Alejandro Lozano

All Publications

• Red teaming ChatGPT in medicine to yield real-world insights on model behavior. NPJ digital medicine Chang, C. T., Farah, H., Gui, H., Rezaei, S. J., Bou-Khalil, C., Park, Y. J., Swaminathan, A., Omiye, J. A., Kolluri, A., Chaurasia, A., Lozano, A., Heiman, A., Jia, A. S., Kaushal, A., Jia, A., Iacovelli, A., Yang, A., Salles, A., Singhal, A., Narasimhan, B., Belai, B., Jacobson, B. H., Li, B., Poe, C. H., Sanghera, C., Zheng, C., Messer, C., Kettud, D. V., Pandya, D., Kaur, D., Hla, D., Dindoust, D., Moehrle, D., Ross, D., Chou, E., Lin, E., Haredasht, F. N., Cheng, G., Gao, I., Chang, J., Silberg, J., Fries, J. A., Xu, J., Jamison, J., Tamaresis, J. S., Chen, J. H., Lazaro, J., Banda, J. M., Lee, J. J., Matthys, K. E., Steffner, K. R., Tian, L., Pegolotti, L., Srinivasan, M., Manimaran, M., Schwede, M., Zhang, M., Nguyen, M., Fathzadeh, M., Zhao, Q., Bajra, R., Khurana, R., Azam, R., Bartlett, R., Truong, S. T., Fleming, S. L., Raj, S., Behr, S., Onyeka, S., Muppidi, S., Bandali, T., Eulalio, T. Y., Chen, W., Zhou, X., Ding, Y., Cui, Y., Tan, Y., Liu, Y., Shah, N., Daneshjou, R. 2025; 8 (1): 149

  Abstract

  Red teaming, the practice of adversarially exposing unexpected or undesired model behaviors, is critical towards improving equity and accuracy of large language models, but non-model creator-affiliated red teaming is scant in healthcare. We convened teams of clinicians, medical and engineering students, and technical professionals (80 participants total) to stress-test models with real-world clinical cases and categorize inappropriate responses along axes of safety, privacy, hallucinations/accuracy, and bias. Six medically-trained reviewers re-analyzed prompt-response pairs and added qualitative annotations. Of 376 unique prompts (1504 responses), 20.1% were inappropriate (GPT-3.5: 25.8%; GPT-4.0: 16%; GPT-4.0 with Internet: 17.8%). Subsequently, we show the utility of our benchmark by testing GPT-4o, a model released after our event (20.4% inappropriate). 21.5% of responses appropriate with GPT-3.5 were inappropriate in updated models. We share insights for constructing red teaming prompts, and present our benchmark for iterative model assessments.

  View details for DOI 10.1038/s41746-025-01542-0

  View details for PubMedID 40055532

  View details for PubMedCentralID 10564921

• Upper limb musculoskeletal model as path generator for control a virtual orthosis: A dynamic neural network approach ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE Lozano, A., Cruz-Ortiz, D., Ballesteros, M., Chairez, I. 2025; 141

  View details for DOI 10.1016/j.engappai.2024.109670

  View details for Web of Science ID 001374462300001

• Asymmetric Constrained Control of a Cervical Orthotic Device Based on Barrier Sliding Modes APPLIED SCIENCES-BASEL Mireles, C., Lozano, A., Ballesteros, M., Cruz-Ortiz, D., Salgado, I. 2022; 12 (20)

  View details for DOI 10.3390/app122010286

  View details for Web of Science ID 000872289000001

• Active neck orthosis for musculoskeletal cervical disorders rehabilitation using a parallel mini-robotic device CONTROL ENGINEERING PRACTICE Lozano, A., Ballesteros, M., Cruz-Ortiz, D., Chairez, I. 2022; 128

  View details for DOI 10.1016/j.conengprac.2022.105312

  View details for Web of Science ID 000863293100001