Références Scientifiques

L'IA au service du pneumo-allergologue

Congrès Marrakech Allergologie et Immunologie Clinique - Samedi 01 Novembre 2025

I. Intelligence Artificielle en Médecine Respiratoire (Références 1-6)

[1] Mekov E, Miravitlles M, Petkov R. Artificial intelligence and machine learning in respiratory medicine. Expert Rev Respir Med. 2020;14(6):559-564.
[2] Karthika M, et al. Artificial intelligence in respiratory care. Front Digit Health. 2024;6:1502434.
[3] Al-Anazi S, et al. Artificial intelligence in respiratory care: Current scenario and future perspectives. PMC. 2024;11100474.
[4] Kaplan A, Cao H, FitzGerald JM, et al. Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis. J Allergy Clin Immunol Pract. 2021;9(6):2255-2263.
[5] Gonem S, Janssens W, Das N, Topalovic M. Applications of artificial intelligence and machine learning in respiratory medicine. Thorax. 2020;75(8):695-701.
[6] Antão J, et al. Demystification of artificial intelligence for respiratory medicine. Expert Rev Respir Med. 2024;18(1):1-12.

II. Imagerie Thoracique et Deep Learning (Références 7-11)

[7] Kim JY, et al. Better performance of deep learning pulmonary nodule detection. Sci Rep. 2024;14:15858.
[8] Cui X, et al. Performance of a deep learning-based lung nodule detection system. Eur J Radiol. 2022;145:110010.
[9] Hillis JM, Bizzo BC, Mercaldo S, et al. Evaluation of an artificial intelligence model for detection of pneumothorax and tension pneumothorax in chest radiographs. JAMA Netw Open. 2022;5(12):e2247172.
[10] Lind Plesner L, et al. Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion. Radiology. 2023;308(3):e231236.
[11] Jiang B, Li N, Shi X, et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology. 2022;303(1):202-212.

III. Intelligence Artificielle en Allergologie (Références 12-14)

[12] Indolfi C, et al. Artificial intelligence in the transition of allergy: a valuable tool from childhood to adulthood. Front Med. 2024;11:1469161.
[13] González-Díaz SN, et al. Artificial intelligence in allergy practice: Digital transformation and the future of clinical care. World Allergy Organ J. 2025.
[14] van Breugel M, et al. Artificial intelligence in allergy and immunology: Recent developments, implementation challenges, and the road toward clinical impact. J Allergy Clin Immunol. 2025.

IV. Prédiction des Exacerbations Asthmatiques (Références 15-18)

[15] Molfino NA, Turcatel G, Riskin D. Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review. Adv Ther. 2024;41(2):534-552.
[16] Zein JG, Wu CP, Attaway AH, et al. Novel machine learning can predict acute asthma exacerbation. Chest. 2021;159(5):1747-1757.
[17] Finkelstein J, Jeong IC. Machine learning approaches to personalize early prediction of asthma exacerbations. Ann N Y Acad Sci. 2017;1387(1):153-165.
[18] Sagheb E, Wi CI, King KS, Kshatriya BSA, Ryu E, Liu H, Park MA, Seol HY, Overgaard SM, Sharma DK, Juhn YJ, Sohn S. AI model for predicting asthma prognosis in children. J Allergy Clin Immunol Glob. 2025;4(2):100429.

V. Éthique et Intelligence Artificielle (Références 19-21)

[19] Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018;378(11):981-983.
[20] Beauchamp TL, Childress JF. Principles of Biomedical Ethics. 8th ed. Oxford University Press; 2019. ISBN: 9780190640873
[21] Morley J, Machado CCV, Burr C, et al. The ethics of AI in health care: A mapping review. Soc Sci Med. 2020;260:113172.

VI. Médecine Personnalisée et Médecine des 4P (Références 22-23)

[22] Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol. 2011;8(3):184-187.
[23] Flores M, Glusman G, Brogaard K, Price ND, Hood L. P4 medicine: how systems medicine will transform the healthcare sector and society. Per Med. 2013;10(6):565-576.

VII. Épreuves Fonctionnelles Respiratoires et IA (Références 24-26)

[24] Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, Janssen R, Kerstjens HAM, Liistro G, Louis R, Ninane V, Pison C, Schlesser M, Vercauter P, Vogelmeier CF, Wouters E, Wynants J, Janssens W; Pulmonary Function Study Investigators. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J. 2019;53(4):1801660.
[25] Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease: Current status and future potential. Curr Opin Pulm Med. 2018;24(2):117-123.
[26] ArtiQ - Quality control automation for spirometry.

VIII. Recommandations Internationales (Référence 27)

[27] GINA 2024. Global Strategy for Asthma Management and Prevention.

IX. Outils et Technologies d'IA en Pneumo-Allergologie (Références 28-32)

[28] VEYE Lung Nodules - Aidence. Deep learning for lung nodule detection.
[29] GLEAMER - AP-HP Partnership. AI for medical imaging.
[30] Hippo DX - S.P.A.T. System (Skin Prick Automated Testing). Automated skin prick testing.
[31] MASK-air (Mobile Airways Sentinel Network). Mobile application for allergic rhinitis and asthma.
[32] NIOX. FeNO measurement devices.

X. Sources de Prix et Coûts des Technologies (Références 33-35)

[33] Medical Device Depot (USA). Spirometry and respiratory equipment pricing.
[34] Medisave UK. Medical devices and equipment.
[35] Fisher Scientific. Laboratory and medical equipment.