Skip to main content

Advertisement

SpringerLink
Log in

A Review of Biomedical Devices: Classification, Regulatory Guidelines, Human Factors, Software as a Medical Device, and Cybersecurity

  • Review
  • Published:
Biomedical Materials & Devices Aims and scope Submit manuscript

Abstract

Biomedical devices provide a critical role in the healthcare system to positively impact patient well-being. This paper aims to provide the current classifications and subclassifications of hardware and software medical devices according to the Food and Drug Administration (FDA) guidelines. An overview of the FDA regulatory pathway for medical device development such as radiation-emitting electronic product verification, product classification database, Humanitarian Use Device (HUD), premarket approval, premarket notification and clearance, post-market surveillance, and reclassification is provided. Current advances and the advantages of implementing human factors engineering in biomedical device development to reduce the risk of user error, product recalls and effective safe use are discussed. This paper also provides a review of evolving topics such as the Internet of Things (IoT), software as medical devices, artificial intelligence (AI), machine learning (ML), mobile medical devices, and clinical decision software support systems. A comprehensive discussion of the first FDA-approved AI-medical device for the diagnosis of diabetic retinopathy is presented. Further, potential cybersecurity-related risks associated with software-driven AI/ML and IoT medical devices are discussed with an emphasis on government regulations. Futuristic trends in biomedical device development and their implications on patient care and the healthcare system are elucidated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Copyright from [163]

Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. U.S. Food and Drugs Administration, Importing Medical Devices (2018), https://www.fda.gov/industry/importing-fda-regulated-products/importing-medical-devices#What is a medical device.

  2. World Health Organization, Medical Devices. https://www.who.int/health-topics/medical-devices#tab=tab_1

  3. C. Peña, K. Li, R. Felten, N. Ogden, M. Melkerson, An example of US Food and Drug Administration device regulation: medical devices indicated for use in acute ischemic stroke. Stroke 38(6), 1988–1992 (2007)

    Article  PubMed  Google Scholar 

  4. S.K. Gupta, Medical device regulations: a current perspective. J. Young Pharm. 8(1), 6 (2016)

    Article  Google Scholar 

  5. S. Desai, S. Parupelli, Additive manufacturing (3D printing), in Maynard's Industrial and Systems Engineering Handbook, 6th ed. (Springer, Berlin, 2022)

  6. S. Bose, S.F. Robertson, A. Bandyopadhyay, Surface modification of biomaterials and biomedical devices using additive manufacturing. Acta Biomater. 66, 6–22 (2018)

    Article  CAS  PubMed  Google Scholar 

  7. M. Olowe, S.K. Parupelli, S. Desai, A review of 3D-printing of microneedles. Pharmaceutics 14(12), 2693 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. E. Adarkwa, R. Kotoka, S. Desai, 3D printing of polymeric coatings on AZ31 Mg alloy substrate for corrosion protection of biomedical implants. Med. Devices Sensors 4(1), e10167 (2021)

    Article  CAS  Google Scholar 

  9. G. Haeberle, S. Desai, Additive manufacturing (3D printing) of thermoform tooling. Int. J. Mech. Prod. Eng 7, 1–4 (2019)

    Google Scholar 

  10. A. Aljohani, S. Desai, 3D printing of porous scaffolds for medical applications. Am. J. Eng. Appl. Sci. 11(3), 1076–1085 (2018)

    Article  Google Scholar 

  11. S.K. Parupelli, S. Desai, Understanding hybrid additive manufacturing of functional devices. Am. J. Eng. Appl. Sci. 10, 264–271 (2017). https://doi.org/10.3844/ajeassp.2017.264.271

    Article  Google Scholar 

  12. F. Aldawood, S. Desai, Additive manufacturing of compensator devices for radiation therapy, in Proceedings of the 2020 IISE Annual Conference (2020)

  13. J. McKenzie, S. Parupelli, D. Martin, S. Desai, Additive manufacturing of multiphase materials for electronics, in IIE Annual Conference. Proceedings, pp. 1133–1138 (2017)

  14. L.J. Kelly, T. Jones, Medical device classification: focus on vascular access. Br. J. Nurs. 27(14), S14–S19 (2018). https://doi.org/10.12968/bjon.2018.27.14.S14

    Article  PubMed  Google Scholar 

  15. Centre for Devices & Radiological Health. FDA, Unique device identification and the EHR-types of medical devices and examples (2013), https://slideplayer.com/slide/1480438/. Accessed 13 July 2022

  16. U.S. Food and Drug Administration-Products and Medical Procedures (2021), https://www.fda.gov/medical-devices/products-and-medical-procedures

  17. A.N. Johnson, Medical Devices & MedTech Products (2020), https://angelanjohnson.com/medicaldevices/

  18. FDA, US Food and Drugs Administration-Classify Your Medical Device (2018), https://www.fda.gov/medical-devices/overview-device-regulation/classify-your-medical-device

  19. US Food and Drug Administration (FDA), Consumers (Medical Device), 2017. https://www.fda.gov/medical-devices/consumers-medical-devices/learn-if-medical-device-has-been-cleared-fda-marketing

  20. A.V. Kaplan, D.S. Baim, J.J. Smith, D.A. Feigal, M. Simons, D. Jefferys, T.J. Fogarty, R.E. Kuntz, M.B. Leon, Medical device development: from prototype to regulatory approval. Circulation 109, 3068–3072 (2004)

