This paper is in the following e-collection/theme issue:

Reviews in Medical Informatics (93) JMIR Theme Issue: COVID-19 Special Issue (2452) Theme Issue: Medical Informatics and COVID-19 (107) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517)

Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23811, first published .
Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

Authors of this article:

Hafsa Bareen Syeda1 Author Orcid Image ;   Mahanazuddin Syed2 Author Orcid Image ;   Kevin Wayne Sexton2, 3, 4 Author Orcid Image ;   Shorabuddin Syed2 Author Orcid Image ;   Salma Begum5 Author Orcid Image ;   Farhanuddin Syed6 Author Orcid Image ;   Fred Prior2, 7 Author Orcid Image ;   Feliciano Yu Jr2 Author Orcid Image
Article Authors Cited by (100) Tweetations (3) Metrics

Journals

  1. Leslie D, Mazumder A, Peppin A, Wolters M, Hagerty A. Does “AI” stand for augmenting inequality in the era of covid-19 healthcare?. BMJ 2021:n304 View
  2. Siddique S, Chow J. Machine Learning in Healthcare Communication. Encyclopedia 2021;1(1):220 View
  3. Topaz M, Peltonen L, Mitchell J, Alhuwail D, Barakati S, Lewis A, Moen H, Veeranki S, Block L, Risling T, Ronquillo C. How to Improve Information Technology to Support Healthcare to Address the COVID-19 Pandemic: an International Survey with Health Informatics Experts. Yearbook of Medical Informatics 2021;30(01):061 View
  4. Souza A, Almeida D, Barcelos T, Bortoletto R, Munoz R, Waldman H, Goes M, Silva L. RETRACTED ARTICLE: Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network. Soft Computing 2023;27(6):3295 View
  5. Born J, Beymer D, Rajan D, Coy A, Mukherjee V, Manica M, Prasanna P, Ballah D, Guindy M, Shaham D, Shah P, Karteris E, Robertus J, Gabrani M, Rosen-Zvi M. On the role of artificial intelligence in medical imaging of COVID-19. Patterns 2021;2(6):100269 View
  6. TOKMAK M, KÜÇÜKSİLLE E. DERİN SİNİR AĞLARI VE YENİDEN ÖRNEKLEME METOTLARI İLE RUTİN KAN TESTLERİNE DAYALI COVID-19 TESPİTİ. Konya Journal of Engineering Sciences 2021;9(2):522 View
  7. Corsaro C, Sturniolo A, Fazio E. Gaussian Parameters Correlate with the Spread of COVID-19 Pandemic: The Italian Case. Applied Sciences 2021;11(13):6119 View
  8. Valtchev S, Asgary A, Chen M, Cronemberger F, Najafabadi M, Cojocaru M, Wu J. Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data. Electronics 2021;10(14):1626 View
  9. Adadi A, Lahmer M, Nasiri S. Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead. Journal of King Saud University - Computer and Information Sciences 2022;34(8):5898 View
  10. Abdulaal A, Patel A, Al-Hindawi A, Charani E, Alqahtani S, Davies G, Mughal N, Moore L. Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis. JMIR Formative Research 2021;5(7):e27992 View
  11. Gutiérrez-Aguado A, Curioso W, Machicao J, Eguia H. Strengthening capacities of multidisciplinary professionals to apply data science in public health: Experience of an international graduate diploma program in Peru. International Journal of Medical Informatics 2023;169:104913 View
  12. Ho M, Arnold C, Decker S, Hazle J, Krupinski E, Mankoff D. Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic. Academic Radiology 2023;30(4):631 View
  13. Abugabah A, Shahid F. Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19. Mathematics 2023;11(4):1051 View
  14. Liuzzi P, Campagnini S, Fanciullacci C, Arienti C, Patrini M, Carrozza M, Mannini A. Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution. Medical & Biological Engineering & Computing 2022;60(2):459 View
  15. Hamon R, Junklewitz H, Sanchez I, Malgieri G, De Hert P. Bridging the Gap Between AI and Explainability in the GDPR: Towards Trustworthiness-by-Design in Automated Decision-Making. IEEE Computational Intelligence Magazine 2022;17(1):72 View
