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Journal articles on the topic 'Artificial intelligence and machine learning'

Author: Grafiati

Published: 25 June 2021

Last updated: 2 February 2022

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1

Григоров, Отто Володимирович, Галина Оттівна Аніщенко, Всеволод Вікторович Стрижак, Надія Олександрівна Петренко, Ольга Володимирівна Турчин, Антон Олександрович Окунь, and Олег Ернестович Пономарьов. "Artificial intelligence. Machine learning." Vehicle and Electronics. Innovative Technologies, no. 15 (June 2, 2019): 17. http://dx.doi.org/10.30977/veit.2226-9266.2019.15.0.17.

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2

Tiwari, Ashutosh. "Artificial Intelligence And Machine Learning Empowering The Mass Medicine." Advanced Materials Letters 10, no. 5 (February 1, 2019): 302. http://dx.doi.org/10.5185/amlett.2019.1005.

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Pedoia, V. "Machine Learning and Artificial Intelligence." Osteoarthritis and Cartilage 28 (April 2020): S16. http://dx.doi.org/10.1016/j.joca.2020.02.010.

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4

Yakovlev, Igor. "Thesaurus Artificial Intelligence: Machine Learning." Upravlenie Megapolisom, no. 5 (2014): 18–33. http://dx.doi.org/10.14570/issn.2073-2724/um-5-2014/02-yakovlev.

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5

Wolfgang, Kelly. "Artificial Intelligence and Machine Learning." Hearing Journal 72, no. 3 (March 2019): 26. http://dx.doi.org/10.1097/01.hj.0000554346.30951.8d.

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6

Mashita, Tomohiro. "Artificial Intelligence and Machine Learning." Journal of The Institute of Image Information and Television Engineers 72, no. 3 (2018): 235–40. http://dx.doi.org/10.3169/itej.72.235.

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7

Bratko, Ivan. "Machine learning in artificial intelligence." Artificial Intelligence in Engineering 8, no. 3 (January 1993): 159–64. http://dx.doi.org/10.1016/0954-1810(93)90002-w.

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8

Lawlor, Bonnie. "Artificial Intelligence and Machine Learning." Chemistry International 43, no. 1 (January 1, 2021): 8–13. http://dx.doi.org/10.1515/ci-2021-0103.

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Abstract The uses of Artificial Intelligence (AI) and Machine Learning (ML) are topics of presentations at most conferences today across diverse professional disciplines. Why? The following quote says it all:

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Aryal, Gopi. "Artificial intelligence in surgical pathology." Journal of Pathology of Nepal 9, no. 1 (April 2, 2019): I. http://dx.doi.org/10.3126/jpn.v9i1.23444.

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Artificial intelligence (AI) is machine intelligence that mimics human cognitive function. It denotes the intelligence presented by some artificial entities including computers and robots. In supervised learning, a machine is trained with data that contain pairs of inputs and outputs. In unsupervised learning, machines are given data inputs that are not explicitly programmed.1 Machine learning refines a model that predicts outputs using sample inputs (features) and a feedback loop. It relies heavily on extracting or selecting salient features, which is a combination of art and science (“feature engineering”). A subset of feature learning is deep learning, which harnesses neural networks modeled after the biological nervous system of animals. Deep learning discovers the features from the raw data provided during training. Hidden layers in the artificial neural network represent increasingly more complex features in the data. Convolutional neural network is a type of deep learning commonly used for image analysis.

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Ghahramani, Zoubin. "Probabilistic machine learning and artificial intelligence." Nature 521, no. 7553 (May 2015): 452–59. http://dx.doi.org/10.1038/nature14541.

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Varshney, Kush R. "Trustworthy machine learning and artificial intelligence." XRDS: Crossroads, The ACM Magazine for Students 25, no. 3 (April 10, 2019): 26–29. http://dx.doi.org/10.1145/3313109.

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VanLehn, Kurt. "Machine learning: An artificial intelligence approach." Artificial Intelligence 25, no. 2 (February 1985): 233–36. http://dx.doi.org/10.1016/0004-3702(85)90004-9.

