In March, health technology startup HeHealth debuted Calmara AI, an app proclaiming to be “your intimacy bestie for safer sex.” The app was heavily marketed to women, who were told they could upload a picture of their partner’s penis for Calmara to scan for evidence of a sexually transmitted infection (STI). Users would get an emoji-laden “Clear!” or “Hold!!!” verdict — with a disclaimer saying the penis in question wasn’t necessarily free of all STIs.

The reaction Ella Dawson, sex and culture critic, had when she first saw Calmara AI’s claim to provide “AI-powered scans [that] give you clear, science-backed answers about your partner’s sexual health status” can be easily summed up: “big yikes.” She raised the alarm on social media, voicing her concerns about privacy and accuracy. The attention prompted a deluge of negative press and a Los Angeles Times investigation.

The Federal Trade Commission was also concerned. The agency notified HeHealth, the parent company of Calmara AI, that it was opening an investigation into possibly fraudulent advertising claims and privacy concerns. Within days, HeHealth pulled its apps off the market.

HeHealth CEO Yudara Kularathne emphasized that the FTC found no wrongdoing and said that no penalties were imposed. “The HeHealth consumer app was incurring significant losses, so we decided to close it to focus on profitability as a startup,” he wrote over email, saying that the company is now focused on business-to-business projects with governments and NGOs mostly outside the United States.

More and more AI-powered sexual health apps have been cropping up, and there’s no sign of stopping. Some of the new consumer-focused apps are targeted toward women and queer people, who often have difficulties getting culturally sensitive and gender-informed care. Venture capitalists and funders see opportunities in underserved populations — but can prioritize growth over privacy and security.

  • Hossenfeffer
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    2 months ago

    When I had a camera shoved up my fundament an AI was watching the camera feed to learn how to spot potential cancerous growths, precancerous polyps, etc. Lucky AI. Apparently the process is that it scans the feed, highlights on screen areas it wants the radiologist to take another look at, and they then verify if it’s a real issue or nothing to worry about. In that process flow I’m entirely comfortable with it being a second pair of eyes for the radiologist.

    Eventually I guess it could replace the radiologist, but I’d want to see a 100% success rate demonstrated over a sufficiently long test period before that could happen.