Literature DB >> 31309802

Artificial Intelligence and Deep Learning: The Future of Medicine and Medical Practice.

Madhusudana Girija Sanal1, Kolin Paul2, Senthil Kumar3, Nirmal Kumar Ganguly4.   

Abstract

ABSTRACT: Artificial Intelligence (AI) and access to "Big Data" together with the evolving techniques in biotechnology will change the medical practice a big way. Many diseases such as type II diabetes will no longer be considered as a single disease. Many familiar cancers such as cancer of liver or pancreas will have hundreds of subtypes whose management will be very different. The way we think about diseases will change. It will no longer be possible for clinicians to make a diagnosis, remember the names of diseases, the names of drugs or management protocols without the help of computers. As computer intelligence becomes more important than human intelligence in deciding diagnosis and treatment there will be a paradigm in the role of doctors. Internet, computers and social media will become more important than individuals in decision making. As a result, medicine will go more and more egalitarian ("wiki") with increasing community participation in health decision making and management. A socialistic pattern will evolve over time globally as an adaptive reaction to the pressures put by artificial intelligence. This is because the individual differences in knowledge or intellect between human beings will become less apparent compared to the super powers of artificial intelligence. Qualities which are unique for humans such as compassion, empathy and emotional care will decide the professional success of future physicians even more than today. Today we are using artificial intelligence in diagnosis and prediction to help clinicians. Clinical algorithms and human experience cannot be replaced by machines. It will take many years to completely merge or replace humans with machines. However, we need to modify our medical education system in order to prepare the medical community and sensitize the society well in advance for a smooth transition. © Journal of the Association of Physicians of India 2011.

Entities:  

Mesh:

Year:  2019        PMID: 31309802

Source DB:  PubMed          Journal:  J Assoc Physicians India        ISSN: 0004-5772


  7 in total

1.  Filtration Selection and Data Consilience: Distinguishing Signal from Artefact with Mechanical Impact Simulator Data.

Authors:  Nathan D Schilaty; Nathaniel A Bates; Ryo Ueno; Timothy E Hewett
Journal:  Ann Biomed Eng       Date:  2020-07-06       Impact factor: 3.934

2.  Machine learning algorithms as new screening approach for patients with endometriosis.

Authors:  Sofiane Bendifallah; Anne Puchar; Stéphane Suisse; Léa Delbos; Mathieu Poilblanc; Philippe Descamps; Francois Golfier; Cyril Touboul; Yohann Dabi; Emile Daraï
Journal:  Sci Rep       Date:  2022-01-12       Impact factor: 4.379

Review 3.  Machine learning and deep learning predictive models for type 2 diabetes: a systematic review.

Authors:  Luis Fregoso-Aparicio; Julieta Noguez; Luis Montesinos; José A García-García
Journal:  Diabetol Metab Syndr       Date:  2021-12-20       Impact factor: 3.320

Review 4.  An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection.

Authors:  Aurelle Tchagna Kouanou; Thomas Mih Attia; Cyrille Feudjio; Anges Fleurio Djeumo; Adèle Ngo Mouelas; Mendel Patrice Nzogang; Christian Tchito Tchapga; Daniel Tchiotsop
Journal:  J Healthc Eng       Date:  2021-11-22       Impact factor: 2.682

Review 5.  Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.

Authors:  Chandrabose Selvaraj; Ishwar Chandra; Sanjeev Kumar Singh
Journal:  Mol Divers       Date:  2021-10-23       Impact factor: 2.943

6.  Salivary MicroRNA Signature for Diagnosis of Endometriosis.

Authors:  Sofiane Bendifallah; Stéphane Suisse; Anne Puchar; Léa Delbos; Mathieu Poilblanc; Philippe Descamps; Francois Golfier; Ludmila Jornea; Delphine Bouteiller; Cyril Touboul; Yohann Dabi; Emile Daraï
Journal:  J Clin Med       Date:  2022-01-26       Impact factor: 4.241

7.  MicroRNome analysis generates a blood-based signature for endometriosis.

Authors:  Sofiane Bendifallah; Yohann Dabi; Stéphane Suisse; Ludmila Jornea; Delphine Bouteiller; Cyril Touboul; Anne Puchar; Emile Daraï
Journal:  Sci Rep       Date:  2022-03-08       Impact factor: 4.379

  7 in total

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