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Stefik, Mark J. "Machine learning: An artificial intelligence approach." Artificial Intelligence 25, no. 2 (February 1985): 236–38. http://dx.doi.org/10.1016/0004-3702(85)90005-0.

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Agrawal, Prateek. "Artificial Intelligence Applications and Machine Learning." Recent Advances in Computer Science and Communications 13, no. 6 (January 28, 2021): 1113–14. http://dx.doi.org/10.2174/266625581306201203092924.

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15

Pokutta, Sebastian. "Mathematik, Machine Learning und Artificial Intelligence." Mitteilungen der Deutschen Mathematiker-Vereinigung 28, no. 4 (December 1, 2020): 213–19. http://dx.doi.org/10.1515/dmvm-2020-0067.

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Mathur, Pankaj, Shweta Srivastava, Xiaowei Xu, and Jawahar L. Mehta. "Artificial Intelligence, Machine Learning, and Cardiovascular Disease." Clinical Medicine Insights: Cardiology 14 (January 2020): 117954682092740. http://dx.doi.org/10.1177/1179546820927404.

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Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

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Donepudi, Praveen Kumar. "Machine Learning and Artificial Intelligence in Banking." Engineering International 5, no. 2 (2017): 83–86. http://dx.doi.org/10.18034/ei.v5i2.490.

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Machine Learning and Artificial Intelligence applications in the financial sector have been thriving in the recent past. Their immense power has been harnessed in these institutions to offer business solutions in front end and back end processes to create efficiency and improve customer experience. This article will lay bare the applications of Machine Learning and Artificial Intelligence and evaluate their utility in different banking industry functional areas and frame how these institutions effectively use computational intelligence to improve their business. While traditional banking institutions are quickly catching up with the computational intelligence technologies with products like Chatbot, fintech companies, which seem to have embrace A.I. a long time ago, plays a critical role through its innovation and contribute substantially to financial intelligence. In conclusion, we can aptly say that Machine Learning and Artificial Intelligence technologies are taking over the banking sector, and it seems like there's nothing we can do about it.

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Singla, Rajiv, Ankush Singla, Yashdeep Gupta, and Sanjay Kalra. "Artificial intelligence/machine learning in diabetes care." Indian Journal of Endocrinology and Metabolism 23, no. 4 (2019): 495. http://dx.doi.org/10.4103/ijem.ijem_228_19.

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19

Forghani, Reza. "Machine Learning and Other Artificial Intelligence Applications." Neuroimaging Clinics of North America 30, no. 4 (November 2020): i. http://dx.doi.org/10.1016/s1052-5149(20)30067-8.

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Sivapalaratnam, Suthesh. "Artificial intelligence and machine learning in haematology." British Journal of Haematology 185, no. 2 (February 6, 2019): 207–8. http://dx.doi.org/10.1111/bjh.15774.

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Becker, Jan U., David Mayerich, Meghana Padmanabhan, Jonathan Barratt, Angela Ernst, Peter Boor, Pietro A. Cicalese, Chandra Mohan, Hien V. Nguyen, and Badrinath Roysam. "Artificial intelligence and machine learning in nephropathology." Kidney International 98, no. 1 (July 2020): 65–75. http://dx.doi.org/10.1016/j.kint.2020.02.027.

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Büyük, Süleyman Kutalmış, and Sedanur Hatal. "Artificial intelligence and machine learning in orthodontics." Ortadoğu Tıp Dergisi 11, no. 4 (December 1, 2019): 517–23. http://dx.doi.org/10.21601/ortadogutipdergisi.547782.

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Riedl, Mark O. "Human‐centered artificial intelligence and machine learning." Human Behavior and Emerging Technologies 1, no. 1 (January 2019): 33–36. http://dx.doi.org/10.1002/hbe2.117.

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Wichmann, Julian L., Martin J. Willemink, and Carlo N. De Cecco. "Artificial Intelligence and Machine Learning in Radiology." Investigative Radiology 55, no. 9 (July 29, 2020): 619–27. http://dx.doi.org/10.1097/rli.0000000000000673.

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25

Connor, Christopher W. "Artificial Intelligence and Machine Learning in Anesthesiology." Anesthesiology 131, no. 6 (December 1, 2019): 1346–59. http://dx.doi.org/10.1097/aln.0000000000002694.

