Artificial intelligence (AI) is the study of science, engineering, and the development of intelligent machines. AI is based on human intelligence with the exception that it is not restricted by biologically observable limitations. AI has developed rapidly over the past few years and has become important all over the world. This Master’s thesis brings up AI as a medical device and the European market. The thesis provides guidance in the form of important aspects to be considered by small and medium-sized enterprises (SMEs) when marketing products in Europe. There is a lack of guidance and clear descriptions regarding AI/ML-based medical devices in Europe. Both MDR and medical devices with AI/ML are relatively new and uncharted. There are no clear guidelines, instructions, or articles that clearly describe what is needed to get an AI/ML-based medical device on the European market. In summary, there is no guidance that SMEs could benefit from when it comes to AI/ML-based medical devices and the European market. With this thesis the subject is enlightened and hopefully, the gap in knowledge about this is reduced. The chosen method to achieve the goal of this thesis is both a literature review and qualitative research in the form of interviews with relevant experts within the field. The results show that there is a lack of guidelines and regulations for AI-based medical devices, it is harder for SMEs to market such devices and it is complicated to put an AI-based medical device on the European market due to MDR. SMEs should consider certain aspects important when developing an AI/ML-based medical device and placing it on the European market. The identified aspects are creating a regulatory plan, using guidelines from example FDA, procuring regulatory competence from the start, risk classification, economics, clinical evaluation, risk management, having end-user in mind during the development, and data management/cybersecurity. The results also show that if guidelines are developed, they should contain thresholds for different characteristics in AI/ML-based medical devices, risk classification of the device, classification requirements, checklists, templates, actions, good manufacturing process description, data management, cybersecurity, patient safety process description, clinical evaluation process description, regional regulatory adaptions, and risk mitigation. The results of this thesis can be used in many ways and by many. By solely using this report for AI/ML-based medical devices, complete compliance with MDR is not fulfilled.

The value of heart rate variability (HRV) in the fields of health, disease, and exercise science has been established through numerous investigations. The typical mobile-based HRV device simply records interbeat intervals, without differentiation between noise or arrythmia as can be done with an electrocardiogram (ECG). The intent of this report is to validate a new single channel ECG device, the Movesense Medical sensor, against a conventional 12 channel ECG. A heterogeneous group of 21 participants performed an incremental cycling ramp to failure with measurements of HRV, before (PRE), during (EX), and after (POST). Results showed excellent correlations between devices for linear indexes with Pearson’s r between 0.98 to 1.0 for meanRR, SDNN, RMSSD, and 0.95 to 0.97 for the non-linear index DFA a1 during PRE, EX, and POST. There was no significant difference in device specific meanRR during PRE and POST. Bland–Altman analysis showed high agreement between devices (PRE and POST: meanRR bias of 0.0 and 0.4 ms, LOA of 1.9 to −1.8 ms and 2.3 to −1.5; EX: meanRR bias of 11.2 to 6.0 ms; LOA of 29.8 to −7.4 ms during low intensity exercise and 8.5 to 3.5 ms during high intensity exercise). The Movesense Medical device can be used in lieu of a reference ECG for the calculation of HRV with the potential to differentiate noise from atrial fibrillation and represents a significant advance in both a HR and HRV recording device in a chest belt form factor for lab-based or remote field-application.

California Polytechnic State University, San Luis Obispo is a university that encourages students to approach learning hands-on. As such, there is cutting-edge technology being developed by students in all departments on campus. Being that the university possesses an outstanding biomedical engineering department, there are groundbreaking medical devices that students are creating at Cal Poly SLO. These are devices that can better the lives of individuals suffering from ailments or fulfill needs in the medical industry. Subsequently, it is vital that these devices make it out of campus laboratories and into the hands of consumers. In order to move a product from ideation to the market, numerous steps must be completed and often times, especially with the challenges of commercializing medical devices, these efforts can result in failed product launches. As such, there is demand for a commercialization process to be created at Cal Poly SLO that will aid student created medical devices in reaching the market. This paper documents the progress made thus far on such a process at Cal Poly SLO.

