Introduction: 3-dimensional (3D) printing and augmented reality (AR) are emerging technologies that are used in a wide variety of scientific fields. Among them, medicine is one of the most promising fields of application since these technologies can benefit not only surgeons, but also medical/surgical trainees, patients and can potentially benefit health care systems with better educated staff working on personalized solutions for the patients. Thus, potentially reducing intra-operative and post operative complications and overall costs for the health care systems. Hepatic malignancy surgeries are some of the most demanding surgeries that could a general surgeon perform. The intra-operative and post-operative risks and complications render them demanding. In literature there are cases of research studies including applications of 3D printing and augmented reality in hepatic malignancies. Methods: For this, a comprehensive literature search was conducted on Scopus and Pubmed databases (latest search September 5, 2024). Research studies that included applications of 3D printing and AR in hepatic malignancies were eligible for the review. Results: Herein, twelve papers have been included and presented, which either include the use of 3D printing or the use of AR. There are some cases where both technologies were used simultaneously. 3D printing technology and AR can be used alone or in combination together to aid in the management of hepatic malignancies. Conclusion: Encouraging results (eg, efforts to reduce cost of 3D printing, proper surgical pre-planning, usefulness in education of medical personnel and patients) from the use of these technologies, not only qualitatively but also quantitatively, show that the medical staff can help patients and improve their part of the health system. Yet much more studies need to validate whether the use of these two technologies provides positive results on the surgeries or not.

3D printing can produce intuitive, precise, and personalized anatomical models, providing invaluable support for precision medicine, particularly in areas like surgical training and preoperative planning. However, conventional 3D printed models are often significantly more rigid than human organs and cannot undergo repetitive resection, which severely restricts their clinical value. Here we report the stereolithographic 3D printing of personalized liver models based on physically crosslinked self-healing elastomers with liver-like softness. Benefiting from the short printing time, the highly individualized models can be fabricated immediately following enhanced CT examination. Leveraging the high-efficiency self-healing performance, these models support repetitive resection for optimal trace through a trial-and-error approach. At the preliminary explorative clinical trial (NCT06006338), a total of 5 participants are included for preoperative planning. The primary outcomes indicate that the negative surgery margins are achieved and the unforeseen injuries of vital vascular structures are avoided. The 3D printing of liver models can enhance the safety of hepatic surgery, demonstrating promising application value in clinical practice.

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

With the continuous development of digital medicine, minimally invasive precision and safety have become the primary development trends in hepatobiliary surgery. Due to the specificity and complexity of hepatobiliary surgery, traditional preoperative imaging techniques such as computed tomography and magnetic resonance imaging cannot meet the need for identification of fine anatomical regions. Imaging-based three-dimensional (3D) reconstruction, virtual simulation of surgery and 3D printing optimize the surgical plan through preoperative assessment, improving the controllability and safety of intraoperative operations, and in difficult-to-reach areas of the posterior and superior liver, assistive robots reproduce the surgeon's natural movements with stable cameras, reducing natural vibrations. Electromagnetic navigation in abdominal surgery solves the problem of conventional surgery still relying on direct visual observation or preoperative image assessment. We summarize and compare these recent trends in digital medical solutions for the future development and refinement of digital medicine in hepatobiliary surgery.

To evaluate the effectiveness of a three-dimensional (3D) printed transparent kidney model as a surgical navigator for robot-assisted partial nephrectomy (RPN) in patients with complex renal tumours, defined by a R.E.N.A.L. (Radius, Exophytic/Endophytic, Nearness, Anterior/Posterior, Location) nephrometry score of ≥7.

A total of 80 patients who underwent RPN were included in the present prospective case-matched study (case group [n = 40, application of 3D-printed transparent kidney model during RPN] vs matching group [n = 40, routine protocol]). The RPNs were performed by a single experienced surgeon. The RPN procedure consisted of six steps: (i) preparation of the renal hilar vessel for clamping, (ii) tumour detection and dissection, (iii) robotic ultrasonography, (iv) tumour resection, (v) calyx repair and haemostasis, and (vi) renorrhaphy. The time for each step, console time, and warm ischaemia time were compared between the two groups as a surrogate marker for surgical effectiveness.

Both groups were well-balanced for all baseline characteristics. The use of the model reduced the console time by ~20% compared to the matched group (64.6 vs 78.5 min, P = 0.001). On multivariate logistic regression analysis, tumour radius (P < 0.001) and application of the model (P = 0.009) were identified as significant predictors of a console time of ≤70 min.

We established the usefulness of a personalised 3D-printed transparent kidney model for more effective RPNs. Use of the 3D-printed transparent kidney model reduced the operative time even for complex renal tumours and would be expected to broaden the indications for PN.