Launching prediction markets after trial enrollment doesn't eliminate bias risk. Patients, who often correctly guess their treatment group, can be influenced by market signals. If a market predicts their arm will fail, they may be more likely to drop out or change their reporting behavior, corrupting the study's data.

The long-held belief that visible, liquid prediction markets would improve collective wisdom and decision-making has been falsified. In practice, platforms like Polymarket and Kalshi are dominated by trading and gambling behavior, not the rigorous epistemic practice of forecasting.

The rise of accessible prediction markets creates perverse incentives for individuals to profit from insider information or by directly manipulating events. Examples range from a special ops soldier betting on a mission to someone using a hairdryer to spike a temperature sensor, illustrating a new, "democratized" form of sleaze.

While prediction markets offer pure, insightful data that can outperform traditional polling, they have a dark side. High stakes can incentivize bettors to shift from predicting events to actively influencing them, including threatening journalists to alter their reporting and swing a market in their favor.

Beyond the controversy, new platforms allowing betting on clinical trial outcomes could serve a practical purpose. They may provide a crowdsourced, real-time 'Probability of Success' (PoS) metric that analysts and investors can incorporate into financial models, offering an alternative to traditional expert forecasts.

Experts express skepticism about the scientific value of AI-powered clinical trial prediction markets. The primary concern is that they function more as sophisticated betting platforms than tools to advance medicine. Their predictive power may not surpass the collective intelligence already embedded in public stock prices.

Unlike other industries, biopharma's clinical trial process grants confidential data access to a wide range of individuals—researchers, contractors, and regulators—magnifying the risk of misuse in prediction markets.

A more significant danger than insider trading is that individuals in power could actively manipulate real-world outcomes to ensure their bets on a prediction market pay out. This moves beyond leveraging information to actively corrupting decision-making for financial gain, akin to throwing a game in sports.

Bioethicist Jonathan Kimmelman argues that if prediction markets become highly effective, they destroy the ethical foundation of randomized trials. The principle of "equipoise" requires genuine uncertainty; if a market "knows" a drug is inferior, it becomes unethical to randomize patients to that treatment arm.