    Article  PubMed  Google Scholar 

  21. L.H. Monsein, Primer on medical device regulation. Part II. Regulation of medical devices by the US Food and Drug Administration. Radiology 205, 10–18 (1997)

    Article  CAS  PubMed  Google Scholar 

  22. L. Keutzer, U.S.H. Simonsson, Medical device apps: an introduction to regulatory affairs for developers. JMIR mHealth uHealth 8(6), e17567 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  23. B. Zhang, S.B. Shankara, J. Guo, H. Zhang, Pivotal clinical trials with patient-reported outcome measures in premarket approval applications for high-risk medical devices from 2005 to 2018: review, examples, and regulatory considerations. Contemp. Clin. Trials 116, 106757 (2022)

    Article  PubMed  Google Scholar 

  24. J. Li, M. Stachowski, Z. Zhang, Application of responsive polymers in implantable medical devices and biosensors, in Switchable and Responsive Surfaces and Materials for Biomedical Applications (Woodhead Publishing, Cambridge, 2015), pp. 259–298

  25. R. Plowman et al., The rate and cost of hospital-acquired infections occurring in patients admitted to selected specialties of a district general hospital in England and the national burden imposed. J. Hosp. Infect. 47(3), 198–209 (2001). https://doi.org/10.1053/jhin.2000.0881

    Article  CAS  PubMed  Google Scholar 

  26. E.E. Bennett, J. VanBuren, S.L. Bratton, Presence of invasive devices and risks of healthcare-associated infections and sepsis. J. Pediatr. Intensive Care 7, 188–195 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  27. N. Buetti et al., Strategies to prevent central line-associated bloodstream infections in acute-care hospitals: 2022 update. Infect. Control Hosp. Epidemiol. 43(5), 553–569 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  28. R.B. Wilson, Y. Farooque, Risks and prevention of surgical site infection after hernia mesh repair and the predictive utility of ACS-NSQIP. J. Gastrointest. Surg. 26(4), 950–964 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  29. R.J. Pratt et al., epic2: National evidence-based guidelines for preventing healthcare-associated infections in NHS hospitals in England. J. Hosp. Infect. 65, S1–S59 (2007)

    Article  PubMed  PubMed Central  Google Scholar 

  30. G. Flodgren, L.O. Conterno, A. Mayhew, O. Omar, C.R. Pereira, S. Shepperd, Interventions to improve professional adherence to guidelines for prevention of device‐related infections. Cochrane Database Syst. Rev. (3), CD006559 (2013)

  31. D. Lebeaux, J.-M. Ghigo, C. Beloin, Biofilm-related infections: bridging the gap between clinical management and fundamental aspects of recalcitrance toward antibiotics. Microbiol. Mol. Biol. Rev. 78(3), 510–543 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  32. L. Hall-Stoodley et al., Towards diagnostic guidelines for biofilm-associated infections. FEMS Immunol. Med. Microbiol. 65(2), 127–145 (2012)

    Article  CAS  PubMed  Google Scholar 

  33. M.K. Kasliwal, L.A. Tan, V.C. Traynelis, Infection with spinal instrumentation: review of pathogenesis, diagnosis, prevention, and management. Surg. Neurol. Int. 4(Suppl 5), S392 (2013)

    PubMed  PubMed Central  Google Scholar 

  34. L. Sangkum, G.L. Liu, L. Yu, H. Yan, A.D. Kaye, H. Liu, Minimally invasive or noninvasive cardiac output measurement: an update. J. Anesth. 30(3), 461–480 (2016). https://doi.org/10.1007/s00540-016-2154-9

    Article  PubMed  Google Scholar 

  35. C. Lin, Y. Tsai, Y. Lu, J. Yang, M. Chen, Application of a novel biosensor for salivary conductivity in detecting chronic kidney disease. Biosensors 12(3), 178 (2022). https://doi.org/10.3390/bios120301782022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. W. Li, O. Auciello, R.N. Premnath, B. Kabius, Giant dielectric constant dominated by Maxwell–Wagner relaxation in Al2O3/TiO2 nanolaminates synthesized by atomic layer deposition. Appl. Phys. Lett. 96(16), 162907 (2010)

    Article  ADS  Google Scholar 

  37. R. Magjarević, Non-invasive and minimally invasive medical devices. Report, May, pp. 10–12 (2017)

  38. C.H. Chen, T.H. Tao, Y.H. Chou, Y.W. Chuang, T.B. Chen, Arteriovenous fistula flow dysfunction surveillance: early detection using pulse radar sensor and machine learning classification. Biosensors (2021). https://doi.org/10.3390/bios11090297

    Article  PubMed  PubMed Central  Google Scholar 

  39. J.F. Mooney et al., Relative value of cystatin C and creatinine-based estimates of glomerular filtration rate in predicting long-term mortality after cardiac surgery: a cohort study. BMJ Open 9(9), e029379 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  40. T.K. Chen, D.H. Knicely, M.E. Grams, Chronic kidney disease diagnosis and management: a review. JAMA 322(13), 1294–1304 (2019). https://doi.org/10.1001/jama.2019.14745

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. K.P. Bhatia et al., Consensus statement on the classification of tremors from the task force on tremor of the International Parkinson and Movement Disorder Society. Mov. Disord. 33(1), 75–87 (2018)