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Abstract Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated. The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them—perhaps bringing anesthesiology into an era of machine-assisted discovery.

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Siswoyo, Bambang. "MultiClass Decision Forest Machine Learning Artificial Intelligence." Journal of Applied Informatics and Computing 4, no. 1 (January 23, 2020): 1–7. http://dx.doi.org/10.30871/jaic.v4i1.1155.

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Kecerdasan buatan merupakan solusi yang terbaik dalam menangai berbagai bidang. Algoritma Multi Class Decesion Forest Machine Learning yang merupakan bagian dari kecerdasan buatan adalah bidang yang menarik untuk diterapkan dalam bidang perbankan. Penelitian ini dikembangkan untuk membangun model yang dapat memprediksi dan mengevaluasi kebangkrutan industri perbankan. Variabel prediktor adalah rasio keuangan yang didapatkan dari publikasi situs http://www.idx.co.id. Modeling machine learning dengan enam variabel, dimana lima variabel sebagai input dan satu variabel sebagai target. Secara keseluruhan, Multi Class Decistion Forest Machine Learning mampu melatih data hubungan input-output dan perilaku pemodelan dengan baik, nilai accuracy 92%, nilai precision 92% dan nilai under area curve 90%.

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27

Sana, Munib. "Machine Learning and Artificial Intelligence in Radiology." Journal of the American College of Radiology 15, no. 8 (August 2018): 1139–42. http://dx.doi.org/10.1016/j.jacr.2017.11.015.

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Westcott, R. Jeffrey, and James E. Tcheng. "Artificial Intelligence and Machine Learning in Cardiology." JACC: Cardiovascular Interventions 12, no. 14 (July 2019): 1312–14. http://dx.doi.org/10.1016/j.jcin.2019.03.026.

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29

ŞCHEAU, Mircea Constantin, Adrian Liviu ARSENE, and Gabriel POPESCU. "Artificial Intelligence/Machine Learning Challenges and Evolution." International Journal of Information Security and Cybercrime 7, no. 1 (June 29, 2018): 11–22. http://dx.doi.org/10.19107/ijisc.2018.01.01.

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For 2018, one of the big challenges is the construction of security systems based on AI. However, it should take time and considerable resources to verify the effect of technologies involving machine learning and driving patterns. We can say that these structures are, conceptually, a computerized replica of their developers. While the trend is interesting, it does not provide any real guarantee as long as the same steps can be performed by criminals. The two armies faced in the virtual environment are in a continuous arms race, as faithful copies of their creators. Even if efforts seem to be hampered by the dynamics of the criminal spectrum, they are necessary precisely because of their mobility. Malware and ransomware attacks have targeted disparate and seemingly unrelated targets, globally. Condensing huge amounts of data at certain points or in mega-cloud spaces offers management advantages, but it can also be the premise of future offensive, not at all desirable. The confrontation engages the brightest minds on the planet, from both camps.

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30

Wei, Xian Min. "Analysis of Machine Learning Research and Application." Advanced Materials Research 171-172 (December 2010): 740–43. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.740.

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The artificial intelligence is an important branch of computer science, in recent years with the development of computer technology, artificial intelligence has also been in good development. Machine learning is a core part of artificial intelligence, machine learning background, research status, and applications in network intrusion detection, text categorization and data mining were studied in this paper.

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Anjum, Uzma. "Artificial Intelligence, Machine Learning and Deep Learning In Healthcare." Bioscience Biotechnology Research Communications 14, no. 7 (June 25, 2021): 144–48. http://dx.doi.org/10.21786/bbrc/14.7.36.

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32

Barash, Guy, Mauricio Castillo-Effen, Niyati Chhaya, Peter Clark, Huáscar Espinoza, Eitan Farchi, Christopher Geib, et al. "Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence." AI Magazine 40, no. 3 (September 30, 2019): 67–78. http://dx.doi.org/10.1609/aimag.v40i3.4981.

