BACKGROUND The current study assesses artificial intelligence methods to identify prostate carcinoma patients at low risk for lymph node spread. If patients can be assigned accurately to a low risk group, unnecessary lymph node dissections can be avoided, thereby reducing morbidity and costs. METHODS A rule‐derivation technology for simple decision‐tree analysis was trained and validated using patient data from a large database (4133 patients) to derive low risk cutoff values for Gleason sum and prostate specific antigen (PSA) level. An empiric analysis was used to derive a low risk cutoff value for clinical TNM stage. These cutoff values then were applied to 2 additional, smaller databases (227 and 330 patients, respectively) from separate institutions. RESULTS The decision‐tree protocol derived cutoff values of ≤ 6 for Gleason sum and ≤ 10.6 ng/mL for PSA. The empiric analysis yielded a clinical TNM stage low risk cutoff value of ≤ T2a. When these cutoff values were applied to the larger database, 44% of patients were classified as being at low risk for lymph node metastases (0.8% false‐negative rate). When the same cutoff values were applied to the smaller databases, between 11 and 43% of patients were classified as low risk with a false‐negative rate of between 0.0 and 0.7%. CONCLUSIONS The results of the current study indicate that a population of prostate carcinoma patients at low risk for lymph node metastases can be identified accurately using a simple decision algorithm that considers preoperative PSA, Gleason sum, and clinical TNM stage. The risk of lymph node metastases in these patients is ≤ 1%; therefore, pelvic lymph node dissection may be avoided safely. The implications of these findings in surgical and nonsurgical treatment are significant. Cancer 2000;88:2105–9. © 2000 American Cancer Society. Artificial intelligence methods can be used to predict accurately the low risk of lymph node spread in men with clinically localized prostate carcinoma.