    Article  MathSciNet  PubMed  Google Scholar 

  42. E.D. Louis, Tremor. Contin. Lifelong Learn. Neurol. 25(4), 959–975 (2019)

    Article  Google Scholar 

  43. V. Srinivasan, V.K. Pamula, M.G. Pollack, R.B. Fair, Clinical diagnostics on human whole blood, plasma, serum, urine, saliva, sweat, and tears on a digital microfluidic platform, in Proc. MicroTAS, pp. 1287–1290 (2003)

  44. G. Gaobotse, E. Mbunge, J. Batani, B. Muchemwa, Non-invasive smart implants in healthcare: Redefining healthcare services delivery through sensors and emerging digital health technologies. Sensors Int. 3, 100156 (2022). https://doi.org/10.1016/j.sintl.2022.100156

    Article  Google Scholar 

  45. K. Dixit, S. Fardindoost, A. Ravishankara, N. Tasnim, M. Hoorfar, Exhaled breath analysis for diabetes diagnosis and monitoring: relevance, challenges and possibilities. Biosensors 11(12), 476 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. K. Lee et al., Real-world outcomes of glucose sensor use in type 1 diabetes—findings from a large UK centre. Biosensors 11(11), 457 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Graphene patches over diabetes treatment (2016), https://www.sciencedirect.com/science/article/abs/pii/S1748013216301347?via%3Dihub

  48. F.F. Franco, R.A. Hogg, L. Manjakkal, Cu2O-based electrochemical biosensor for non-invasive and portable glucose detection. Biosensors (2022). https://doi.org/10.3390/bios12030174

    Article  PubMed  PubMed Central  Google Scholar 

  49. J. Kim, A.S. Campbell, B.E.-F. de Avila, J. Wang, Wearable biosensors for healthcare monitoring. Nat. Biotechnol. 37(4), 389–406 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. R.J. Morrison et al., Regulatory considerations in the design and manufacturing of implantable 3D-printed medical devices. Clin. Transl. Sci. 8(5), 594–600 (2015). https://doi.org/10.1111/cts.12315

    Article  PubMed  PubMed Central  Google Scholar 

  51. E. Adarkwa, A. Roy, J. Ohodnicki, B. Lee, P.N. Kumta, S. Desai, 3D printing of drug-eluting bioactive multifunctional coatings for orthopedic applications. Int. J. Bioprinting 9(2), 661 (2023)

    Article  CAS  Google Scholar 

  52. S.K. Parupelli, S. Desai, Hybrid additive manufacturing (3D printing) and characterization of functionally gradient materials via in situ laser curing. Int. J. Adv. Manuf. Technol. 110, 543–556 (2020)

    Article  Google Scholar 

  53. F.K. Aldawood, S.X. Chang, S. Desai, Design and manufacture of a high precision personalized electron bolus device for radiation therapy. Med. Devices Sensors 3(6), e10077 (2020)

    Article  CAS  Google Scholar 

  54. G. Haeberle, S. Desai, Investigating rapid thermoform tooling via additive manufacturing (3D printing). Am. J. Appl. Sci 16, 238–243 (2019)

    Article  CAS  Google Scholar 

  55. J. McKenzie, S. Desai, Investigating sintering mechanisms for additive manufacturing of conductive traces. Am. J. Eng. Appl. Sci. 11(2), 652–662 (2018)

    Article  Google Scholar 

  56. S. Desai, B. Bidanda, P.J. Bártolo, Emerging trends in the applications of metallic and ceramic biomaterials, in Bio-Materials and Prototyping Applications in Medicine (Springer, Cham, 2021), pp. 1–17

  57. S. Desai, M.R. Shankar, Emerging trends in polymers, composites, and nano biomaterial applications, in Bio-Materials and Prototyping Applications in Medicine (Springer, Cham, 2021), pp. 19–34

  58. S. Desai, P. Gomes, Design for nano/micro manufacturing using a flexible decision making technique (AHP). J. Udyog Pragati 39(2), 18–25 (2015)

    Google Scholar 

  59. J. Perkins et al., Direct writing of polymeric coatings on magnesium alloy for tracheal stent applications. Ann. Biomed. Eng. 43, 1158–1165 (2015)

    Article  PubMed  Google Scholar 

  60. S. Desai, B. Harrison, Direct-writing of biomedia for drug delivery and tissue regeneration, in Printed Biomaterials: Novel Processing and Modeling Techniques for Medicine and Surgery, pp. 71–89 (2010)

  61. S. Desai, M.R. Shankar, Polymers, composites and nano biomaterials: current and future developments, in Bio-Materials and Prototyping Applications in Medicine (Springer, Cham, 2008), pp. 15–26

  62. S. Desai, B. Bidanda, Metallic and ceramic biomaterials: current and future developments, in Bio-Materials and Prototyping Applications in Medicine (Springer, Cham, 2008), pp. 1–14

  63. P. Honigmann, N. Sharma, B. Okolo, U. Popp, B. Msallem, F.M. Thieringer, Patient-specific surgical implants made of 3D printed PEEK: material, technology, and scope of surgical application. Biomed. Res. Int. 2018, 4520636 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  64. U.S. Food and Drugs Administration, Medical applications of 3D printing (2017), https://www.fda.gov/medical-devices/3d-printing-medical-devices/medical-applications-3d-printing

  65. U.S. Food and Drugs Administration, Technical considerations for additive manufactured medical devices (2018), https://www.fda.gov/regulatory-information/search-fda-guidance-documents/technical-considerations-additive-manufactured-medical-devices

