Instruction

Every health technology reaches the public through media, and media is designed to persuade. This unit asks you to tell true stories about AI in medicine, so start by studying how persuasion works and how to test it against evidence.

WPA health posters (1936-1943). During the Great Depression, artists employed by the Works Progress Administration (later the Work Projects Administration) designed thousands of silkscreen posters, many promoting public health: tuberculosis testing, syphilis treatment, nutrition, clean milk and safety at work. The Library of Congress holds a large collection. Their design is direct: flat areas of bold color, one image, a short command in large type. They were made to change behavior quickly, often for people with limited schooling. Look at one and ask: what does it want you to feel, and what does it want you to do?

Today’s version. AI health products are presented with a different visual language: clean white interfaces, glowing overlays on scans, confident percentages. Like the posters, these are designed to create trust. Unlike the posters, they often promote a product. Your job is to check whether the evidence supports the feeling.

A documented success, with a twist. Diabetic retinopathy is damage to the retina caused by diabetes and can lead to blindness if not caught. In 2016 Google researchers published in JAMA a deep-learning system that graded retinal photographs for referable diabetic retinopathy with high sensitivity and specificity compared with ophthalmologists. In 2018 DeepMind (also owned by Google’s parent company) and Moorfields Eye Hospital published in Nature Medicine a system that recommended referrals for many eye conditions from OCT scans. Also in 2018 the FDA authorised IDx-DR, from a different company, to give a screening result without a clinician interpreting the image. Then came the twist. In 2020 Google researchers published a human-centered study (Beede and colleagues) of their system in clinics in Thailand. Nurses found that many images taken in real conditions, with poor lighting, were rejected by the system as ungradable, and slow internet connections delayed results. The model worked; the deployment struggled. The lesson is that a laboratory result is not the same as a clinical success.

A documented failure. IBM’s Watson for Oncology was promoted in the 2010s as a tool to recommend cancer treatments. Published reporting, including internal IBM documents obtained by the health news outlet STAT in 2018, described recommendations that doctors judged unsafe or incorrect, and training that relied partly on hypothetical cases rather than real patient data. A separate project at the University of Texas MD Anderson Cancer Center was halted after a 2017 university audit. IBM sold its Watson Health data and analytics business in 2022. The gap between the marketing and the evidence became the story.

Reading evidence. For every case, separate three kinds of claim: what the developer says, what an independent study found, and what happened in real deployment. Note who did each study. A study by the developer’s own researchers is useful but not independent. Keep a two-column chart: claim and evidence. It will become the spine of your talk.

Formative check

Work through these before moving on. They are not graded — they tell you, and your teacher, whether the standard below has landed yet.