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The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

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Tiwari, Tanya, Tanuj Tiwari, and Sanjay Tiwari. "How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?" International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 2 (March 6, 2018): 1. http://dx.doi.org/10.23956/ijarcsse.v8i2.569.

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There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth. Machine Learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. Machine learning (ML)is a vibrant field of research, with a range of exciting areas for further development across different methods and applications. These areas include algorithmic interpretability, robustness, privacy, fairness, inference of causality, human-machine interaction, and security. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones. This paper gives an overview of artificial intelligence, machine learning & deep learning techniques and compare these techniques.

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Gigan, Sylvain, Florent Krzakala, Laurent Daudet, and Igor Carron. "Artificial intelligence: From electronics to optics." Photoniques, no. 104 (September 2020): 49–52. http://dx.doi.org/10.1051/photon/202010449.

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Machine Learning and big data are currently revolutionizing our way of life, in particular with the recent emergence of deep learning. Powered by CPU and GPU, they are currently hardware limited and extremely energy intensive. Photonics, either integrated or in free space, offers a very promising alternative for realizing optically machine learning tasks at high speed and low consumption. We here review the history and current state of the art of optical computing and optical machine learning.

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Patil, Mrs Pushpalata S. "Artificial Intelligence and Machine Learning’s Impact on Market Design." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2232–37. http://dx.doi.org/10.31142/ijtsrd14534.

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Hashimoto, Daniel A., Elan Witkowski, Lei Gao, Ozanan Meireles, and Guy Rosman. "Artificial Intelligence in Anesthesiology." Anesthesiology 132, no. 2 (February 1, 2020): 379–94. http://dx.doi.org/10.1097/aln.0000000000002960.

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Abstract Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence. The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

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Wang, Renjie, Wei Pan, Lei Jin, Yuehan Li, Yudi Geng, Chun Gao, Gang Chen, Hui Wang, Ding Ma, and Shujie Liao. "Artificial intelligence in reproductive medicine." Reproduction 158, no. 4 (October 2019): R139—R154. http://dx.doi.org/10.1530/rep-18-0523.

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Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.

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Abhinav, G. V. K. S., and S. Naga Subrahmanyam. "Artificial Intelligence in Healthcare." Journal of Drug Delivery and Therapeutics 9, no. 5-s (October 15, 2019): 164–66. http://dx.doi.org/10.22270/jddt.v9i5-s.3634.

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Artificial intelligence is to reduce human cognitive functions. It is bringing an approach to healthcare, powdered by increasing the availability of healthcare data and rapid progress of analyst techniques. We can survey the current status of Artificial intelligence applications in healthcare and discuss its future uses. It is the most transformative technology of the 21th century. Healthcare has been identified as an early candidate to be revolutized by artificial intelligence technologies. This article aims for providing an early stage contribution with the decision making capacities of artificial intelligence technologies. The possible ethical and legally complex backdrop of the existing framework. I will conclude the present structures are largely fit to deal with the challenge of artificial intelligence are present will discuss clearly about the artificial intelligence contribution to the present health care. Artificial intelligence, machine learning, deep learning can assist with proactive patient care, reduced future risk and streamlined work processes. Keywords: Artificial intelligence, machine learning, clinical decision support.

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KIM, Kyungmin. "Artificial Intelligence in Gravitational-Wave Science." Physics and High Technology 30, no. 6 (June 30, 2021): 14–19. http://dx.doi.org/10.3938/phit.30.018.

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Artificial intelligence gaining popularity not only in the computational engineering industry but also in fundamental science. For the realization of artificial intelligence, numerous machine learning algorithms have been introduced and tested for their applicability. Even in the field of gravitational-wave science, the application of machine learning has been widely studied to enhance conventional analyses in all disciplines from searching for gravitational-wave signals to characterizing noise transients. In this article, I briefly introduce the current status of gravitational-wave science and summarize research topics in which machine learning is applied to each discipline of gravitational-wave science.

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Milojević, Nenad, and Srdjan Redzepagic. "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management." Journal of Central Banking Theory and Practice 10, no. 3 (September 1, 2021): 41–57. http://dx.doi.org/10.2478/jcbtp-2021-0023.

