## 1 Introduction
Although current challenges such as cost, variability, and limited mechanical strength still exist (Table 1), the synergistic integration of these technologies offers hope for more durable, effective, and more accessible collagen-based neurosurgical implants. Artificial intelligence (AI), particularly machine learning (ML), is playing a key role in optimizing the mechanical design of tissue models [7; 8] and implants [9; 10] by analyzing large data sets on material behavior, patient anatomy, and surgical outcomes. AI can predict the mechanical performance of different collagen formulations under physiological conditions, helping researchers fine-tune properties such as tensile strength and elasticity. ML algorithms can model material degradation rates to ensure implants maintain structural integrity throughout the healing period. AI is enabling real-time feedback systems in smart implants by integrating sensors that monitor stress, strain, and pressure, moving toward human-centric, adaptive Industry 5.0 technologies. ML facilitates automated quality control during production by identifying flaws or inconsistencies in collagen structure that could affect mechanical properties. AI-based simulations can help researchers create digital prototypes of implants, dramatically reducing development time and costs while increasing mechanical precision. AI supports personalized implant design by adjusting mechanical properties based on patient-specific data such as age, injury type, and biomechanical needs. Using deep learning, scientists can uncover hidden patterns of mechanical failure, leading to more robust implant structures and predictive maintenance models. AI helps bridge data silos between materials science, biomechanics, and clinical outcomes, enabling more integrated development cycles aligned with Industry 4.0. AI enables the transition \begin{table}
\begin{tabular}{c c} \hline
**Gap/Challenge** & **Results** \\ \hline Inconsistent mechanical strength of collagen implants & Tensile and compressive properties if implants often do not match native dura mater or neural tissue \\ \hline The rate of collagen biodegradation can be & It affects long-term mechanical stability during the healing process \\ \hline Lack of standardization in manufacturing processes & Leads to batch-to-batch variability in implant strength, elasticity, and structural integrity \\ \hline Insufficient integration of smart materials & Limits their potential for Industry 4.0/5.0 \\ and sensors in current collagen-based implants & Lipids such as real-time monitoring \\ \hline Current collagen implants lack adaptive & Limited dynamical response to changing physiological loads in the neural environment \\ \hline Mechanical mismatch between collagen & Can lead to micromotion-induced inflammation or implant failure \\ \hline Limited integration of data analysis in the & Hindered optimization of mechanical properties using AI-based modeling and simulation tools \\ \hline Sensitivity of collagen to moisture and temperature fluctuations during storage & Challenges to mechanical reliability in various clinical environments \\ and handling & Hindered consistent mechanical performance in line with Industry 4.0/5.0 \\ Lack of automated quality control systems & Hindered consistent mechanical performance in line with Industry 4.0/5.0 \\ in implant manufacturing & standards \\ \hline Lack of regulatory frameworks and testing & Slows the transition to personalized \\ protocols to assess intelligent mechanical & Industry 5.0 implants \\ behavior and integration with healthcare & \\ systems & \\ \hline \end{tabular}
\end{table} 
Table 1: Main observed gaps and challenges (own elaboration). to Industry 5.0 by enabling intelligent systems that co-design implants with surgeons, ensuring optimal mechanical compatibility and improved neurosurgical outcomes [11]. Novelty and contribution of our research lies in the use of AI/ML which introduces a novel, data-driven approach to optimizing the mechanical properties of collagen implants tailored for neurosurgical applications.