Leveraging AI and Generative AI for Medical Device Innovation: Enhancing Custom Product Development and Patient Specific Solutions
DOI:
https://doi.org/10.52783/jns.v14.1825Keywords:
Artificial intelligence, generative AI, custom, custom products, customization, medical devices, patient specificAbstract
The integration of artificial intelligence (AI) into CAD platforms will dramatically influence the way medical devices are designed, produced, and evaluated. It will allow the creation of intelligent customization platforms for perfect natural device design, dealing with patient-specific compatibility, function, and intraoperative customization. Similar trends occurred for 3D printing and have led to the democratization of an exciting innovation. AI tools are essential to facilitate the design’s performance evaluation of these complex 4D concepts, made of a new generation of advanced soft active materials, actuators, and novel bioprinting strategies. Advanced machine learning will be used to improve predictive generative platforms' biomechanical bio-operation and bio-integration simulation, leading to the design of a novel generation of temporary sophisticated 4D custom-printed objects. Examples of these new advanced bioprinted smart active materials’ future patient-specific applications will be given for drug-printed biodegradable temporary medical devices, passively adaptive volumetric intravascular devices, and internally actuated endovascular complex were driven objects.
AI transforming medical practices, doctors, health providers, or hospitals were where patients head to receive various treatments. AI is in the process of becoming omnipresent, perceiving the patient’s symptoms and medical history and providing a proper diagnosis. In the long run, this may lead to a paradigm shift where instead of reaching out to the medical devices, the medical devices will be sent to the necessary places where the patients/people live, study, work, and relax. Therefore, increasing attention is placed on wearable or ubiquitous medical technologies exploiting generative AI tools, providing the shift from passive monitoring to active patient custom home care. Examples of how generative AI fueled the new medical devices from their idea generation and development to the next real-world applications are given as easily customizable ultrathin epidermal sensory patch, pocket essential skin care devices, and customized comfort shoe inserts.
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Ravi Kumar Vankayalapati, Dilip Valiki, Venkata Krishna Azith Teja Ganti (2025) Zero-Trust Security Models for Cloud Data Analytics: Enhancing Privacy in Distributed Systems . Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-436. DOI: doi.org/10.47363/JAICC/2025(4)415
Manikanth Sarisa , Gagan Kumar Patra , Chandrababu Kuraku , Siddharth Konkimalla , Venkata Nagesh Boddapati. (2024). Stock Market Prediction Through AI: Analyzing Market Trends With Big Data Integration . Migration Letters, 21(4), 1846–1859. Retrieved from https://migrationletters.com/index.php/ml/article/view/11245
Tulasi Naga Subhash Polineni , Kiran Kumar Maguluri , Zakera Yasmeen , Andrew Edward. (2022). AI-Driven Insights Into End-Of-Life Decision-Making: Ethical, Legal, And Clinical Perspectives On Leveraging Machine Learning To Improve Patient Autonomy And Palliative Care Outcomes. Migration Letters, 19(6), 1159–1172. Retrieved from https://migrationletters.com/index.php/ml/article/view/11497
Munjala, M. B. (2025). Harnessing the Power of Data Analytics and Business Intelligence to Drive Innovation in Biotechnology and Healthcare: Transforming Patient Outcomes through Predictive Analytics, Genomic Research, and Personalized Medicine. Cuestiones de Fisioterapia, 54(3), 2222-2235.
Sanjay Ramdas Bauskar, Chandrakanth Rao Madhavaram, Eswar Prasad Galla, Janardhana Rao Sunkara, Hemanth Kumar Gollangi (2024) AI-Driven Phishing Email Detection: Leveraging Big Data Analytics for Enhanced Cybersecurity. Library Progress International, 44(3), 7211-7224.
Aravind, R. (2024). Integrating Controller Area Network (CAN) with Cloud-Based Data Storage Solutions for Improved Vehicle Diagnostics using AI. Educational Administration: Theory and Practice, 30(1), 992-1005.
Pandugula, C., Kalisetty, S., & Polineni, T. N. S. (2024). Omni-channel Retail: Leveraging Machine Learning for Personalized Customer Experiences and Transaction Optimization. Utilitas Mathematica, 121, 389-401.
Aravind, R., & Shah, C. V. (2024). Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI. International Journal Of Engineering And Computer Science, 13(01).
9] Madhavaram, C. R., Sunkara, J. R., Kuraku, C., Galla, E. P., & Gollangi, H. K. (2024). The Future of Automotive Manufacturing: Integrating AI, ML, and Generative AI for Next-Gen Automatic Cars. In IMRJR (Vol. 1, Issue 1). Tejass Publishers. https://doi.org/10.17148/imrjr.2024.010103
Korada, L. (2024). Use Confidential Computing to Secure Your Critical Services in Cloud. Machine Intelligence Research, 18(2), 290-307.
Jana, A. K., & Saha, S. (2024, July). Comparative Performance analysis of Machine Learning Algorithms for stability forecasting in Decentralized Smart Grids with Renewable Energy Sources. In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET (pp. 1-7). IEEE.
[Eswar Prasad G, Hemanth Kumar G, Venkata Nagesh B, Manikanth S, Kiran P, et al. (2023) Enhancing Performance of Financial Fraud Detection Through Machine Learning Model. J Contemp Edu Theo Artific Intel: JCETAI-101.
Jana, A. K., Saha, S., & Dey, A. DyGAISP: Generative AI-Powered Approach for Intelligent Software Lifecycle Planning.
Korada, L. (2024). GitHub Copilot: The Disrupting AI Companion Transforming the Developer Role and Application Lifecycle Management. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-365. DOI: doi. org/10.47363/JAICC/2024 (3), 348, 2-4.
Paul, R., & Jana, A. K. Credit Risk Evaluation for Financial Inclusion Using Machine Learning Based Optimization. Available at SSRN 4690773.
Korada, L. (2024). Data Poisoning-What Is It and How It Is Being Addressed by the Leading Gen AI Providers. European Journal of Advances in Engineering and Technology, 11(5), 105-109.
Jana, A. K., & Paul, R. K. (2023, November). xCovNet: A wide deep learning model for CXR-based COVID-19 detection. In Journal of Physics: Conference Series (Vol. 2634, No. 1, p. 012056). IOP Publishing.
Korada, L. Role of Generative AI in the Digital Twin Landscape and How It Accelerates Adoption. J Artif Intell Mach Learn & Data Sci 2024, 2(1), 902-906.
Jana, A. K., & Paul, R. K. (2023, October). Performance Comparison of Advanced Machine Learning Techniques for Electricity Price Forecasting. In 2023 North American Power Symposium (NAPS) (pp. 1-6). IEEE.
Mandala, V., & Mandala, M. S. (2022). ANATOMY OF BIG DATA LAKE HOUSES. NeuroQuantology, 20(9), 6413.
Sunkara, J. R., Bauskar, S. R., Madhavaram, C. R., Galla, E. P., & Gollangi, H. K. (2023). Optimizing Cloud Computing Performance with Advanced DBMS Techniques: A Comparative Study. In Journal for ReAttach Therapy and Developmental Diversities. Green Publication.
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