  66. A.A. Raheem et al., A review on development of bio-inspired implants using 3D printing. Biomimetics 6(4), 65 (2021)

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  67. A.L. Jardini et al., Improvement in cranioplasty: advanced prosthesis biomanufacturing. Procedia CIRP 49, 203–208 (2016)

    Article  Google Scholar 

  68. X. Chen, J.K. Possel, C. Wacongne, A.F. Van Ham, P.C. Klink, P.R. Roelfsema, 3D printing and modelling of customized implants and surgical guides for non-human primates. J. Neurosci. Methods 286, 38–55 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  69. K. Phan, A. Sgro, M.M. Maharaj, P. D’Urso, R.J. Mobbs, Application of a 3D custom printed patient specific spinal implant for C1/2 arthrodesis. J. Spine Surg. 2(4), 314 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  70. A. Dzian, J. Živčák, R. Penciak, R. Hudák, Implantation of a 3D-printed titanium sternum in a patient with a sternal tumor. World J. Surg. Oncol. 16, 1–4 (2018)

    Article  Google Scholar 

  71. S. Gerke, B. Babic, T. Evgeniou, I.G. Cohen, The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. npj Digit. Med. 3(1), 1–4 (2020). https://doi.org/10.1038/s41746-020-0262-2

    Article  Google Scholar 

  72. S. Desai, M. Lovell, CFD analysis of a continuous inkjet print head for direct write fabrication, in ASME International Mechanical Engineering Congress and Exposition, vol. 43076, pp. 209–213 (2007)

  73. J. Cordeiro, S. Desai, Process parameter studies of molecular dynamics models to control substrate wettability, in International Manufacturing Science and Engineering Conference, vol. 56826, p. V001T02A025 (2015)

  74. J. Cordeiro, S. Desai, Exploring nano scale design space with molecular dynamics simulations, in IIE Annual Conference Proceedings, p. 856 (2015)

  75. S. Desai, Methods and Apparatus for Manufacturing Micro-and/or Nano-Scale Features. Google Patents, 28 Nov 2013

  76. S. Desai, M. Lovell, Modeling fluid–structure interaction in a direct write manufacturing process. J. Mater. Process. Technol. 212(10), 2031–2040 (2012)

    Article  Google Scholar 

  77. E. Adarkwa, S. Desai, Scalable droplet based manufacturing using in-flight laser evaporation. J. Nanoeng. Nanomanuf. 6(2), 87–92 (2016)

    Article  CAS  Google Scholar 

  78. S. Desai, M. Lovell, Multiphysics modeling of a piezoelectric bimorph disc in a direct write fabrication process, in ASME International Mechanical Engineering Congress and Exposition, vol. 42347, pp. 437–442 (2005)

  79. S. Desai, M. Lovell, Coupled field analysis of a piezoelectric bimorph disc within a CIJ microfabrication process, in IIE Annual Conference. Proceedings, p. 1 (2006)

  80. S. Murgu, H. Colt, Tracheobronchomalacia and excessive dynamic airway collapse. Clin. Chest Med. 34(3), 527–555 (2013)

    Article  PubMed  Google Scholar 

  81. S. Desai, J. Perkins, B.S. Harrison, J. Sankar, Understanding release kinetics of biopolymer drug delivery microcapsules for biomedical applications. Mater. Sci. Eng. B 168(1–3), 127–131 (2010)

    Article  CAS  Google Scholar 

  82. S.K. Parupelli, A. Aljohani, S. Desai, S. Khanal, N. Bhattarai, Direct jet printing and characterization of calcium alginate microcapsules for biomedical applications, in IIE Annual Conference. Proceedings, pp. 300–305 (2019)

  83. S. Desai, A. Moore, B. Harrison, J. Sankar, Understanding microdroplet formations for biomedical applications, in ASME International Mechanical Engineering Congress and Exposition, vol. 48760, pp. 119–123 (2008)

  84. S. Desai, J. Sankar, A. Moore, B. Harrison, Biomanufacturing of microcapsules for drug delivery and tissue engineering applications, in IIE Annual Conference. Proceedings, p. 507 (2008)

  85. J.L. Perkins, S. Desai, B. Harrison, J. Sankar, Understanding release kinetics of calcium alginate microcapsules using drop on demand inkjet printing, in ASME International Mechanical Engineering Congress and Exposition, vol. 43871, pp. 77–82 (2009)

  86. S. Bose, K.D. Traxel, A.A. Vu, A. Bandyopadhyay, Clinical significance of three-dimensional printed biomaterials and biomedical devices. MRS Bull. 44(6), 494–504 (2019)

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  87. M. Zarek, N. Mansour, S. Shapira, D. Cohn, 4D printing of shape memory-based personalized endoluminal medical devices. Macromol. Rapid Commun. 38(2), 1–6 (2017). https://doi.org/10.1002/marc.201600628

    Article  CAS  Google Scholar 

  88. J. Li, C. Wu, P.K. Chu, M. Gelinsky, 3D printing of hydrogels: Rational design strategies and emerging biomedical applications. Mater. Sci. Eng. R Rep. 140, 100543 (2020)

    Article  Google Scholar 

  89. A. Kirillova, R. Maxson, G. Stoychev, C.T. Gomillion, L. Ionov, 4D biofabrication using shape-morphing hydrogels. Adv. Mater. 29(46), 1703443 (2017)

    Article  Google Scholar 

  90. H. Wei, Q. Zhang, Y. Yao, L. Liu, Y. Liu, J. Leng, Direct-write fabrication of 4D active shape-changing structures based on a shape memory polymer and its nanocomposite. ACS Appl. Mater. Interfaces 9(1), 876–883 (2017)