  16. Hendl J. STROJOVÉ UČENÍ, UMĚLÁ INTELIGENCE A VĚDA O DATECH V BOJI S PANDEMIÍ COVID-19. Medsoft 2021;33(1):11 View
  17. Majeed A, Hwang S. Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry 2021;14(1):16 View
  18. Hassan S, Ahamed J, Ahmad K. Analytics of machine learning-based algorithms for text classification. Sustainable Operations and Computers 2022;3:238 View
  19. Shelmerdine S, Arthurs O, Denniston A, Sebire N. Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health & Care Informatics 2021;28(1):e100385 View
  20. Kamalov F, Rajab K, Cherukuri A, Elnagar A, Safaraliev M. Deep learning for Covid-19 forecasting: State-of-the-art review.. Neurocomputing 2022;511:142 View
  21. Zarei J, Jamshidnezhad A, Haddadzadeh Shoushtari M, Mohammad Hadianfard A, Cheraghi M, Sheikhtaheri A, Ieracitano C. Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations. Journal of Healthcare Engineering 2022;2022:1 View
  22. Jemioło P, Storman D, Orzechowski P. Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review. Journal of Clinical Medicine 2022;11(7):2054 View
  23. Cushnan D, Bennett O, Berka R, Bertolli O, Chopra A, Dorgham S, Favaro A, Ganepola T, Halling-Brown M, Imreh G, Jacob J, Jefferson E, Lemarchand F, Schofield D, Wyatt J. An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis. GigaScience 2021;10(11) View
  24. Song W, Zhang L, Liu L, Sainlaire M, Karvar M, Kang M, Pullman A, Lipsitz S, Massaro A, Patil N, Jasuja R, Dykes P. Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods. Journal of the American Medical Informatics Association 2022;29(10):1661 View
  25. Al Khalili S, Al Maani A, Al Wahaibi A, Al Yaquobi F, Al-Jardani A, Al Harthi K, Alqayoudhi A, Al Manji A, Al Rawahi B, Al-Abri S. Challenges and Opportunities for Public Health Service in Oman From the COVID-19 Pandemic: Learning Lessons for a Better Future. Frontiers in Public Health 2021;9 View
  26. De Groote H. Economic analysis of pest problems in agriculture and food chains in Africa. Current Opinion in Insect Science 2022;54:100969 View
  27. Markom M, Mohd Taha S, Adom A, Abdull Sukor A, Abdul Nasir A, Yazid H, Tan E, Markom A. A Review: Deep Learning Classification Performance of Normal and COVID-19 Chest X-ray Images. Journal of Physics: Conference Series 2021;2071(1):012003 View
  28. Kini A, Gopal Reddy A, Kaur M, Satheesh S, Singh J, Martinetz T, Alshazly H, Teekaraman Y. Ensemble Deep Learning and Internet of Things‐Based Automated COVID‐19 Diagnosis Framework. Contrast Media & Molecular Imaging 2022;2022(1) View
  29. Lee C, Chen Y. New Insights Into Drug Repurposing for COVID-19 Using Deep Learning. IEEE Transactions on Neural Networks and Learning Systems 2021;32(11):4770 View
  30. Ramón A, Zaragozá M, Torres A, Cascón J, Blasco P, Milara J, Mateo J. Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab. Journal of Clinical Medicine 2022;11(16):4729 View
  31. de Lara-Tuprio E, Estadilla C, Macalalag J, Teng T, Uyheng J, Espina K, Pulmano C, Estuar M, Sarmiento R. Policy-driven mathematical modeling for COVID-19 pandemic response in the Philippines. Epidemics 2022;40:100599 View
  32. Ramos M, Gomes D, Mello N, Silva E, Barreto J, Shimizu H. Big Data e Inteligência Artificial para pesquisa translacional na Covid-19: revisão rápida. Saúde em Debate 2022;46(135):1202 View
  33. Sharma D, Subramanian M, Malyadri P, Reddy B, Sharma M, Tahreem M. Classification of COVID-19 by using supervised optimized machine learning technique. Materials Today: Proceedings 2022;56:2058 View
  34. Hatmal M, Al-Hatamleh M, Olaimat A, Mohamud R, Fawaz M, Kateeb E, Alkhairy O, Tayyem R, Lounis M, Al-Raeei M, Dana R, Al-Ameer H, Taha M, Bindayna K. Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors. Vaccines 2022;10(3):366 View
  35. E. Samadi M, Kiefer S, Fritsch S, Bickenbach J, Schuppert A, Chen C. A training strategy for hybrid models to break the curse of dimensionality. PLOS ONE 2022;17(9):e0274569 View
