AI is being used in oncology workflows for clinical note-taking, patient preparation and complex scheduling, according to US Oncology executive Jackie Njoroge. Her account shows how these tools are being fitted around clinicians’ work, with an agentic scheduling system still a goal rather than a described live service.
Watch Desk analysis
What happened
Njoroge, US Oncology’s vice-president for digital transformation and operational excellence, describes the organisation’s current and planned uses of AI in a Forbes Technology Council article.
She says ambient scribing produces a draft clinical record for a clinician to review and sign. AI tools also prepare concise summaries from records held across multiple systems, and help identify patterns in oncology scheduling, where labs, pretreatment steps and infusions may need careful sequencing. Njoroge says the organisation is building towards an AI agent that could handle patient intake and schedule visits using that logic. She also describes AI being used in prior authorisation, billing and care navigation.
Why it matters
These examples put AI in the administrative and coordination work surrounding care, not in place of a clinician’s judgement. Njoroge says clinicians still review and sign notes, and describes clinician involvement in configuring and refining the tools. That distinction matters in oncology, where delays and fragmented records can add friction to an already demanding patient journey.
Her article is an executive’s account of work at her organisation, not a measured evaluation of patient outcomes. Still, it gives a more useful picture than a generic promise that AI will transform healthcare: specific tasks, a human review step, and a proposed next step in scheduling.
Our read
The compelling idea is not that AI should make clinical decisions faster. It is that reducing the paperwork and coordination burden may leave care teams with more time for patients. That is a sensible aim, but the proof should be in whether workflows improve without adding new errors or review burdens. In healthcare, “more efficient” is a claim that needs to survive contact with the clinic.
What to watch
- Whether the planned intake-and-scheduling agent is deployed, and what decisions it can make.
- How clinicians review AI-generated notes and summaries in day-to-day practice.
- Whether the organisation publishes evidence on time saved, delays or patient outcomes.
Discussion spark: Which healthcare workflow should AI tackle first: documentation, appointment coordination or billing, and what evidence should be required before calling it an improvement?
Sources and evidence
- Why Oncology Is Becoming AI's Most Important Proving Ground – Forbes (15 October 2026, 19:45 UTC)
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