Publications by authors named "Maksymilian A Brzezicki"

3 Publications

  • Page 1 of 1

Artificial intelligence outperforms human students in conducting neurosurgical audits.

Clin Neurol Neurosurg 2020 05 10;192:105732. Epub 2020 Feb 10.

Department of Physiology and Pharmacology, Clinical Research and Imaging Centre, University of Bristol, UK. Electronic address:

Objectives: Neurosurgical audits are an important part of improving the safety, efficiency and quality of care but require considerable resources, time, and funding. To that end, the advent of the Artificial Intelligence-based algorithms offered a novel, more economically viable solution. The aim of the study was to evaluate whether the algorithm can indeed outperform humans in that task.

Patients & Methods: Forty-six human students were invited to inspect the clinical notes of 45 medical outliers on a neurosurgical ward. The aim of the task was to produce a report containing a quantitative analysis of the scale of the problem (e.g. time to discharge) and a qualitative list of suggestions on how to improve the patient flow, quality of care, and healthcare costs. The Artificial Intelligence-based Frideswide algorithm (FwA) was used to analyse the same dataset.

Results: The FwA produced 44 recommendations whilst human students reported an average of 3.89. The mean time to deliver the final report was 5.80 s for the FwA and 10.21 days for humans. The mean relative error for factual inaccuracy for humans was 14.75 % for total waiting times and 81.06 % for times between investigations. The report produced by the FwA was entirely factually correct. 13 out of 46 students submitted an unfinished audit, 3 out of 46 made an overdue submission. Thematic analysis revealed numerous internal contradictions of the recommendations given by human students.

Conclusion: The AI-based algorithm can produce significantly more recommendations in shorter time. The audits conducted by the AI are more factually accurate (0 % error rate) and logically consistent (no thematic contradictions). This study shows that the algorithm can produce reliable neurosurgical audits for a fraction of the resources required to conduct it by human means.
View Article and Find Full Text PDF

Download full-text PDF

Source Listing
May 2020

Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement.

Adv Med Sci 2019 Sep 2;64(2):292-302. Epub 2019 Apr 2.

Bristol Institute of Clinical Neurosciences, University of Bristol, Southmead Hospital, Bristol, UK.

Purpose: Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor prognosis and a substantial reduction in quality of life. The rate of misdiagnosis of FTD is very high, with patients often waiting for years without a firm diagnosis. This study investigates the current state of the misdiagnosis of FTD using a novel artificial intelligence-based algorithm.

Patients & Methods: An artificial intelligence algorithm has been developed to retrospectively analyse the patient journeys of 47 individuals diagnosed with FTD (age range 52-80). The algorithm analysed the efficiency of patient pathways by utilizing a reward signal of ‒1 to +1 to assess the symptoms, imaging techniques, and clinical judgement in both behavioural and language variants of the disease.

Results: On average, every patient was subjected to 4.93 investigations, of which 67.4% were radiological scans. From first presentation it took on average 939 days for a firm diagnosis. The mean time between appointments was 204 days, and the average patient had their diagnosis altered 7.37 times during their journey. The algorithm proposed improvements by evaluating the interventions that resulted in a decreased reward signal to both the individual and the population as a whole.

Conclusions: The study proves that the algorithm can efficiently guide clinical practice and improve the accuracy of the diagnosis of FTD whilst making the process of auditing faster and more economically viable.
View Article and Find Full Text PDF

Download full-text PDF

Source Listing
September 2019