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Shoppers for brighter health outcomes are increasingly betting on AI-driven tools; clinicians, patients and health systems want to know what actually scales. In a frank conversation at the People & Planet United Global Health & Purpose Summit, a practising oncologist and a health\u2011tech co\u2011founder laid out what it takes to move AI from prototype to widespread clinical benefit.<\/strong><\/p>\n Essential takeaways<\/p>\n When AI teams start with algorithms instead of people, the result often gathers dust. Dr Arturo Loaiza\u2011Bonilla\u2019s dual role as an oncologist and co\u2011founder shows why design rooted in daily practice matters. Tools that anticipate how a tumour board discusses cases, or how a local clinic screens referrals, will feel less foreign and more useful. According to clinical reports, when frontline staff help shape models, uptake is quicker and the outcomes are clearer. If you\u2019re picking a partner, ask who practiced in the specialty and who\u2019s still in the clinic.<\/p>\n One big promise of AI is widening trial access , not by replacing investigators, but by matching patients faster and more accurately to relevant studies. That matters especially outside major centres, where patients often miss options. Industry coverage shows technology can streamline eligibility review and flag candidates in community settings, giving care teams an easier way to discuss research with patients. Practical tip: look for solutions that link to your electronic records and provide simple, explainable reasons for recommendations so clinicians can trust and act on them.<\/p>\n You can\u2019t scale without reliable data pipes. Interoperability, making sure systems talk the same language, is the quiet, gritty work behind every successful deployment. Peer literature highlights the need for curated datasets and transparent validation, not black\u2011box claims. Health systems that invest in governance, quality checks and partnerships with research organisations tend to get usable, reproducible results. For buyers, insist on performance metrics from diverse populations and ongoing post\u2011deployment monitoring.<\/p>\n Regulators and clinicians both want evidence that AI tools help patients, not just models that perform well on retrospective datasets. Robust prospective studies, safety monitoring, and bias audits are increasingly expected. The conversation at the summit made a simple point: ethical design isn\u2019t an add\u2011on, it\u2019s how you keep adoption from stalling. If a vendor can\u2019t show how they assess fairness or update models, that\u2019s a red flag. Build contractual checkpoints for outcomes and safety into any procurement.<\/p>\n Turning a promising pilot into routine care requires more than a good algorithm. It needs predictable workflows, clinician champions, measurable KPIs and funding tied to outcomes. Health systems that treat pilots as experiments with clear stop\/go criteria get better returns than those that keep pilots running indefinitely. Expect necessary investment in training, technical support and change management; the smoothest rollouts make the new tools feel like part of the team, not a separate gadget.<\/p>\n It’s a small change in structure and mindset that helps innovation truly benefit patients at scale.<\/p>\n\n
Why clinician\u2011led design flips the script on many AI projects<\/h2>\n
Closing the patient access gap: trials, community systems and practical reach<\/h2>\n
Data, interoperability and trust: the ingredients of scale<\/h2>\n
Regulation, ethics and demonstrating real benefit<\/h2>\n
From pilot to programme: how systems actually scale technology<\/h2>\n
Source Reference Map<\/h3>\n