Think of this as a ‘medical weather forecast’ system powered by AI: it looks at a huge mix of patient data (labs, scans, genetics, history) to predict who is likely to get which disease and which treatment is most likely to work for each person.
Traditional medicine often treats patients with a one‑size‑fits‑all approach and relies heavily on doctors manually sifting through complex data. This work surveys how AI can systematically improve early and accurate diagnosis and match patients to the most effective, personalized therapies, reducing trial‑and‑error care and adverse events.
The main defensibility comes from access to large, longitudinal, multimodal patient datasets (EHR, imaging, genomics, real‑world evidence) combined with validated clinical workflows and regulatory approvals for specific indications; algorithmic approaches themselves are increasingly commoditized.
Early Majority
This work is positioned as a systematic evaluation across disease areas and modalities (imaging, genomics, EHR) rather than a single-algorithm product, emphasizing evidence on clinical effectiveness, predictive performance, and personalization strategies; differentiation in practice would come from deeply validated models embedded within clinical and pharma R&D workflows rather than from novel algorithms alone.
Think of this as a super-smart co‑driver made of many small AI helpers that can not only see the road and steer, but also plan trips, talk to other systems (like traffic lights or charging stations), and make complex decisions on its own to keep passengers safe and moving efficiently.
This is like a smart GPS and financial advisor for car parts moving around the world: it watches shipping routes, tariffs, and costs in real time and then suggests better ways to move parts so automakers avoid delays and surprise expenses when trade rules change.
Think of a polygenic risk score as a “credit score for heart disease” built from thousands of tiny changes in your DNA. This paper reviews how AI can act like a smarter credit bureau—sifting through massive genomic and clinical datasets to build more accurate and personalized scores that predict who is at high risk of heart problems, long before symptoms start.