  36. Watson M, Awwad Shiekh Hasan B, Al Moubayed N. Using model explanations to guide deep learning models towards consistent explanations for EHR data. Scientific Reports 2022;12(1) View
  37. Wichmann R, Fagundes T, de Oliveira T, Batista A, Chiavegatto Filho A, Roman-Gonzalez A. Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study. PLOS ONE 2022;17(12):e0278397 View
  38. Ong V, Soleimani A, Amirghasemi F, Khazaee Nejad S, Abdelmonem M, Razaviyayn M, Hosseinzadeh P, Comai L, Mousavi M. Impedimetric Sensing: An Emerging Tool for Combating the COVID-19 Pandemic. Biosensors 2023;13(2):204 View
  39. Kaur A, Elarabawy M, Abd-Elnaby M, Varadarajan V, Sharma S, Soni M. IoT-Based Technological Framework for Inhibiting the Spread of COVID-19: A Pandemic Using Machine Learning and Fuzzy-Based Processes. Security and Communication Networks 2022;2022:1 View
  40. Maabreh M, Maabreh A, Qolomany B, Al-Fuqaha A. The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights. International Journal of Distributed Sensor Networks 2022;18(7):155013292211051 View
  41. Adeoye E, Rozenfeld Y, Beam J, Boudreau K, Cox E, Scanlan J. Who was at risk for COVID-19 late in the US pandemic? Insights from a population health machine learning model. Medical & Biological Engineering & Computing 2022;60(7):2039 View
  42. Jung C, Mamandipoor B, Fjølner J, Bruno R, Wernly B, Artigas A, Bollen Pinto B, Schefold J, Wolff G, Kelm M, Beil M, Sviri S, van Heerden P, Szczeklik W, Czuczwar M, Elhadi M, Joannidis M, Oeyen S, Zafeiridis T, Marsh B, Andersen F, Moreno R, Cecconi M, Leaver S, De Lange D, Guidet B, Flaatten H, Osmani V. Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation. JMIR Medical Informatics 2022;10(3):e32949 View
  43. Kiaei A, Salari N, Boush M, Mansouri K, Hosseinian-Far A, Ghasemi H, Mohammadi M. Identification of Suitable Drug Combinations for Treating COVID-19 Using a Novel Machine Learning Approach: The RAIN Method. Life 2022;12(9):1456 View
  44. Saadatmand S, Salimifard K, Mohammadi R, Marzban M, Naghibzadeh-Tahami A. Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning. Medical & Biological Engineering & Computing 2022;60(4):957 View
  45. Palermo M, Policarpo L, Costa C, Righi R. Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics. Network Modeling Analysis in Health Informatics and Bioinformatics 2022;11(1) View
  46. Ramón A, Torres A, Milara J, Cascón J, Blasco P, Mateo J. eXtreme Gradient Boosting-based method to classify patients with COVID-19. Journal of Investigative Medicine 2022;70(7):1472 View
  47. Ovcharenko E, Kutikhin A, Gruzdeva O, Kuzmina A, Slesareva T, Brusina E, Kudasheva S, Bondarenko T, Kuzmenko S, Osyaev N, Ivannikova N, Vavin G, Moses V, Danilov V, Komossky E, Klyshnikov K. Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study. Journal of Cardiovascular Development and Disease 2023;10(2):39 View
  48. Ormeño P, Márquez G, Guerrero-Nancuante C, Taramasco C. Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study. International Journal of Environmental Research and Public Health 2022;19(13):8058 View
  49. KAPUCU V, TURHAN S, PIÇAKÇIEFE M, DOĞU E. COVID19PREDICTOR: WEB-BASED INTERFACE TO DEVELOP MACHINE LEARNING MODELS FOR DIAGNOSIS OF COVID-19 BASED ON CLINICAL DATA AND ROUTINE TESTS. Karya Journal of Health Science 2022;3(3):216 View
  50. Ceccon D, Amaral P, Andrade L, da Silva M, Andrade L, Moraes T, Bagno F, Rocha R, de Almeida Marques D, Ferreira G, Lourenço A, Ribeiro Á, Coelho-dos-Reis J, da Fonseca F, Gonzalez J. New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning. Brazilian Journal of Microbiology 2023;54(2):769 View
