University of Southampton

Bias in data‐driven artificial intelligence systems—An introductory survey

Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju et al.

2020 · 969 Zit.

Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study

Tom Nadarzynski, Oliver Miles, Aimee Cowie et al.

2019 · 775 Zit.

Putting AI ethics to work: are the tools fit for purpose?

Jacqui Ayling, Adriane Chapman

2021 · 223 Zit.

Twenty-Eighth AAAI Conference on Artificial Intelligence

Nicola Gatti, Marco Rocco, Sofia Ceppi et al.

2014 · 184 Zit.

Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

Weisha Wang, Long Chen, Mengran Xiong et al.

2021 · 139 Zit.

How the different explanation classes impact trust calibration: The case of clinical decision support systems

Mohammad Naiseh, Dena Al‐Thani, Nan Jiang et al.

2022 · 125 Zit.

Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

Joseph Alderman, Joanne Palmer, Elinor Laws et al.

2024 · 105 Zit.

Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability

Caroline Jones, James Thornton, Jeremy C Wyatt

2023 · 92 Zit.

Prediction models for childhood asthma: A systematic review

Dilini M. Kothalawala, Latha Kadalayil, Veronique B. N. Weiss et al.

2020 · 79 Zit.

Could artificial intelligence write mental health nursing care plans?

Samuel Woodnutt, Chris Allen, Jasmine Snowden et al.

2023 · 78 Zit.

Trustworthy human-AI partnerships

Sarvapali D. Ramchurn, Sebastian Stein, Nicholas R. Jennings

2021 · 64 Zit.

The future of bone regeneration: integrating AI into tissue engineering

Benita MacKay, Karen M. Marshall, James A. Grant‐Jacob et al.

2021 · 60 Zit.

Explainable AI and the philosophy and practice of explanation

Kieron O’Hara

2020 · 55 Zit.

Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies

Brian Pickering

2021 · 54 Zit.

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

Imogen S. Stafford, Mark Gosink, Enrico Mossotto et al.

2022 · 51 Zit.