Subscribe to RSS
Please copy the URL and add it into your RSS Feed Reader.
https://www.thieme-connect.de/rss/thieme/en/10.1055-s-00043364.xml
Download PDF
CC BY 4.0 · Pharmaceutical Fronts 2025; 07(03): e158-e167
DOI: 10.1055/a-2652-0081
DOI: 10.1055/a-2652-0081
Review Article
Application and Development of Large Language Models in Smart Inhalers
Funding None.
Abstract
The emergence of generative artificial intelligence and Large Language Models (LLMs) has brought revolutionary applications in the medical field, especially in the field of smart inhalers, where LLMs show great potential. LLMs can optimize the functionality of smart inhalers, enhance patient education and feedback mechanisms, and support personalized medical decision-making through natural language processing and deep data analysis. However, the application of these technologies also presents numerous challenges. This paper systematically reviews the prospective applications of LLMs in smart inhalers, discusses the advantages of LLMs in improving patient experience, optimizing medical processes, and facilitating data-driven decision-making, and analyzes the current technical barriers and obstacles. The article envisions the future development of LLMs in smart inhalers, advocating for multidisciplinary collaboration to fully harness their potential while effectively addressing associated risks, thereby advancing medical services toward greater intelligence, personalization, and efficiency.
Keywords
large language models - smart inhalers - patient education - data privacy - medical decision-makingPublication History
Received: 24 January 2025
Accepted: 09 July 2025
Article published online:
18 August 2025
© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
-
References
- 1 Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 2006; 3 (11) e442
- 2 Labiris NR, Dolovich MB. Pulmonary drug delivery. Part I: physiological factors affecting therapeutic effectiveness of aerosolized medications. Br J Clin Pharmacol 2003; 56 (06) 588-599
- 3 Vallorz E, Sheth P, Myrdal P. Pressurized metered dose inhaler technology: manufacturing. AAPS PharmSciTech 2019; 20 (05) 177
- 4 Hickey AJ. Dry powder inhalers: an overview. J Aerosol Med Pulm Drug Deliv 2023; 36 (06) 316-323
- 5 Martin AR, Finlay WH. Nebulizers for drug delivery to the lungs. Expert Opin Drug Deliv 2015; 12 (06) 889-900
- 6 Newman SP. Drug delivery to the lungs: challenges and opportunities. Ther Deliv 2017; 8 (08) 647-661
- 7 Haughney J, Price D, Kaplan A. et al. Achieving asthma control in practice: understanding the reasons for poor control. Respir Med 2008; 102 (12) 1681-1693
- 8 Melani AS, Bonavia M, Cilenti V. et al; Gruppo Educazionale Associazione Italiana Pneumologi Ospedalieri. Inhaler mishandling remains common in real life and is associated with reduced disease control. Respir Med 2011; 105 (06) 930-938
- 9 Pritchard JN, Nicholls C. Emerging technologies for electronic monitoring of adherence, inhaler competence, and true adherence. J Aerosol Med Pulm Drug Deliv 2015; 28 (02) 69-81
- 10 Lavorini F. Inhaled drug delivery in the hands of the patient. J Aerosol Med Pulm Drug Deliv 2014; 27 (06) 414-418
- 11 Teva. Teva launches two digital inhalers in the U.S., AirDuo Digihaler (fluticasone propionate and salmeterol) inhalation powder and ArmonAir Digihaler (fluticasone propionate) inhalation powder. Accessed January 21, 2025 at: https://www.tevapharm.com/news-and-media/latest-news/teva-launches-two-digital-inhalers-in-the-u.s.-airduo-digihaler-fluticasone-propionate-and-salmeterol/
- 12 Propeller Health. Get back to doing the things you love. Accessed January 21, 2025 at: https://propellerhealth.com/
