal:  Digit Health       Date:  2022-05-02

3.  Development and Validation of an Automatic System for Intracerebral Hemorrhage Medical Text Recognition and Treatment Plan Output.

Authors:  Bo Deng; Wenwen Zhu; Xiaochuan Sun; Yanfeng Xie; Wei Dan; Yan Zhan; Yulong Xia; Xinyi Liang; Jie Li; Quanhong Shi; Li Jiang
Journal:  Front Aging Neurosci       Date:  2022-04-08       Impact factor: 5.702

4.  PaccMann: a web service for interpretable anticancer compound sensitivity prediction.

Authors:  Joris Cadow; Jannis Born; Matteo Manica; Ali Oskooei; María Rodríguez Martínez
Journal:  Nucleic Acids Res       Date:  2020-07-02       Impact factor: 16.971

Review 5.  Machine learning applications in drug development.

Authors:  Clémence Réda; Emilie Kaufmann; Andrée Delahaye-Duriez
Journal:  Comput Struct Biotechnol J       Date:  2019-12-26       Impact factor: 7.271

6.  Clinician checklist for assessing suitability of machine learning applications in healthcare.

Authors:  Ian Scott; Stacey Carter; Enrico Coiera
Journal:  BMJ Health Care Inform       Date:  2021-02

7.  "GENYAL" Study to Childhood Obesity Prevention: Methodology and Preliminary Results.

Authors:  Helena Marcos-Pasero; Elena Aguilar-Aguilar; Rocío de la Iglesia; Isabel Espinosa-Salinas; Susana Molina; Gonzalo Colmenarejo; J Alfredo Martínez; Ana Ramírez de Molina; Guillermo Reglero; Viviana Loria-Kohen
Journal:  Front Nutr       Date:  2022-03-08
  7 in total

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