STANDING Together: Equitable AI and In Silico Technologies in Healthcare

STANDING Together: Equitable AI and In Silico Technologies in Healthcare

What if In Silico Technologies in healthcare could truly serve everyone? The STANDING Together recommendations are revolutionising how we think about health data diversity and inclusion. From bustling cities to remote villages, discover how this groundbreaking initiative is reshaping healthcare technologies development through the voices of patients, practitioners, and policymakers. Join us as we explore the journey towards equitable healthcare technology that leaves no one behind

We'll explore the importance of data diversity and patient involvement in shaping the future of medical technology—be it AI diagnostic systems or in silico technologies predicting drug interactions. Understand how these recommendations propose a shift towards a more responsible and inclusive approach, requiring the commitment of researchers, developers, policymakers, and patients themselves.

This episode highlights patient advocacy, urging everyone to become proactive in their healthcare journey and data literacy. As AI becomes increasingly integrated into our lives, ensuring fairness and equity becomes paramount—a responsibility we all share. www.datadiversity.org

Alderman JE, Palmer J, Laws E, McCradden MD, Ordish J, Ghassemi M, Pfohl SR, Rostamzadeh N, Cole-Lewis H, Glocker B, Calvert M, Pollard TJ, Gill J, Gath J, Adebajo A, Beng J, Leung CH, Kuku S, Farmer LA, Matin RN, Mateen BA, McKay F, Heller K, Karthikesalingam A, Treanor D, Mackintosh M, Oakden-Rayner L, Pearson R, Manrai AK, Myles P, Kumuthini J, Kapacee Z, Sebire NJ, Nazer LH, Seah J, Akbari A, Berman L, Gichoya JW, Righetto L, Samuel D, Wasswa W, Charalambides M, Arora A, Pujari S, Summers C, Sapey E, Wilkinson S, Thakker V, Denniston A, Liu X. Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. Lancet Digit Health. 2025 Jan;7(1):e64-e88. doi: 10.1016/S2589-7500(24)00224-3.

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