Artificial intelligence: An introduction for clinicians

被引：5

作者：

Briganti, G. ^{[1
]}

机构：

[1] Univ Mons, Fac Med, Serv Neurosci, Chaire Intelligence Artificielle & Med Digitale, Ave Champs Mars 6, B-7000 Mons, Belgium

来源：

REVUE DES MALADIES RESPIRATOIRES | 2023年 / 40卷 / 04期

关键词：

Medical informatics; Data science; Statistics; Innovation;

D O I：

10.1016/j.rmr.2023.02.005

中图分类号：

R56 [呼吸系及胸部疾病];

学科分类号：

摘要：

Artificial intelligence (AI) is a growing field that has the potential to transform many areas of society, including healthcare. For a physician, it is important to understand the basics of AI and its potential applications in medicine. AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as pattern recognition, learning from data, and decision-making. This technology can be used to analyze large amounts of patient data and to identify trends and patterns that can be difficult for human physicians to detect. This can help doctors to manage their workload more efficiently and provide better care for their patients. All in all, AI has the potential to dramatically improve the practice of medicine and improve patient outcomes. In this work, the definition and the key principles of AI are outlined, with particular focus on the field of machine learning, which has been undergoing considerable development in medicine, providing clinicians with in-depth understanding of the principles underlying the new technologies ensuring improved health care.(c) 2023 SPLF. Published by Elsevier Masson SAS. All rights reserved.

引用

页码：308 / 313

页数：6

共 15 条

[1] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

[2] Artificial Intelligence in Medicine: Today and Tomorrow [J].

Briganti, Giovanni ;

Le Moine, Olivier .

FRONTIERS IN MEDICINE, 2020, 7

[3] Accuracy of a Fourth-Generation Subcutaneous Continuous Glucose Sensor [J].

Christiansen, Mark P. ;

Garg, Satish K. ;

Brazg, Ronald ;

Bode, Bruce W. ;

Bailey, Timothy S. ;

Slover, Robert H. ;

Sullivan, Ashley ;

Huang, Suiying ;

Shin, John ;

Lee, Scott W. ;

Kaufman, Francine R. .

DIABETES TECHNOLOGY & THERAPEUTICS, 2017, 19 (08) :446-456

[4] Human-like behavioral variability blurs the distinction between a human and a machine in a nonverbal Turing test [J].

Ciardo, F. ;

De Tommaso, D. ;

Wykowska, A. .

SCIENCE ROBOTICS, 2022, 7 (68)

[5] Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics [J].

Ding, Michael Q. ;

Chen, Lujia ;

Cooper, Gregory F. ;

Young, Jonathan D. ;

Lu, Xinghua .

MOLECULAR CANCER RESEARCH, 2018, 16 (02) :269-278

[6] Patients' and caregivers' experiences of using continuous glucose monitoring to support diabetes self-management: qualitative study [J].

Lawton, J. ;

Blackburn, M. ;

Allen, J. ;

Campbell, F. ;

Elleri, D. ;

Leelarathna, L. ;

Rankin, D. ;

Tauschmann, M. ;

Thabit, H. ;

Hovorka, R. .

BMC ENDOCRINE DISORDERS, 2018, 18

[7] Deep learning [J].

LeCun, Yann ;

Bengio, Yoshua ;

Hinton, Geoffrey .

NATURE, 2015, 521 (7553) :436-444

[8] What is a support vector machine? [J].

Noble, William S. .

NATURE BIOTECHNOLOGY, 2006, 24 (12) :1565-1567

[9] Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands [J].

Regalia, Giulia ;

Onorati, Francesco ;

Lai, Matteo ;

Caborni, Chiara ;

Picard, Rosalind W. .

EPILEPSY RESEARCH, 2019, 153 :79-82

[10] Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead [J].

Rudin, Cynthia .

NATURE MACHINE INTELLIGENCE, 2019, 1 (05) :206-215

← 1 2 →