In recent years, there is increasing enthusiasm in the healthcare research community for artificial intelligence to provide big data analytics and augment decision making. One of the prime reasons for this is the enormous impact of deep learning for utilization of complex healthcare big data. Although deep learning is a powerful analytic tool for the complex data contained in electronic health records (EHRs), there are also limitations which can make the choice of deep learning inferior in some healthcare applications. In this paper, we give a brief overview of the limitations of deep learning illustrated through case studies done over the years aiming to promote the consideration of alternative analytic strategies for healthcare.

The study identified measures of functional capacity of patient as novel predictors of postoperative bleeding. The study found that risk of postoperative bleeding can be assessed, allowing for better use of human resources in addressing this important adverse event after surgery.

Artificial intelligence (AI) techniques are quickly spreading across medicine as an analytical method to tackle challenging clinical questions. What were previously thought of as highly complex data sources, such as images or free text, are now becoming manageable. Novel analytical methods merge the latest developments in information technology infrastructure with advances in computer science. Once primarily associated with Silicon Valley, AI techniques are now making their way into medicine, including in the field of inflammatory bowel diseases (IBD). Understanding potential applications and limitations of these techniques can be difficult, in particular for busy clinicians. In this article, we explain the basic terminologies and provide particular focus on the foundations behind state-of-the-art AI methodologies in both imaging and text. We explore the growing applications of AI in medicine, with a specific focus on IBD to inform the practicing gastroenterologist and IBD specialist. Finally, we outline possible future uses of these technologies in daily clinical practice.

Purpose Accurate quantification of myocardial perfusion is dependent on reliable electrocardiogram (ECG) triggering. Measuring myocardial blood flow in patients with arrhythmias or poor ECGs is currently infeasible with MR. The purpose of this study is to demonstrate the feasibility of a non-ECG-triggered method with clinically useful 3-slice ventricular coverage for measurement of MBF in healthy volunteers. Methods A saturation recovery magnetization prepared gradient recalled echo (GRE) acquisition was continuously repeated during first-pass imaging. A slice interleaved radial trajectory was employed to enable image based retrospective triggering. The arterial input function (AIF) was generated using a beat-by-beat T1 estimation method. The proposed technique was validated against a conventional ECG-triggered dual-bolus technique in 10 healthy volunteers. The technique was further demonstrated under adenosine stress in 12 healthy volunteers. Results The proposed method produced MBF with no significant difference compared to ECG-triggered technique (mean of 0.76 ± 0.13 to 0.82 ± 0.21). The proposed method yielded mean MPR comparable to published literature. Conclusion We have developed a non-ECG-triggered quantitative perfusion imaging method. In this preliminary study, our results demonstrate that our method yields comparable MBF compared to the conventional ECG-triggered method and it is feasible for stress imaging.

Infection with influenza A (subtypes H1N1 and H3N2) or B viruses results in over half a million deaths worldwide every year. Frequent antigenic changes (drift) in two major viral surface proteins hemagglutinin (HA) and neuraminidase lead to the constant emergence of antigenically distinct virus strains against which there is sub-optimal immunity in the population. Consequently the suitability of the viral strains included in the trivalent influenza vaccine (TIV) has to be re-evaluated annually. While virus seeds selected for vaccine manufacture are very well characterized, there is no assay in place to identify the source of HA in the formulated trivalent vaccine. Our study describes a proteomics-based method to identify the HA strain (not just subtype) and more fully characterize the final vaccine product. Unique and shared tryptic peptides of HAs were predicted by in silico tryptic digest of different influenza A and B virus strains. Recombinant HA and whole virus preparations of selected strains were then digested to identify the peptides detected by MS. Both subtype and strain-specific peptides were observed. The feasibility of this method to accurately identify HA strains in an inactivated TIV was tested using a 2006/2007 formulation. Each of the three HAs in the vaccine was identified in addition to a number of other viral and non-viral proteins. In summary, MS is a powerful method that is both specific and inclusive; in a single analysis, HAs of individual virus strains can be identified and the composition of the TIV fully characterized.

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