Introduction: Traditional organizational routines for the innovation process, like the Stage-Gate Model, are often characterized by rigid and inflexible activities, limiting firms' ability to identify, develop, and commercialize software and digital/connected products and services. Examples of inflexible activities include predefined project milestones, linear progression through development stages, and a lack of iterative feedback loops. This rigidity can lead to prolonged development timelines, increased costs, reduced competitiveness, and the risk of losing market share. Specifically, the rigidity inherent in traditional innovation processes hampers the ability to adapt to the rapidly changing digital landscape, where swift recognition of opportunities, flexible development strategies, and repeated enhancements are key to achieving success. The literature has suggested organizational agility as a potential approach to address the consequences of rigidity. The medical device-development healthcare industry has unique characteristics. It faces challenges that increase the lead time from idea to market in innovation processes, reducing the number of innovations that are developed. Therefore, for medical device development, healthcare firms may need to understand how their innovation process routines align with organizational agility to address the unique features of their industry. Purpose: This thesis aims to understand the current organizational routines of a medical device-developing healthcare firm's innovation process and how these routines align with operational, customer, and partnering agility. Method: A single case study with 12 semi-structured interviews was conducted at a leading European medical device-developing healthcare firm, hereafter referred to as MedTech X. Findings: MedTech X's innovation process routines align with organizational agility through its customer-centric approach, adaption or modification of its extended network to access diverse knowledge, assets, or competencies, practical and iterative operations, and adaptable informal routines. However, as MedTech X further ventures into the realm of software and digital/connected solutions, there is a failure to realize the full potential of real-time customer data. Moreover, there is a recognized need to improve its partnering agility. Also, over-reliance on external resources for sensing may limit the firm's operational agility. Conclusion: Despite certain routines aligning with agility, inconsistency, and overreliance on external resources for sensing pose challenges. Therefore, reassessment and redesign of current innovation routines are advised to balance stability and flexibility. Formalizing innovation processes may be critical for MedTech X to handle unexpected changes, and informal routines play a significant role in this process, suggesting a future research direction.

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Optical Point-of-Care (POC) devices provide a low-cost platform for real-time, non-invasive diagnosis of disease and quantitative estimation of physiological biomarkers, allowing use in a wide variety of institutional settings ranging from acute surgical care to long-term clinical monitoring. POC optical value has resulted in their widespread adoption with great interest in at-home monitoring and explosive growth within wearable consumer electronics. However, recent studies have highlighted the fact that well-established devices such as pulse oximeters can exhibit subtle but dangerous inaccuracies in measurements from some darker skin pigmentation patients whose basis is not completely understood. Emerging optical technologies, such as near-infrared spectroscopy (NIRS) monitoring of bone quality are promising, yet similarly suffer from an incomplete understanding of the relationship between probe design and performance.The focus of this dissertation is to develop next-generation approaches to improve the performance of optical diagnostic devices informed by computational simulations of light-tissue interactions using Monte Carlo (MC) modeling. Although MC simulations have been previously used to design and simulate devices such as Pulse Oximeters or Transcutaneous Bilirubinometers (TcB), the simulations were incapable of capturing population-level heterogeneity and thus evaluating underlying factors contributing to measurement bias. Here, an in-silico MC platform was developed to investigate how population-level heterogeneity impacts Pulse Oximeters and TcB devices. The results demonstrate that fundamental biases in optical measurements exist and are exacerbated by inequitable regulatory guidelines. These findings were used to further demonstrate the impact of changes in regulatory guidelines that can affect measurement accuracy and clinical decision-making. Additionally, simulation results were used to inform the development of spectroscopic oximetry and demonstrate the techniques clinical feasibility and potential to improve accuracy in a human-subjects pilot study. In the case of the NIRS bone quality assessment, a lack of fundamental knowledge of tissue optical properties to allow simulations to inform relative contributions from different tissue features to the overall signal or explore optimization of device design. Studies were performed to collect previously unreported optical properties from musculoskeletal tissues, and this data was used to perform MC simulations which informed bone contribution to NIRS signals and in turn resulted in the design and preliminary characterization of next-generation fiber optic probes for real-time non-ionizing assessment of bone quality. Collectively, this dissertation demonstrates the impact of advances in MC simulations of light-tissue interaction across pressing clinically focused engineering challenges. / Bioengineering