Comparison of AI chatbot predicted and realworld survival outcomes in hepatocellular carcinoma


Kavak E. E., Erdat E. C., Altundağ Derin Z., Dilli İ., KUBİLAY TOLUNAY P., Öksüzoğlu B., ...Daha Fazla

Scientific Reports, cilt.15, sa.1, 2025 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1038/s41598-025-06591-9
  • Dergi Adı: Scientific Reports
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Chemical Abstracts Core, MEDLINE, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: Artificial intelligence, Hepatocellular carcinoma, Predicted overall survival, Prognostic model
  • Ankara Üniversitesi Adresli: Evet

Özet

This study compares survival predictions made by an artificial intelligence (AI) based chatbot with real-world data in hepatocellular carcinoma (HCC) patients. It aims to evaluate the reliability and accuracy of AI technologies in HCC prognosis. A retrospective analysis was conducted on patients diagnosed with HCC. The estimated survival times for each patient were calculated using an artificial intelligence chatbot. The follow-up periods and mortality data for the patients were used to obtain real-life survival data. The predicted and actual survival times were statistically compared. ChatGPT-4o consistently overestimated the overall survival (OS) times compared to real-world outcomes.A statistically significant discrepancy was observed between the predicted and actual survival times (p < 0.05). Nevertheless, while the survival predictions of AI were more accurate in patients with advanced-stage HCC, the predictions differed significantly in patients with early-stage HCC. AI has the potential to play an important role in the prognosis of complex diseases such as HCC. However, this study’s findings indicate that AI’s predictions are not entirely consistent with real-world data, particularly in the context of early-stage diseases. Further large-scale studies may enhance the dependability of incorporating AI into clinical decision-support systems. The use of AI-assisted predictions may prove to be a valuable tool for the prediction of survival in patients with HCC. Nevertheless, further studies are required to substantiate the accuracy and reliability of these systems before they can be implemented in clinical practice.