S1 Ep220: Exploring The Future of Artificial Intelligence and Thoracic Oncology

In a special edition of Oncology On the Go, Chinmay Jani, MD, joined CancerNetwork® in the studio to speak about different research initiatives he is involved with across precision oncology. He discussed ongoing work dedicated to validating and applying artificial intelligence (AI)–based tools in clinical work as well as overcoming immunotherapy resistance among patients with lung cancer.

Jani, chief fellow in Hematology and Oncology at University of Miami Sylvester Comprehensive Cancer Center, first detailed findings from a study he presented at the 2026 American Society of Clinical Oncology (ASCO) Annual Meeting evaluating AI decision support in the context of EGFR-mutated non–small cell lung cancer (NSCLC). Although AI systems aligned with expert decision-making in frontline treatment, significant divergence was observed in second-line care, highlighting a need for more rigorous validation and clinical safeguards when integrating AI into oncologic decision-making. Improving documentation and using tools more ethically, Jani said, will also be critical for future applications of AI in field.

Jani also spoke about the rapidly evolving thoracic oncology field based on research he and colleagues are leading at the University of Miami. Different investigations are exploring potential advancements in precision medicine, overcoming immunotherapy resistance, and early cancer detection to help elevate outcomes among patients with lung cancer. Looking ahead, Jani emphasized how novel therapeutics like tarlatamab-dlle (Imdelltra) and the incorporation of liquid biopsy may assist with the goal of turning lung cancer into “a chronic disease” where patients can survive not just for a few month or years but for decades.

According to Jani, other key concerns in the field include the evolving landscape surrounding adolescent and young adult (AYA) patients, who may require different types of molecular testing and therapeutic needs compared with adult populations. Being able to detect more fusions and alterations that may inform therapeutic strategies via circulating tumor DNA plus circulating tumor RNA or through wider minimal residual disease testing, he said, represents another ongoing goal in terms of precision medicine.

Reference

Jani C, Pérez-Granado J, Kalucha A, et al. Evaluating AI decision support in a rapidly evolving therapeutic landscape: EGFR-mutant metastatic NSCLC. J Clin Oncol. 2026;44(suppl 16):1630. doi:10.1200/JCO.2026.44.16_suppl.1630