  51. S. Alotaibi S, Al-Rasheed A, Althahabi S, Ahmed Hamza M, Mohamed A, Sarwar Zamani A, Motwakel A, I. Eldesouki M. Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset. Computers, Materials & Continua 2022;73(2):3305 View
  52. Rakhshan S, Nejad M, Zaj M, Ghane F. Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19. Computers in Biology and Medicine 2023;158:106817 View
  53. Machado M, Silva R, Namias M, Lessa A, Neves M, Silva C, Oliveira D, Reina T, Lira A, Almeida L, Zanchettin C, Netto E. Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography. Journal of Medical and Biological Engineering 2023;43(2):156 View
  54. Dabbagh R, Jamal A, Bhuiyan Masud J, Titi M, Amer Y, Khayat A, Alhazmi T, Hneiny L, Baothman F, Alkubeyyer M, Khan S, Temsah M. Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review. Cureus 2023 View
  55. Mulenga C, Kaonga P, Hamoonga R, Mazaba M, Chabala F, Musonda P, Ramadas A. Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning. Global Health 2023;2023:1 View
  56. Ghassemi N, Shoeibi A, Khodatars M, Heras J, Rahimi A, Zare A, Zhang Y, Pachori R, Gorriz J. Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning. Applied Soft Computing 2023;144:110511 View
  57. Maiti P, Nand M, Mathpal S, Wahab S, Kuniyal J, Sharma P, Joshi T, Ramakrishnan M, Chandra S. Potent multi-target natural inhibitors against SARS-CoV-2 from medicinal plants of the Himalaya: a discovery from hybrid machine learning, chemoinformatics, and simulation assisted screening. Journal of Biomolecular Structure and Dynamics 2024;42(20):10551 View
  58. Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior K, Poirrier J, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Review of Pharmacoeconomics & Outcomes Research 2024;24(1):63 View
  59. McFadden B, Reynolds M, Inglis T. Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice. Frontiers in Digital Health 2023;5 View
  60. Sultan L, Haertter A, Al-Hasani M, Demiris G, Cary T, Tung-Chen Y, Sehgal C. Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound. AI 2023;4(4):875 View
  61. Tariq M, Ismail S, Babar M, Ahmad A, Wang L. Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PLOS ONE 2023;18(7):e0287755 View
  62. Sharifi-Kia A, Nahvijou A, Sheikhtaheri A. Machine learning-based mortality prediction models for smoker COVID-19 patients. BMC Medical Informatics and Decision Making 2023;23(1) View
  63. Chandra S, Bajpai M. Efficient Machine Learning and Factional Calculus Based Mathematical Model for Early COVID Prediction. Human-Centric Intelligent Systems 2023;3(4):508 View
  64. Dobrijević D, Vilotijević-Dautović G, Katanić J, Horvat M, Horvat Z, Pastor K. Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms. Viruses 2023;15(7):1522 View
  65. Zapata R, Huang S, Morris E, Wang C, Harle C, Magoc T, Mardini M, Loftus T, Modave F, Yon D. Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records. PLOS ONE 2023;18(10):e0292888 View
  66. Casillas N, Ramón A, Torres A, Blasco P, Mateo J. Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves. Viruses 2023;15(11):2184 View
  67. Wusu A, Olabanjo O, Mazzara M. Estimating the Basic Reproduction Number for the Second Wave of Covid-19 Pandemic in Nigeria. International Journal of Pure and Applied Mathematics Research 2022;2(2):1 View
  68. Djeddi W, Hermi K, Ben Yahia S, Diallo G. Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining. BMC Bioinformatics 2023;24(1) View
  69. Lyu S, Adegboye O, Adhinugraha K, Emeto T, Taniar D. Unlocking Insights: Analysing COVID-19 Lockdown Policies and Mobility Data in Victoria, Australia, through a Data-Driven Machine Learning Approach. Data 2023;9(1):3 View
  70. Shiferaw K, Wali P, Waltemath D, Zeleke A. Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling. Frontiers in Cardiovascular Medicine 2024;10 View
