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Artificial Intelligence and Machine Learning to Improve the Effectiveness and Efficiency of Health Care


Project Description

Artificial intelligence (AI) is widespread throughout commercial industries (think Google, Uber, Alexa, and Siri), but rarely used in health care settings. A tsunami of data produced from routine clinical care (e.g. electronic health records), advances in computing power, and pressure for higher quality with less resource provides ample opportunities for AI in health care. Examples worked on by the supervisory team include development of AI systems to: improve the efficiency of triage of patients’ requests for appointments; automate the processing of clinical communications between secondary and primary care; create more accurate and usable clinical decision support systems; send personalised messages to patients about actions they could take to improve their health.

This PhD will explore and evaluate new ways of using AI and ML to improve the effectiveness and efficiency of health care. Dependent on the applicant’s background and personal development goals this could entail: developing new AI applications or studying and optimising existing ones in clinical practice (for example, as developed by the supervisory team). The former would include data engineering, machine learning, and lab-based software testing; the latter would focus on implementation and real-world effects on patient care.

The supervisory team combines both extensive clinical (primary and nursing care) and health informatics research experience, with a track record of producing technological solutions for health care and successful supervision of doctoral students.

Candidates are expected to hold (or be about to obtain) a Masters degree (or equivalent) in a related area. Candidates with a professional qualification in nursing or the allied health professions are encouraged to apply. For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. This project has a Standard Band fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

K. Yu, A.L. Beam, I.S. Kohane, Artificial intelligence in healthcare, Nat. Biomed. Eng. 2 (2018) 719–731. doi:10.1038/s41551-018-0305-z.

K.-H. Yu, I.S. Kohane, Framing the challenges of artificial intelligence in medicine, BMJ Qual. Saf. (2018) bmjqs-2018-008551. doi:10.1136/bmjqs-2018-008551.

V.H. Buch, I. Ahmed, M. Maruthappu, Artificial intelligence in medicine: current trends and future possibilities., Br. J. Gen. Pract. 68 (2018) 143–144. doi:10.3399/bjgp18X695213.

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