Post by Algaurizin

AI won't replace oncologists, but those who leverage AI will replace those who don't. Here's what that looks like when label expansions accelerate at 2026's pace. Three FDA actions in ten days expose a real tension: as therapies multiply across indications, can clinicians keep pace with evolving biomarker testing, access pathways, and safety profiles? AstraZeneca and Daiichi Sankyo US Enhertu now spans HER2-positive metastatic, HER2-low, AND early breast cancer. Datroway bridges NSCLC and TNBC. Celcuity Gedatolisib targets PIK3CA wild-type, a subpopulation needing precision diagnostics many practices haven't standardized. This is where AI stops being hype and becomes infrastructure. Our latest Pipeline to Market issue explores what happens when indication sequencing meets computational medicine: ▸ AI-powered diagnostics automating reflex testing for multiple biomarkers in one workflow ▸ Predictive algorithms flagging PA denial risk before prescriptions submit ▸ NLP tools translating mechanism-of-action data into tumor-specific talking points per specialty ▸ Generative AI synthesizing multi-indication trial data into prescriber-friendly summaries that update in real time Here's what remains uncertain: can these tools integrate with existing EHR workflows without creating new friction? Early pilots suggest yes, but adoption depends on interoperability, reimbursement codes, and whether payers recognize algorithmic recommendations for PA purposes. The full analysis includes clinical pearls, access briefs, and expanded approval dossiers: https://lnkd.in/d-P3JZsC What's one bottleneck in your practice you wish AI could solve immediately, prior auth, reflex testing, AE monitoring, or something else? If AI-generated treatment summaries appeared in your EHR alongside NCCN guidelines, what would need to be true for you to trust them? #PrecisionOncology #AIinHealthcare #BiomarkerTesting