Clifton, David A.

A review of novelty detection

Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 1,649

Multimodal learning with transformers: A survey

Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 1,072

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 1,039

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · BMJ

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 665

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Charlton, P. H., Birrenkott, D. A., Bonnici, T., Pimentel, M. A. F., Johnson, A. E. W., Alastruey, J., Tarassenko, L., Watkinson, P. J., Beale, R., & Clifton, D. A. (2018) · IEEE Reviews in Biomedical Engineering

Non-Invasive Vital Sign MonitoringECG Monitoring and Analysis

Cited by 379

Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring

Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., & Tarassenko, L. (2014) · IEEE Journal of Biomedical and Health Informatics

Non-Invasive Vital Sign MonitoringECG Monitoring and Analysis

Cited by 368

An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram

Charlton, P. H., Bonnici, T., Tarassenko, L., Clifton, D. A., Beale, R., & Watkinson, P. J. (2016) · Physiological Measurement

Non-Invasive Vital Sign MonitoringECG Monitoring and Analysis

Cited by 366

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding, X., Clifton, D., Ji, N., Lovell, N. H., Bonato, P., Chen, W., Yu, X., Xue, Z., Xiang, T., Long, X., et al. (2021) · IEEE Reviews in Biomedical Engineering

Non-Invasive Vital Sign MonitoringAdvanced Sensor and Energy Harvesting Materials

Cited by 281

A Survey of Large Language Models in Medicine: Progress, Application, and Challenge

Zhou, H., Liu, F., Gu, B., Zou, X., Huang, J., Wu, J., Li, Y., Chen, S. S., Zhou, P., Liu, J., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 240

Photoplethysmographic derivation of respiratory rate: A review of relevant physiology

Meredith, D. J., Clifton, D., Charlton, P., Brooks, J., Pugh, C. W., & Tarassenko, L. (2012) · Journal of Medical Engineering & Technology

Non-Invasive Vital Sign MonitoringHeart Rate Variability and Autonomic Control

Cited by 226

Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening

Yang, J., Soltan, A. A. S., & Clifton, D. A. (2022) · npj Digital Medicine

Machine Learning in HealthcareCOVID-19 diagnosis using AI

Cited by 181

The correlation between baseline score and post-intervention score, and its implications for statistical analysis

Clifton, L., & Clifton, D. A. (2019) · Trials

Statistical Methods in Clinical TrialsMeta-analysis and systematic reviews

Cited by 178

External validation of AI models in health should be replaced with recurring local validation

Youssef, A., Pencina, M., Thakur, A., Zhu, T., Clifton, D., & Shah, N. H. (2023) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 170

Self-supervised learning for human activity recognition using 700,000 person-days of wearable data

Yuan, H., Chan, S., Creagh, A. P., Tong, C., Acquah, A., Clifton, D. A., & Doherty, A. (2024) · npj Digital Medicine

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 166

An adversarial training framework for mitigating algorithmic biases in clinical machine learning

Yang, J., Soltan, A. A. S., Eyre, D. W., Yang, Y., & Clifton, D. A. (2023) · npj Digital Medicine

Ethics and Social Impacts of AIMachine Learning in Healthcare

Cited by 159

A medical multimodal large language model for future pandemics

Liu, F., Zhu, T., Wu, X., Yang, B., You, C., Wang, C., Lu, L., Liu, Z., Zheng, Y., Sun, X., et al. (2023) · npj Digital Medicine

Multimodal Machine Learning ApplicationsCOVID-19 diagnosis using AI

Cited by 155

A scoping review of large language models for generative tasks in mental health care

Hua, Y., Na, H., Li, Z., Liu, F., Fang, X., Clifton, D., & Torous, J. (2025) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationDigital Mental Health Interventions

Cited by 151

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test

Soltan, A. A. S., Kouchaki, S., Zhu, T., Kiyasseh, D., Taylor, T., Hussain, Z. B., Peto, T., Brent, A. J., Eyre, D. W., & Clifton, D. A. (2021) · The Lancet Digital Health

COVID-19 Clinical Research StudiesMachine Learning in Healthcare

Cited by 150

Gaussian processes for personalized e-health monitoring with wearable sensors

Clifton, L., Clifton, D. A., Pimentel, M. A. F., Watkinson, P. J., & Tarassenko, L. (2013) · IEEE Transactions on Biomedical Engineering

Non-Invasive Vital Sign MonitoringGaussian Processes and Bayesian Inference

Cited by 117

Application of large language models in medicine

Liu, F., Zhou, H., Gu, B., Zou, X., Huang, J., Wu, J., Li, Y., Chen, S. S., Hua, Y., Zhou, P., et al. (2025) · Nature Reviews Bioengineering

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 94

A Robust Fusion Model for Estimating Respiratory Rate From Photoplethysmography and Electrocardiography

Birrenkott, D. A., Pimentel, M. A. F., Watkinson, P. J., & Clifton, D. A. (2018) · IEEE Transactions on Biomedical Engineering

Non-Invasive Vital Sign MonitoringECG Monitoring and Analysis

Cited by 71

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis

Creagh, A. P., Hamy, V., Yuan, H., Mertes, G., Tomlinson, R., Chen, W.-H., Williams, R., Llop, C., Yee, C., Duh, M. S., et al. (2022) · npj Digital Medicine

Rheumatoid Arthritis Research and TherapiesMobile Health and mHealth Applications

Cited by 64

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis

Creagh, A. P., Hamy, V., Yuan, H., Mertes, G., Tomlinson, R., Chen, W.-H., Williams, R., Llop, C., Yee, C., Duh, M. S., et al. (2024) · npj Digital Medicine

Rheumatoid Arthritis Research and TherapiesMobile Health and mHealth Applications

Cited by 64

Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening

Soltan, A. A. S., Yang, J., Pattanshetty, R., Novak, A., Yang, Y., Rohanian, O., Beer, S., Soltan, M. A., Thickett, D. R., Fairhead, R., et al. (2022) · The Lancet Digital Health

COVID-19 Clinical Research StudiesSARS-CoV-2 detection and testing

Cited by 54