- 13 Adherium. Adherium Limited is an international respiratory ehealth company focused on patient adherence, remote monitoring and data management solutions for patients, physicians, payers, and providers. Accessed January 21, 2025 at: https://adherium.com/
- 14 Aptar. Aptar Pharma Launches HeroTracker Sense. Accessed January 21, 2025 at: https://aptar.com/news-events/aptar-pharma-launches-herotracker-sense-smart-connected-healthcare-device/
- 15 Cognita Labs. Your personal inhaler guide that goes wherever you do!. Accessed January 21, 2025 at: https://capmedicinhaler.com/product/?srsltid=AfmBOooNUNSpMjcdvURb6XK-4TTCT0JLLDH2Jc_9NGsPcxCQmrpvJHEh
- 16 FindAir. Smart inhalers that make remote asthma & COPD care a reality. Accessed January 21, 2025 at: https://findair.eu/
- 17 Amiko. Upgrading respiratory care with digital medicines. Accessed January 21, 2025 at: https://amiko.io/
- 18 Häußermann S, Arendsen LJ, Pritchard JN. Smart dry powder inhalers and intelligent adherence management. Adv Drug Deliv Rev 2022; 191: 114580
- 19 Manickam P, Mariappan SA, Murugesan SM. et al. Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors (Basel) 2022; 12 (08) 562
- 20 Sulaiman I, Greene G, MacHale E. et al. A randomised clinical trial of feedback on inhaler adherence and technique in patients with severe uncontrolled asthma. Eur Respir J 2018; 51 (01) 1701126
- 21 Taylor TE, Holmes MS, Sulaiman I, Costello RW, Reilly RB. Monitoring inhaler inhalations using an acoustic sensor proximal to inhaler devices. J Aerosol Med Pulm Drug Deliv 2016; 29 (05) 439-446
- 22 Braido F, Paa F, Ponti L. et al. A new tool for inhalers' use and adherence monitoring: the Amiko validation trial. Int J Eng Res Sci 2016; 2: 159-166
- 23 Pothirat C, Chaiwong W, Phetsuk N, Pisalthanapuna S, Chetsadaphan N, Choomuang W. Evaluating inhaler use technique in COPD patients. Int J Chron Obstruct Pulmon Dis 2015; 10: 1291-1298
- 24 Pais C, Liu J, Voigt R, Gupta V, Wade E, Bayati M. Large language models for preventing medication direction errors in online pharmacies. Nat Med 2024; 30 (06) 1574-1582
- 25 Yang JM, Chen BJ, Li RY. et al. Artificial intelligence in medical metaverse: applications, challenges, and future prospects. Curr Med Sci 2024; 44 (06) 1113-1122
- 26 Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25 (01) 44-56
- 27 Tommelein E, Mehuys E, Van Hees T. et al. Effectiveness of pharmaceutical care for patients with chronic obstructive pulmonary disease (PHARMACOP): a randomized controlled trial. Br J Clin Pharmacol 2014; 77 (05) 756-766
- 28 Hui CY, McKinstry B, Mclean S, Buchner M, Pinnock H. Assessing the technical feasibility of a flexible, integrated Internet-of-things connected for asthma (C4A) system to support self-management: a mixed method study exploring patients and healthcare professionals perspectives. JAMIA Open 2022; 5 (04) ooac110
- 29 Dierick BJH, Achterbosch M, Eikholt AA. et al. Electronic monitoring with a digital smart spacer to support personalized inhaler use education in patients with asthma: the randomized controlled OUTERSPACE trial. Respir Med 2023; 218: 107376
- 30 Liau JC, Ho CY. Intelligence IoT (Internal of Things) telemedicine health care space system for the elderly living alone. Paper presented at: 2019 IEEE Eurasia conference on biomedical engineering, healthcare and sustainability (ECBIOS); May 31–June 3, 2019; Okinawa, Japan
- 31 Attaway AH, Alshabani K, Bender B, Hatipoğlu US. The utility of electronic inhaler monitoring in COPD management: promises and challenges. Chest 2020; 157 (06) 1466-1477
- 32 Zhang Y, Liu C, Liu M. et al. Attention is all you need: utilizing attention in AI-enabled drug discovery. Brief Bioinform 2023; 25 (01) bbad467