    Article  CAS  PubMed  Google Scholar 

  91. M.S. Cabrera, B. Sanders, O.J.G.M. Goor, A. Driessen-Mol, C.W.J. Oomens, F.P.T. Baaijens, Computationally designed 3D printed self-expandable polymer stents with biodegradation capacity for minimally invasive heart valve implantation: a proof-of-concept study. 3D Print. Addit. Manuf. 4(1), 19–29 (2017)

    Article  PubMed  Google Scholar 

  92. Y. Wang, H. Cui, T. Esworthy, D. Mei, Y. Wang, L.G. Zhang, Emerging 4D printing strategies for next-generation tissue regeneration and medical devices. Adv. Mater. 34(20), 2109198 (2022)

    Article  CAS  Google Scholar 

  93. U.S. Food and Drugs Administration, Laser Products and Instruments (2023), https://www.fda.gov/radiation-emitting-products/home-business-and-entertainment-products/laser-products-and-instruments.

  94. US Food and Drugs Administration, Radiation-Emitting Electronic Products (2018), https://www.fda.gov/industry/regulated-products/radiation-emitting-electronic-products. Accessed 8 Jul 2022

  95. U.S. Food and Drugs Administration, Device Classification Panels (2018), https://www.fda.gov/medical-devices/classify-your-medical-device/device-classification-panels.

  96. U.S. Food and Drugs Administration, Product Code Classification Database (2018), https://www.fda.gov/medical-devices/classify-your-medical-device/product-code-classification-database.

  97. US Food and Drug Administration, Premarket Notification 510(k), March 13 (2020), https://www.fda.gov/medical-devices/premarket-submissions-selecting-and-preparing-correct-submission/premarket-notification-510k. Accessed 8 Jul 2022

  98. US Food and Drug Administration, Postmarket Requirements (Devices) (2018), https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/postmarket-requirements-devices

  99. U.S. Food and Drugs Administration, Reclassification (2021), https://www.fda.gov/about-fda/cdrh-transparency/reclassification

  100. U.S. Food and Drugs Administration, Medical Device Accessories (2020), https://www.fda.gov/medical-devices/classify-your-medical-device/medical-device-accessories

  101. U.S. Food and Drugs Administration, CFR—Code of Federal Regulations Title 21 (2023), https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm?fr=820.3

  102. W. Gilmore, The User-Computer Interface in Process Control: A Human Factors Engineering Handbook (Elsevier, Amsterdam, 2012)

    Google Scholar 

  103. R. Gagnon et al., A user-centered evaluation of three intravenous infusion pumps, in Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 48, no. 15, pp. 1773–1777 (2004)

  104. L. Lin, K.J. Vicente, D.J. Doyle, Patient safety, potential adverse drug events, and medical device design: a human factors engineering approach. J. Biomed. Inform. 34(4), 274–284 (2001)

    Article  CAS  PubMed  Google Scholar 

  105. D. Sawyer, K.J. Aziz, C.L. Backinger, E.T. Beers, A. Lowery, S.M. Sykes, An introduction to human factors in medical devices, U.S. Department of Health and Human Services Public Health Service Food and Drug Administration Center for Devices and Radiological Health, p. 55 (1996)

  106. N.E. Schaeffer, The role of human factors in the design and development of an insulin pump. J. Diabetes Sci. Technol. 6(2), 260–264 (2012). https://doi.org/10.1177/193229681200600208

    Article  PubMed  PubMed Central  Google Scholar 

  107. J.-E. Kim, L. Kessler, Z. McCauley, I. Niiyama, L.N. Boyle, Human factors considerations in designing a personalized mobile dialysis device: an interview study. Appl. Ergon. 85, 103003 (2020)

    Article  PubMed  Google Scholar 

  108. R. North, C. Pospisil, R.J. Clukey, C.G. Parkin, Impact of human factors testing on medical device design: validation of an automated CGM sensor applicator. J. Diabetes Sci. Technol. 13(5), 949–953 (2019). https://doi.org/10.1177/1932296819831071

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. A.L. Cassano, Applying human factors to the procurement of electrosurgical medical devices: a case study. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 47(15), 1815–1819 (2003)

    Article  Google Scholar 

  110. C.J. Vincent, Y. Li, A. Blandford, Integration of human factors and ergonomics during medical device design and development: it’s all about communication. Appl. Ergon. 45(3), 413–419 (2014). https://doi.org/10.1016/j.apergo.2013.05.009

    Article  PubMed  Google Scholar 

  111. G. Ginsburg, Human factors engineering: a tool for medical device evaluation in hospital procurement decision-making. J. Biomed. Inform. 38(3), 213–219 (2005). https://doi.org/10.1016/j.jbi.2004.11.008

    Article  MathSciNet  PubMed  Google Scholar 

  112. J. Nielsen, R. Molich, Heuristic evaluation of user interfaces, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 249–256 (1990)

  113. B. Schneiderman, Designing the User Interface. Strategies for Effective Human–Computer Interaction (Addison-Wesley, Hoboken, 1992)

    Google Scholar 

  114. M.-H. Maras, Internet of Things: security and privacy implications. Int. Data Priv. Law 5(2), 99 (2015)

    Article  Google Scholar 

  115. A. Chacko, T. Hayajneh, Security and privacy issues with IoT in healthcare. EAI Endorsed Trans. Pervasive Heal. Technol. 4(14), 1–8 (2018). https://doi.org/10.4108/eai.13-7-2018.155079

