Background: Multidisciplinary Tumor Councils (MDTs) are vital platforms that provide tailored treatment plans for cancer patients by combining expertise from various medical disciplines. Recently, Artificial Intelligence (AI) tools have been investigated as decision-support systems within these councils. Methods: In this prospective study, the compatibility of AI (ChatGPT-4.0) with MDT decisions was evaluated in 100 cancer patients presented to the tumor council between November 2024 and January 2025. AI-generated treatment recommendations based on anonymized, detailed clinical summaries were compared with real-time MDT decisions. Cohen’s Kappa and Spearman correlation tests were used for statistical analysis. Results: Neoadjuvant treatment (45%) and surgery (36%) were the most frequent MDT decisions. AI recommended surgery (39%) and neoadjuvant treatment (37%) most frequently. A high concordance rate of 76.4% was observed between AI and MDT decisions (κ = 0.764 [95% CI; 0.658–0.870] p < 0.001, ρ = 0.810 [95% CI; 0.729–0.868], p < 0.001). Most inconsistencies arose in cases requiring individualized decisions, indicating AI’s current limitations in incorporating contextual clinical judgment. Conclusion: AI demonstrates substantial agreement with MDT decisions, particularly in cases adhering to standardized oncological guidelines. However, for AI integration into clinical workflows, it must evolve to interpret real-time patient data and function transparently within ethical and legal frameworks.

To compare patients that received intravenous (i.v.) analgesics with those that received transversus abdominis plane (TAP) block for pain relief after laparoscopic cholecystectomy. This retrospective study enrolled patients that had undergone laparoscopic cholecystectomy and divided them into two groups: the i.v. analgesic group (controls; group A) and the TAP block group (group T). Data retrieved from the medical records included postoperative visual analogue scale (VAS) pain scores, duration of intensive care unit (ICU) stay, total hospital stay, additional analgesic requirements and the occurrence of nausea and vomiting. A total of 515 patients were included (group A, n = 247; group T, n = 268). Postoperative VAS pain scores at 0, 2, 4 and 6 h and the need for additional analgesics were significantly lower in group T than in group A. Postoperative VAS pain scores at 12 and 24 h were significantly higher in group T than in group A. Postoperative nausea and vomiting were significantly lower in group T than in group A. The rate of ICU admission in group T was significantly lower than in group A. Effective postoperative analgesia can be achieved with TAP block and undesirable effects can be reduced.

Objectives. Diabetic foot ulcer (DFU) is one of the most important and common complication of type 2 diabetes mellitus (T2DM). A new therapy, consisting of intralesional administration of human recombinant epidermal growth factor (hrEGF), has been suggested to accelerate wound healing and prevent amputations. The effect of hrEGF on DFU treatment was investigated in this study. Materials and Methods. 20 patients with DFU were included in this study, all of whom were receiving insulin treatment for T2DM. In addition, they received intralesional therapy with a dose of 75 μgr of hrEGF, three times a week. Results. In 18 patients, complete granulation response was achieved in approximately 3,3 weeks. There were 2 cases of recurrence at 6 months after EGF treatment. Between 6 and 12 doses of epidermal growth factor were used for this study. The most common side effects were tremor, chills, pain and burning at the site of administration. Conclusions. Our study shows that intralesional administration of hrEGF in T2DM can prevent amputations in DFU and also accelerate wound healing. Thus, intralesional application of hrEGF should be an option for standard care, as a second line of treatment (given its cost-effectiveness) when appropriate.