  71. Rakhshan S, Zaj M, Ghane F, Nejad M. Exploring the potential of learning methods and recurrent dynamic model with vaccination: A comparative case study of COVID-19 in Austria, Brazil, and China. Physical Review E 2024;109(1) View
  72. Simoncini E, Jarry A, Moussion A, Marcheschi A, Giordanino P, Lusenti C, Bruder N, Velly L, Boussen S. Predictive Modeling of COVID-19 Intensive Care Unit Patient Flows and Nursing Complexity. CIN: Computers, Informatics, Nursing 2024 View
  73. Indolfi C, Klain A, Dinardo G, D’Addio E, Ferrara S, Decimo F, Ciprandi G, Tosca M, Miraglia del Giudice M. COVID-19 Pediatric Follow-Up: Respiratory Long COVID-Associated Comorbidities and Lung Ultrasound Alterations in a Cohort of Italian Children. Children 2024;11(2):166 View
  74. Ramón A, Bas A, Herrero S, Blasco P, Suárez M, Mateo J. Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach. Journal of Clinical Medicine 2024;13(7):1837 View
  75. Kuziemsky C, Chrimes D, Minshall S, Mannerow M, Lau F. AI Quality Standards in Health Care: Rapid Umbrella Review. Journal of Medical Internet Research 2024;26:e54705 View
  76. Tariq M, Ismail S. AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods. Osong Public Health and Research Perspectives 2024;15(2):115 View
  77. El-Saleh A, Sheikh A, Albreem M, Honnurvali M. The Internet of Medical Things (IoMT): opportunities and challenges. Wireless Networks 2024 View
  78. Sadeghi S, Hempel L, Rodemund N, Kirsten T. Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV. Scientific Reports 2024;14(1) View
  79. Barros B, Lacerda P, Albuquerque C, Conci A. Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification. Sensors 2021;21(16):5486 View
  80. Diller S, Stenzel L, Passmore J. The coach bots are coming: exploring global coaches’ attitudes and responses to the threat of AI coaching. Human Resource Development International 2024;27(4):597 View
  81. Kaliyar R, Goswami A, Narang P, Chamola V. Understanding the Use and Abuse of Social Media: Generalized Fake News Detection With a Multichannel Deep Neural Network. IEEE Transactions on Computational Social Systems 2024;11(4):4878 View
  82. Ertem Z. COVID Geliştirme Duyarlılığını Tahmin Etmek için Makine Öğrenimi Modellerinin Geliştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 2024;36(2):957 View
  83. Novack A, Cobre A, Stremel D, Ferreira L, Campos M, Pontarolo R. Development and validation of a new method by MIR-FTIR and chemometrics for the early diagnosis of leprosy and evaluation of the treatment effect. Chemometrics and Intelligent Laboratory Systems 2024;254:105248 View

Books/Policy Documents

  1. Gussain R, Kumar N, Sharma M, Dehraj P. Inventive Computation and Information Technologies. View
  2. Jaulip V, Alfred R. Proceedings of the 8th International Conference on Computational Science and Technology. View
  3. Mitra S, Arockiam P, Kulandai J, Costa M, Notts R. Lessons from COVID-19. View
  4. Klaudel B, Obuchowski A, Dąbrowska M, Sałaga-Zaleska K, Kowalczuk Z. Intelligent and Safe Computer Systems in Control and Diagnostics. View
  5. Lefèvre T, Colineaux H, Morgand C, Tournois L, Delpierre C. Artificial Intelligence in Covid-19. View
  6. Dinacci M, Chen T, Mahmud M, Parkinson S. Artificial Intelligence in Healthcare. View
  7. Chang V, Ali M, Hossain A. Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19. View
  8. Sgroi G, Parasiliti Palumbo G, Di Salvatore V, Russo G, Pappalardo F. Bioengineering and Biomedical Signal and Image Processing. View
  9. Yu F. Clinical Informatics Study Guide. View
  10. Navarro A, Hernández L, Elouali A, Mora H, Signes-Pont M. Research and Innovation Forum 2022. View
  11. Nandi A, Yadav S, Hobisyashi A, Ghosh A, Mishra S, Chaudhary V. International Conference on Innovative Computing and Communications. View
  12. Manimaran A, Goundar S, Chandramohan D, Arulkumar N. Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations. View