    Article  Google Scholar 

  116. F.Z. Amara, M. Hemam, M. Djezzar, M. Maimor, Semantic web and Internet of Things: challenges, applications and perspectives. J. ICT Stand. 10(2), 261–292 (2022)

    Google Scholar 

  117. B. Pradhan, S. Bhattacharyya, K. Pal, IoT-based applications in healthcare devices. J. Healthc. Eng. 2021, 6632599 (2021). https://doi.org/10.1155/2021/6632599

    Article  PubMed  PubMed Central  Google Scholar 

  118. L.M. Dang, M.J. Piran, D. Han, K. Min, H. Moon, A survey on internet of things and cloud computing for healthcare. Electronics 8(7), 768 (2019)

    Article  Google Scholar 

  119. R. Hughes, Patient Safety and Quality: An Evidence-Based Handbook for Nurses (Agency for Healthcare Research and Quality, Rockville, 2008)

    Google Scholar 

  120. S.E. Bibri, Ethical implications of AMI and the IoT: risks to privacy, security, and trust, and prospective technological safeguards, in The Shaping of Ambient Intelligence and the Internet of Things (Atlantis Press, Paris, 2015), pp. 217–238

  121. D.S. Punithavathani, K. Sujatha, J.M. Jain, Surveillance of anomaly and misuse in critical networks to counter insider threats using computational intelligence. Cluster Comput. 18(1), 435–451 (2015)

    Article  Google Scholar 

  122. B. Altubaishe, S. Desai, Multicriteria decision making in supply chain management using FMEA and hybrid AHP-PROMETHEE algorithms. Sensors 23(8), 4041 (2023)

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  123. U.S. Food and Drugs Administration, Software as a medical device (SaMD) (2018), https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd

  124. US Food and Drugs Administration, Policy for device software functions and mobile medical applications. Guidance for Industry and Food and Drug Administration Staff (2019), https://www.fda.gov/media/80958/download

  125. U.S. Food and Drugs Administration, Digital Health Criteria (2018), https://www.fda.gov/medical-devices/digital-health-center-excellence/digital-health-criteria

  126. International Medical Device Regulators Forum, Software as a medical device: possible framework for risk categorization and corresponding considerations (2014), http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-140918-samd-framework-risk-categorization-141013.pdf

  127. US Food and Drugs Administration, What are examples of Software as a Medical Device? (2017) https://www.fda.gov/medical-devices/software-medical-device-samd/what-are-examples-software-medical-device

  128. Y. Fang, D. Liu, Z. Jiang, H. Wang, Monitoring of sleep breathing states based on audio sensor utilizing Mel-Scale features in home healthcare. J. Healthc. Eng. 2023, 6197564 (2023). https://doi.org/10.1155/2023/6197564

    Article  PubMed  PubMed Central  Google Scholar 

  129. E.F. Camargos, F.M. Louzada, O.T. Nóbrega, Wrist actigraphy for measuring sleep in intervention studies with Alzheimer’s disease patients: application, usefulness, and challenges. Sleep Med. Rev. 17(6), 475–488 (2013)

    Article  PubMed  Google Scholar 

  130. S.-J. Zhou et al., Measuring sleep stages and screening for obstructive sleep apnea with a wearable multi-sensor system in comparison to polysomnography. Nat. Sci. Sleep 15, 353–362 (2023). https://doi.org/10.2147/NSS.S406359

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. V. Patel, A. Chesmore, C.M. Legner, S. Pandey, Trends in workplace wearable technologies and connected-worker solutions for next-generation occupational safety, health, and productivity. Adv. Intell. Syst. 4(1), 2100099 (2022)

    Article  Google Scholar 

  132. Y. Xue, A review on intelligent wearables: Uses and risks. Hum. Behav. Emerg. Technol. 1(4), 287–294 (2019)

    Article  Google Scholar 

  133. M.C. Schall Jr., R.F. Sesek, L.A. Cavuoto, Barriers to the adoption of wearable sensors in the workplace: a survey of occupational safety and health professionals. Hum. Factors 60(3), 351–362 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  134. J. Kim et al., Simultaneous monitoring of sweat and interstitial fluid using a single wearable biosensor platform. Adv. Sci. 5(10), 1800880 (2018)

    Article  Google Scholar 

  135. A. Koh et al., A soft, wearable microfluidic device for the capture, storage, and colorimetric sensing of sweat. Sci. Transl. Med. 8(366), 1-366ra165 (2016)

    Article  Google Scholar 

  136. J. Kim et al., Non-invasive mouthguard biosensor for continuous salivary monitoring of metabolites. Analyst 139(7), 1632–1636 (2014)

    Article  CAS  PubMed  ADS  Google Scholar 

  137. F. Güder et al., Paper-based electrical respiration sensor. Angew. Chem. Int. Ed. 55(19), 5727–5732 (2016)

    Article  Google Scholar 

  138. M. Padash, C. Enz, S. Carrara, Microfluidics by additive manufacturing for wearable biosensors: a review. Sensors 20(15), 4236 (2020)

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  139. U.S. Food and Drugs Administration, Wireless Medical Device (2018), https://www.fda.gov/medical-devices/digital-health-center-excellence/wireless-medical-devices

  140. P. Hamet, J. Tremblay, Artificial intelligence in medicine. Metabolism 69, S36–S40 (2017). https://doi.org/10.1016/j.metabol.2017.01.011

