Disease-modifying treatments such as monoclonal antibodies can be highly effective in chronic inflammatory diseases such as COPD, but often fail in clinical trials due to heterogeneity of inflammation and imperfect tools to stratify patients to select optimal therapeutic approaches. Molecular imaging provides the potential to transform precision medicine in this field.

There is a critical unmet need for scalable, accessible and objective diagnostic tests for stratification in dementia. Biofluid Raman spectroscopy (RS) due to its simplicity, holistic and label-free nature, is a powerful approach that has the potential to offer differential diagnosis across dementia types including Alzheimer's disease (AD). RS is a laser-based optical method that can rapidly provide chemically rich information ('spectral biomarkers') from biofluids but its utility for AD diagnosis has not been established in a 'real-world' context, specifically from a clinically heterogenous cohort of patients. We carried out RS measurements on cerebrospinal fluid (CSF) samples of patients from a mixed clinical cohort (N = 143). All patients reported cognitive complaints and were clinically diagnosed over 2 years with conditions including AD and other neurodegenerative diseases, as well as developmental and long-term chronic conditions. Machine-learning algorithms were trained, optimised and evaluated on Raman spectra to classify AD from non-AD. AD was classified with 93% accuracy for patients in the testing set. Time from sample to classification was < 1 h. Spectral biomarkers explaining AD classification were identified and primarily assigned to protein-derived aromatic amino acids, representing a difference in proteome signature between AD and non-AD groups. Signals from a subset of spectral biomarkers directly correlated with pathological CSF biomarker concentrations including amyloid-β 42, phosphorylated-tau 181, and total tau. This pre-clinical study is a first step towards realising the real-world application of RS for dementia diagnosis. Compared to current and emerging methods, RS does not require sophisticated instrumentation or specialised labs. It is reagentless and simple, offering unprecedented rapidity, scalability, accessibility for dementia diagnosis.

WB-SPECT/CT can provide valuable insight into metastatic disease, assist in diagnosing numerous conditions, and enable volumetric dosimetry. Various approaches have made whole-body (WB) SPECT/CT less feasible in routine practice as it takes impractically long acquisition times. We aim to determine whether acquire during step (ADS) technology can be used for WB SPECT/CT studies to minimise acquisition time without compromising diagnostic quality.

Medical physics and clinical engineering (MPCE) professionals have a critical role in the safe and effective deployment of artificial intelligence (AI) in healthcare, however their attitudes and opinions towards AI are not well understood. A 2024 survey was launched by the Institute of Physics and Engineering in Medicine to UK MPCE professionals to gather information on the current usage of AI, whether it is believed their role will change, if there is any fear about job replacement, the training being conducted, levels of preparedness, concerns about AI introduction, and barriers to AI deployment. A total of 409 responses were received. It was found that AI is widely used (59% of respondents), with wide disparities between disciplines (radiotherapy 76% compared to clinical engineering 37%). Job losses are predicted by 40% of staff, with junior NHS staff more concerned. Nearly 80% of respondents are investing in their own learning, but only 23% know where to look for training resources. Only 10% of the cohort had some prior AI education. Without prior education on AI, only 13% of respondents feel prepared for AI introduction; but this increases by a factor of three with education. Lack of training and knowledge is the major concern and barrier to AI adoption, while lack of a clear AI governance framework was also frequently cited. This survey provides a snapshot of the current status and attitudes of the UK MPCE workforce towards AI and should be used in guiding future efforts in training and education, addressing discipline disparities and overcoming deployment barriers.

Alzheimer's disease (AD) greatly affects the daily functioning and life quality of patients and is prevalent in the elderly population. Amyloid-β (Aβ) accumulation in the brain is the main hallmark of AD pathophysiology. Positron Emission Tomography (PET) imaging is the most accurate method to identify Aβ deposits in the brain, but it is expensive and not widely available. The development of a low-cost method to detect Aβ deposition in the brain, as an alternative to PET, would therefore be of great value. This study aims to develop and validate machine learning algorithms for accurately predicting brain Aβ positivity using plasma biomarkers, genetic information, and clinical data as a cost-effective alternative to PET imaging.