Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation - Takeaways - MDSpire

Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation

  • By

  • Sharmake Gaiye Bashir

  • Hiba Abdi Salad

  • Yakub Burhan Abdullahi

  • Yusuf Hared Abdi

  • Mohamed Sharif Abdi

  • Naima Ibrahim Ahmed

  • Shuaibu Saidu Musa

  • Nafisa M. K. Elehamer

  • Muhammad Kabir Musa

  • Obasanjo Bolarinwa

  • Olusegun Dada

  • June 19, 2026

  • 0 min

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  • 1

    Artificial intelligence (AI) enhances early detection and management of adverse pregnancy outcomes through improved risk prediction and clinical decision support.

  • 2

    Predictive performance of AI in obstetric complications shows considerable heterogeneity, with AUROC values ranging from 0.73 to 0.97 across different studies.

  • 3

    Eight key bias mechanisms affecting AI model performance in maternal healthcare include sampling bias, measurement bias, and algorithmic bias.

  • 4

    Limited external validation and absence of prospective clinical impact trials constrain current evidence on AI applications in diverse populations.

  • 5

    Responsible clinical translation of AI in maternal health requires inclusive dataset development, multisite validation, and regulatory strengthening.

Original Source(s)

Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation

  • by Sharmake Gaiye Bashir, Hiba Abdi Salad, Yakub Burhan Abdullahi, Yusuf Hared Abdi, Mohamed Sharif Abdi, Naima Ibrahim Ahmed, Shuaibu Saidu Musa, Nafisa M. K. Elehamer, Muhammad Kabir Musa, Obasanjo Bolarinwa, Olusegun Dada

  • June 19, 2026

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