    Article  CAS  Google Scholar 

  141. L. Monostori, in Artificial Intelligence BT, in CIRP Encyclopedia of Production Engineering, ed. by L. Laperrière, G. Reinhart (Springer, Berlin, 2014), pp. 47–50

  142. F. Jiang et al., Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  143. A.M. Darcy, A.K. Louie, L.W. Roberts, Machine learning and the profession of medicine. JAMA 315(6), 551–552 (2016)

    Article  CAS  PubMed  Google Scholar 

  144. H.J. Murff et al., Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 306(8), 848–855 (2011)

    Article  CAS  PubMed  Google Scholar 

  145. U.S. Food and Drugs Administration, Artificial Intelligence and Machine Learning in Software as a Medical Device (2021), https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device

  146. Y. Xu et al., Artificial intelligence: a powerful paradigm for scientific research. Innovations 2(4), 100179 (2021). https://doi.org/10.1016/j.xinn.2021.100179

    Article  MathSciNet  Google Scholar 

  147. M. Ogunsanya, S. Desai, Predictive modeling of additive manufacturing process using deep learning algorithm, in IIE Annual Conference. Proceedings, pp. 1–6 (2022)

  148. H. Elhoone, T. Zhang, M. Anwar, S. Desai, Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0. Int. J. Prod. Res. 58(9), 2841–2861 (2020)

    Article  Google Scholar 

  149. T. Akter, S. Desai, Developing a predictive model for nanoimprint lithography using artificial neural networks. Mater. Des. 160, 836–848 (2018)

    Article  CAS  Google Scholar 

  150. S. Desai, C. Dean, Y. Desai, Cyber-enabled concurrent material and process selection in a flexible design for manufacture paradigm. Int. J. Adv. Manuf. Technol. 97(5–8), 1719–1731 (2018)

    Article  Google Scholar 

  151. K.W. Johnson et al., Artificial intelligence in cardiology. J. Am. Coll. Cardiol. 71(23), 2668–2679 (2018). https://doi.org/10.1016/j.jacc.2018.03.521

    Article  PubMed  Google Scholar 

  152. A.-M. Singeap, C. Stanciu, A. Trifan, Capsule endoscopy: the road ahead. World J. Gastroenterol. 22(1), 369 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. B. Sushma, P. Aparna, Recent developments in wireless capsule endoscopy imaging: Compression and summarization techniques. Comput. Biol. Med. 149, 106087 (2022). https://doi.org/10.1016/j.compbiomed.2022.106087

    Article  Google Scholar 

  154. D. Nikolayev, M. Zhadobov, R. Sauleau, P. Karban, Antennas for ingestible capsule telemetry, in Advances in Body-Centric Wireless Communication: Applications and State-of-the-Art (IET, London, 2016), pp. 143–186

  155. P.P. Stanich, B. Kleinman, K. Betkerur, N. Mehta Oza, K. Porter, M.M. Meyer, Video capsule endoscopy is successful and effective in outpatients with implantable cardiac devices. Dig. Endosc. 26(6), 726–730 (2014)

    Article  PubMed  Google Scholar 

  156. I. Tziortziotis, F.-M. Laskaratos, S. Coda, Role of artificial intelligence in video capsule endoscopy. Diagnostics 11(7), 1192 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  157. G.S. Raju, L. Gerson, A. Das, B. Lewis, American Gastroenterological Association (AGA) Institute technical review on obscure gastrointestinal bleeding. Gastroenterology 133(5), 1697–1717 (2007)

    Article  PubMed  Google Scholar 

  158. H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008). https://doi.org/10.1016/j.cviu.2007.09.014

    Article  Google Scholar 

  159. D.K. Iakovidis, S. Tsevas, A. Polydorou, Reduction of capsule endoscopy reading times by unsupervised image mining. Comput. Med. Imaging Graph. 34(6), 471–478 (2010). https://doi.org/10.1016/j.compmedimag.2009.11.005

    Article  CAS  PubMed  Google Scholar 

  160. H.-G. Lee, M.-K. Choi, B.-S. Shin, S.-C. Lee, Reducing redundancy in wireless capsule endoscopy videos. Comput. Biol. Med. 43(6), 670–682 (2013). https://doi.org/10.1016/j.compbiomed.2013.02.009

    Article  PubMed  Google Scholar 

  161. S.C. Payne, J.B. Furness, M.J. Stebbing, Bioelectric neuromodulation for gastrointestinal disorders: effectiveness and mechanisms. Nat. Rev. Gastroenterol. Hepatol. 16(2), 89–105 (2019)

    Article  PubMed  Google Scholar 

  162. D. Miley, L.B. Machado, C. Condo, A.E. Jergens, K.-J. Yoon, S. Pandey, Video capsule endoscopy and ingestible electronics: emerging trends in sensors, circuits, materials, telemetry, optics, and rapid reading software. Adv. Devices Instrum. (2021). https://doi.org/10.34133/2021/9854040

    Article  Google Scholar 

  163. T. Nakamura, Capsule endoscopy in Japan. Dig. Endosc. 34, 76–78 (2022)

    Article  PubMed  Google Scholar 

  164. J. Park et al., Recent development of computer vision technology to improve capsule endoscopy. Clin. Endosc. 52(4), 328–333 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  165. X. Jia, M.Q.-H. Meng, Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features, in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3154–3157 (2017). https://doi.org/10.1109/EMBC.2017.8037526

