Digital technologies are making diagnosis faster and more data-driven while enabling more precise, remote and personalized treatment. However, benefits depend on data quality, professional oversight, access and ethical governance. This topic is shared by SL and HL.
In the IB Digital Society health context, digital systems create, store, process and distribute health data. Artificial intelligence can identify patterns in images or patient records, while wearables collect indicators such as heart rate. Clinicians use this information to support diagnosis, monitor treatment and detect changes earlier.
| Area | How digital technologies create change | Main opportunities and risks |
|---|---|---|
| Diagnosis | Machine-learning systems analyse scans, results and electronic health records. | Earlier detection and consistent analysis, but biased or incomplete training data can produce unequal error rates. |
| Treatment | Robotic systems assist surgery, algorithms support personalized medicine, and connected devices monitor patients remotely. | Greater precision and access, but failures, cyberattacks and dependence on automated recommendations create risks. |
| Telemedicine | Connects patients and professionals across distance. | Improves remote access, although limited connectivity and digital literacy may deepen the digital divide. |
A specific real-world example is LumineticsCore, authorized by the Food and Drug Administration in 2018. System: Its AI analyses retinal images for diabetic retinopathy. Example specifics: It produces results without initial specialist interpretation. Impacts: Patients may be screened sooner, directing specialists toward higher-risk cases. Implications: Wider deployment could improve access, but inaccurate or unequal results could harm trust and health outcomes. This raises power and values and ethics because developers and institutions shape clinical processes, accountability and equitable access.
For an IB exam response, distinguish observed impacts from future implications and identify at least two stakeholder groups. Correct the misconception that AI automatically replaces medical judgment. For evaluate or discuss, weigh efficiency and access against bias, privacy and inequality before a supported conclusion.
