Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening by Yang, J, Soltan, AAS, Clifton, DA

Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning by Yang, J, Soltan, AAS, Eyre, DW, Clifton, DA

An adversarial training framework for mitigating algorithmic biases in clinical machine learning by Yang, J, Soltan, AAS, Eyre, DW, Yang, Y, Clifton, DA

Addressing label noise for electronic health records: insights from computer vision for tabular data by Yang, J, Triendl, H, Soltan, AAS, Prakash, M, Clifton, DA

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test by Soltan, AAS, Kouchaki, S, Zhu, T, Kiyasseh, D, Taylor, T, Hussain, ZB, Peto, T, Brent, AJ, Eyre, DW, Clifton, DA

Mitigating machine learning bias between high income and low–middle income countries for enhanced model fairness and generalizability by Yang, J, Clifton, L, Dung, NT, Phong, NT, Yen, LM, Thy, DBX, Soltan, AAS, Thwaites, L, Clifton, DA

Deep reinforcement learning for multi-class imbalanced training: applications in healthcare by Yang, J, El-Bouri, R, O'Donoghue, O, Lachapelle, A, Soltan, AAS, Eyre, DW, Lu, L, Clifton, DA

Generalizability assessment of AI models across hospitals in a low-middle and high income country by Yang, J, Dung, NT, Thach, PN, Phong, NT, Phu, VD, Phu, KD, Yen, LM, Thy, DBX, Soltan, AAS, Thwaites, L, Clifton, DA

Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening by Soltan, AAS, Yang, J, Pattanshetty, R, Novak, A, Yang, Y, Rohanian, O, Beer, S, Soltan, MA, Thickett, DR, Fairhead, R, Zhu, T, Eyre, DW, Clifton, DA