  166. Y. Yuan, M.Q. Meng, Deep learning for polyp recognition in wireless capsule endoscopy images. Med. Phys. 44(4), 1379–1389 (2017)

    Article  PubMed  Google Scholar 

  167. D.K. Iakovidis, A. Koulaouzidis, Software for enhanced video capsule endoscopy: challenges for essential progress. Nat. Rev. Gastroenterol. Hepatol. 12(3), 172–186 (2015). https://doi.org/10.1038/nrgastro.2015.13

    Article  PubMed  Google Scholar 

  168. Z. Ding et al., Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology 157(4), 1044–1054 (2019)

    Article  PubMed  Google Scholar 

  169. T. Aoki et al., Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading. Dig. Endosc. 32(4), 585–591 (2020)

    Article  PubMed  Google Scholar 

  170. G. Ciuti et al., Frontiers of robotic endoscopic capsules: a review. J. Micro-bio Robot. 11, 1–18 (2016)

    Article  PubMed  Google Scholar 

  171. J.C. Norton et al., Intelligent magnetic manipulation for gastrointestinal ultrasound. Sci. Robot. 4(31), eaav7725 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  172. J. Min, Y. Yang, Z. Wu, W. Gao, Robotics in the gut. Adv. Ther. 3(4), 1900125 (2020)

    Article  Google Scholar 

  173. U.S. Food and Drugs Administration, Ingestible telemetric gastrointestinal capsule imaging system—final class ii special controls guidance document for industry and FDA (2018), https://www.fda.gov/medical-devices/guidance-documents-medical-devices-and-radiation-emitting-products/ingestible-telemetric-gastrointestinal-capsule-imaging-system-final-class-ii-special-controls

  174. B. Prabhakar, R.K. Singh, K.S. Yadav, Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device. Comput. Med. Imaging Graph. 87, 101818 (2021). https://doi.org/10.1016/j.compmedimag.2020.101818

    Article  PubMed  Google Scholar 

  175. X. Zhang et al., Prevalence of diabetic retinopathy in the United States, 2005–2008. JAMA 304(6), 649–656 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Cruise Ophthalmic, Portable eye fundus camera Digital Retinal Scan Photography. https://www.aliexpress.com/item/3256801216401596.html?gatewayAdapt=4itemAdapt

  177. Digital Diagnosis-AI the right way. https://www.digitaldiagnostics.com/products/eye-disease/idx-dr/?gclid=CjwKCAjw46CVBhB1EiwAgy6M4hNGGgKBv1kg0j_9nkFxQHp5UqBclhg-5WyP8KGTFVOLrpYWTGp7fRoCuPIQAvD_BwE

  178. F. Mahgoub, Here’s How the FDA Approval of IDx-DR Will Impact Patients (2018), https://introwellness.com/eyes/idx-dr/

  179. E.H. Nepal, Review of 3nethra Classic Digital Non-mydriatic Fundus Camera (2021), https://www.eyehealthnepal.com/3nethra-classic-fundus-camera/

  180. L. Tsang et al., The impact of artificial intelligence on medical innovation in the European Union and United States. Intellect. Prop. Technol. Law J. 29(8), 3–12 (2017)

    Google Scholar 

  181. U.S. Food and Drugs Administration, Device Software Functions Including Mobile Medical Applications (2022), https://www.fda.gov/medical-devices/digital-health-center-excellence/device-software-functions-including-mobile-medical-applications

  182. U.S. Food and Drugs Administration, Medical Device Data Systems (2019), https://www.fda.gov/medical-devices/general-hospital-devices-and-supplies/medical-device-data-systems

  183. G. Freckmann et al., Insulin pump therapy for patients with type 2 diabetes mellitus: evidence, current barriers, and new technologies. J. Diabetes Sci. Technol. 15(4), 901–915 (2021)

    Article  CAS  PubMed  Google Scholar 

  184. N.J. Wimmer et al., Assessing the cost burden of United States FDA-mandated post-approval studies for medical devices. J. Health Care Finance 2016(Spec FEATURES) (2016)

Download references

Acknowledgements

The authors acknowledge the funding from the National Science Foundation (NSF) #1663128, #2100739, #2100850, #2200538. We would like to thank the Center of Excellence in Product Design and Advanced Manufacturing (CEPDAM), North Carolina A & T State University.

Funding

The authors acknowledge the funding from the National Science Foundation (NSF) #1663128, #2100739, #2100850, #2200538.

Author information

Authors and Affiliations

  1. Industrial and Systems Engineering Department, North Carolina Agricultural and Technical State University, Greensboro, NC, 27411, USA

    Felix Tettey, Santosh Kumar Parupelli & Salil Desai

  2. Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC, 27411, USA

    Felix Tettey, Santosh Kumar Parupelli & Salil Desai

Authors
  1. Felix Tettey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santosh Kumar Parupelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salil Desai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the composition of the manuscript. The final version of the manuscript has been approved by all authors.

Corresponding author

Correspondence to Salil Desai.

Ethics declarations

Competing interests

There are no competing interests for this review article.

Ethical Approval

All the information presented is cited appropriately and relevant literature in support of the claims made.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tettey, F., Parupelli, S.K. & Desai, S. A Review of Biomedical Devices: Classification, Regulatory Guidelines, Human Factors, Software as a Medical Device, and Cybersecurity. Biomedical Materials & Devices 2, 316–341 (2024). https://doi.org/10.1007/s44174-023-00113-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s44174-023-00113-9

Keywords

Search

Navigation