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Abstract Artificial intelligence and machine learning have increasing influence on the financial sector, but also on economy as a whole. The impact of artificial intelligence and machine learning on banking risk management has become particularly interesting after the global financial crisis. The research focus is on artificial intelligence and machine learning potential for further banking risk management improvement. The paper seeks to explore the possibility for successful implementation yet taking into account challenges and problems which might occur as well as potential solutions. Artificial intelligence and machine learning have potential to support the mitigation measures for the contemporary global economic and financial challenges, including those caused by the COVID-19 crisis. The main focus in this paper is on credit risk management, but also on analysing artificial intelligence and machine learning application in other risk management areas. It is concluded that a measured and well-prepared further application of artificial intelligence, machine learning, deep learning and big data analytics can have further positive impact, especially on the following risk management areas: credit, market, liquidity, operational risk, and other related areas.

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Kocsis, Zoltan Tamas. "Artificial Neural Networks in Medicine." Acta Technica Jaurinensis 12, no. 2 (April 26, 2019): 117–29. http://dx.doi.org/10.14513/actatechjaur.v12.n2.497.

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In recent years, Information Technology has been developed in a way that applications based on Artificial Intelligence have emerged. This development has resulted in machines being able to perform increasingly complex learning processes. The use of Information Technology, including Artificial Intelligence is becoming more and more widespread in all fields of life. Some common examples are face recognition in smartphones, or the programming of washing machines. As you may think, Artificial Intelligence can also be used in medicine. In this study I am presenting the relationship between machine learning and neural networks and their possible use in medicine.

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L, Anusha, and Nagaraja G. S. "Outlier Detection in High Dimensional Data." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 128–30. http://dx.doi.org/10.35940/ijeat.e2675.0610521.

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Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.

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Mori, Kensaku. "Computer Aided Surgery and Artificial Intelligence/Machine Learning." Journal of Japan Society of Computer Aided Surgery 19, no. 3 (2017): 147–50. http://dx.doi.org/10.5759/jscas.19.147.

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Aminzadeh, Fred. "Energy Sustainability with Artificial Intelligence and Machine Learning." Journal of Sustainable Energy Engineering 6, no. 2 (August 23, 2018): 99–100. http://dx.doi.org/10.7569/jsee.2018.629508.

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Balasubramanian, Natarajan, Evan Penniman Starr, Alexander Oettl, Christian Catalini, Prithwiraj Choudhury, and Jorge Guzman. "Machine Learning and Artificial Intelligence in Strategy Research." Academy of Management Proceedings 2018, no. 1 (August 2018): 12214. http://dx.doi.org/10.5465/ambpp.2018.12214symposium.

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Lebedev, G. S., A. P. Maslyukov, I. A. Shaderkin, and A. I. Shaderkina. "Deep machine learning (artificial intelligence) in ultrasound diagnostics." Journal of Telemedicine and E-Health 2020, no. 2 (June 30, 2020): 22–29. http://dx.doi.org/10.29188/2542-2413-2020-6-2-22-29.

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Chen, Xue, Shiyuan Xu, Yibo Cao, Jiahuan He, and Yang Jiao. "Application of artificial intelligence based on machine learning." Journal of Physics: Conference Series 1693 (December 2020): 012084. http://dx.doi.org/10.1088/1742-6596/1693/1/012084.

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Matava, Clyde, Evelina Pankiv, Luis Ahumada, Benjamin Weingarten, and Allan Simpao. "Artificial intelligence, machine learning and the pediatric airway." Pediatric Anesthesia 30, no. 3 (March 2020): 264–68. http://dx.doi.org/10.1111/pan.13792.

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Mekov, Evgeni, Marc Miravitlles, and Rosen Petkov. "Artificial intelligence and machine learning in respiratory medicine." Expert Review of Respiratory Medicine 14, no. 6 (March 17, 2020): 559–64. http://dx.doi.org/10.1080/17476348.2020.1743181.

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Galbusera, Fabio, Gloria Casaroli, and Tito Bassani. "Artificial intelligence and machine learning in spine research." JOR SPINE 2, no. 1 (March 2019): e1044. http://dx.doi.org/10.1002/jsp2.1044.

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