## 3 Results  
### Results of Bibliometric Analysis  
The selected set of publications was then further refined (see Figure 1) by manually re-screening the articles, removing irrelevant items and duplicates, which allowed for determining the final sample size.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameter** & **Description** \\ \hline Inclusion criteria & Articles (original, reviews, communication, editorials) and chapters, including conference proceedings, in English \\ \hline Exclusion criteria & Books older than 10 years, letters, conference abstracts without full text, other languages than English \\ \hline Keywords used & artificial intelligence, additive manufacturing, 3D printing, rehabilitation, physiotherapy OR physical therapy, personalisation OR personalization \\ \hline Used field codes (WoS) & “Subject” field (consisting of title, abstract, keyword plus, and other keywords) \\ \hline Used field codes (Sopus) & article title, abstract, and keywords \\ \hline Used field codes (dblp, PubMed) & manually \\ \hline Boolean operators used & Yes, e.g., “3D printing” AND (“personalization” OR “personalisation”) AND “rehabilitation” \\ \hline Applied filters & Results refined by publication year, document type (e.g., articles, reviews), and subject area (e.g., medicine, industry, engineering) \\ \hline Iteration and validation options & Query run iteratively, refinement based on the results, and validation by ensuring relevant articles appear among the top hits \\ \hline Leverage truncation and wildcards used & Used symbols like * for word variations (e.g., “3D print*”) and? for alternative spellings (e.g., “personali?ation”) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed search query.  
The summary of the bibliographic analysis results is presented in Table 3 and Figure 2a,b. The review included 21 articles (2001-2025) [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52] published in the last 25 years (no older ones were included). The large number of articles that were excluded from the review is due to the fact that they belonged to other medical disciplines, and the articles only mentioned rehabilitation and/or physiotherapy, as well as articles that did not address the issue of clinical applications, but only their remote possibilities (in terms of time, costs, or implementation).  
Figure 1: PRISMA flow diagram of the review process.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter/Feature** & **Value(s)** \\ \hline Leading types of publication & Article (71.77\%), review (11.31\%), proceeding paper (2.92\%), book \\  & chapter (0.42\%) * \\ \hline Leading areas of science & Health sciences (42.26\%), Engineering (27.41\%), \\  & Materials Science (12.28\%), Multidisciplinary sciences (1.61\%) \\ \hline Leading topics & Rehabilitation, Industrial: Design and Manufacturing \\ \hline Leading countries & USA, Australia \\ \hline Leading scientists & None \\ \hline Leading affiliations & None \\ \hline Leading funders (where information available) & Australian government, National Institutes of Health NIH USA \\ \hline Sustainable development goals & Good health and well-being, Industry innovation and infrastructure, \\  & Reduced inequality, Sustainable cities and communities \\ \hline \hline \end{tabular}  
* The remaining 13.58\% did not have a specific publication type in the databases and it was not possible for experts to determine this manually.  
\end{table}
Table 3: Summary of results of bibliographic analysis (WoS, Scopus, dblp, PubMed).  
The key perspective areas of introducing AI to the personalized AM of solutions for rehabilitation and physiotherapy are shown in Table 4.  
The most important AI techniques and methods supporting the 3D printing of solutions for rehabilitation and physiotherapy are shown in Table 5.