- 33 Han W, Wan C, Shan R, Xu X, Chen G, Zhou W, Yang Y, Feng G, Li X, Yang J, Jin K, Chen Q. Evaluation of error detection and treatment recommendations in nucleic acid test reports using ChatGPT models. Clin Chem Lab Med 2025; ; 21; 63 (09) 1698-1708
- 34 Singhal K, Azizi S, Tu T. et al. Large language models encode clinical knowledge. Nature 2023; 620 (7972) 172-180
- 35 Temsah A, Alhasan K, Altamimi I. et al. DeepSeek in healthcare: revealing opportunities and steering challenges of a new open-source artificial intelligence frontier. Cureus 2025; 17 (02) e79221
- 36 Adams LC, Truhn D, Busch F. et al. Llama 3 challenges proprietary state-of-the-art large language models in radiology board-style examination questions. Radiology 2024; 312 (02) e241191
- 37 Agarwal M, Goswami A, Sharma P. Evaluating ChatGPT-3.5 and Claude-2 in answering and explaining conceptual medical physiology multiple-choice questions. Cureus 2023; 15 (09) e46222
- 38 Hugging Face. BigScience Large Open-science Open-access Multilingual Language Model. . Accessed April 20, 2025 at: https://huggingface.co/bigscience/bloom
- 39 Hugging Face. EleutherAI/GPT-NeoX-20B. . Accessed April 20, 2025 at: https://huggingface.co/EleutherAI/gpt-neox-20b
- 40 Hugging Face. DeepSeek-V3–0324. . Accessed April 20, 2025 at: https://huggingface.co/deepseek-ai/DeepSeek-V3-0324
- 41 Hugging Face. Stabilityai/StableLM-2–1_6b. . Accessed April 20, 2025 at: https://huggingface.co/EleutherAI/gpt-neox-20b
- 42 Towards Data Science. Meet M6–10 trillion parameters at 1% GPT-3′s energy cost. . Accessed April 20, 2025 at: https://towardsdatascience.com/meet-m6-10-trillion-parameters-at-1-gpt-3s-energy-cost-997092cbe5e8/
- 43 Huawei Cloud. PanGu. Accessed April 20, 2025 at: https://www.huaweicloud.com/product/pangu.html
- 44 Nvidia Developer. Using DeepSpeed and Megatron to train Megatron-Turing NLG 530B, the World's Largest and Most Powerful Generative Language Model. Accessed April 20, 2025 at: https://developer.nvidia.com/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/
- 45 DeepMind. Language modelling at scale: Gopher, ethical considerations, and retrieval. Accessed April 20, 2025 at: https://deepmind.google/discover/blog/language-modelling-at-scale-gopher-ethical-considerations-and-retrieval
- 46 Nijkamp E, Pang B, Hayashi H. et al. CodeGen: an open large language model for code with multi-turn program synthesis. arXiv. Preprint. February 27, 2023. Accessed April 20, 2025 at: https://doi.org/10.48550/arXiv.2203.13474
- 47 AI Mode. What is ERNIE 4.0?. Accessed April 20, 2025 at: https://aimode.co/model/ernie-4/
- 48 Hugging Face. THUDM/GLM-10b-chinese. . Accessed April 20, 2025 at: https://huggingface.co/THUDM/glm-10b-chinese
- 49 Qwen. Qwen 2.5-Max: Free AI Chat. Accessed April 20, 2025 at: https://qwen.org/
- 50 Blalock SJ, Solow EB, Reyna VF. et al. Enhancing patient understanding of medication risks and benefits. Arthritis Care Res (Hoboken) 2022; 74 (01) 142-150
- 51 Sezgin E, Chekeni F, Lee J, Keim S. Clinical accuracy of large language models and Google search responses to postpartum depression questions: cross-sectional study. J Med Internet Res 2023; 25: e49240
- 52 Wang X. New strategies of clinical precision medicine. Clin Transl Med 2022; 12 (02) e135
- 53 Flaharty KA, Hu P, Hanchard SL. et al. Evaluating large language models on medical, lay-language, and self-reported descriptions of genetic conditions. Am J Hum Genet 2024; 111 (09) 1819-1833
- 54 He Z, Bhasuran B, Jin Q. et al. Quality of answers of generative large language models versus peer users for interpreting laboratory test results for lay patients: evaluation study. J Med Internet Res 2024; 26: e56655
- 55 Meskó B. Prompt engineering as an important emerging skill for medical professionals: tutorial. J Med Internet Res 2023; 25: e50638
- 56 Javan R, Cole J, Hsiao S, Cronquist B, Monfared A. Integration of AI-generated images in clinical otolaryngology. Cureus 2024; 16 (08) e68313
- 57 Hakizimana A, Devani P, Gaillard EA. Current technological advancement in asthma care. Expert Rev Respir Med 2024; 18 (07) 499-512
- 58 Greene G, Costello RW. Personalizing medicine - could the smart inhaler revolutionize treatment for COPD and asthma patients?. Expert Opin Drug Deliv 2019; 16 (07) 675-677
- 59 Moser D, Bender M, Sariyar M. Generating synthetic healthcare dialogues in emergency medicine using large language models. Stud Health Technol Inform 2024; 321: 235-239
- 60 Rathje S, Mirea DM, Sucholutsky I, Marjieh R, Robertson CE, Van Bavel JJ. GPT is an effective tool for multilingual psychological text analysis. Proc Natl Acad Sci U S A 2024; 121 (34) e2308950121
- 61 Xu Y, Liu X, Cao X. et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation (Camb) 2021; 2 (04) 100179
- 62 Eberhardt J, Bilchik A, Stojadinovic A. Clinical decision support systems: potential with pitfalls. J Surg Oncol 2012; 105 (05) 502-510
- 63 Abid M, Schneider AB. Clinical informatics and the electronic medical record. Surg Clin North Am 2023; 103 (02) 247-258
- 64 Mosnaim GS, Greiwe J, Jariwala SP, Pleasants R, Merchant R. Digital inhalers and remote patient monitoring for asthma. J Allergy Clin Immunol Pract 2022; 10 (10) 2525-2533
- 65 Miller T. Explanation in artificial intelligence: insights from the social sciences. Artif Intell 2019; 267: 1-38
- 66 Xu X, Chen Y, Miao J. Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review. J Educ Eval Health Prof 2024; 21: 6
- 67 Tian S, Jin Q, Yeganova L. et al. Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Brief Bioinform 2024; 25 (10) bbad493
- 68 Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: techniques and applications. Comput Biol Med 2023; 158: 106848
- 69 Kurniawan H, Mambo M. Homomorphic encryption-based federated privacy preservation for deep active learning. Entropy (Basel) 2022; 24 (11) 1545
- 70 Kumar R, Kumar J, Khan AA. et al. Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images. Comput Med Imaging Graph 2022; 102: 102139
- 71 Betzler BK, Chen H, Cheng CY. et al. Large language models and their impact in ophthalmology. Lancet Digit Health 2023; 5 (12) e917-e924
- 72 Tan TE, Anees A, Chen C. et al. Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study. Lancet Digit Health 2021; 3 (05) e317-e329
- 73 Ng WY, Tan TE, Xiao Z. et al. Blockchain technology for ophthalmology: coming of age?. Asia Pac J Ophthalmol (Phila) 2021; 10 (04) 343-347
- 74 Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI Chatbot for medicine. N Engl J Med 2023; 388 (13) 1233-1239
- 75 Arvind Barge S, Mary GI. Improving dependability with low power fault detection model for skinny-hash. PLoS One 2024; 19 (12) e0316012
- 76 Weissman G, Mankowitz T, Kanter G. Large language model non-compliance with FDA guidance for clinical decision support devices. Res Sq 2024; rs.3.rs-4868925
- 77 Temsah MH, Jamal A, Alhasan K, Temsah AA, Malki KH. OpenAI o1-preview vs. ChatGPT in healthcare: a new frontier in medical AI reasoning. Cureus 2024; 16 (10) e70640
- 78 Laux J. Institutionalised distrust and human oversight of artificial intelligence: towards a democratic design of AI governance under the European Union AI Act. AI Soc 2024; 39 (06) 2853-2866
- 79 Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol 2023; 18 (01) 109