Photoplethysmogram (PPG) signal reacts according to the volumetric change of blood, that is related to the total cardiovascular system. Hence PPG signals have a very close relation with cardiovascular diseases. As the number of cardiovascular disease (CVD) patients is increasing day by day, it is necessary to develop digital systems for early detection and treatment. In this study, we have aimed to design a field programmable gate array (FPGA) based hardware system that can detect various CVDs. We have chosen six different combinations of diseases from the PPG BP dataset. The selected signals are denoised using filters, and then features are extracted. Then SVM classifier is used to determine the class of the preprocessed signals. For FPGA implementation, we have targeted Xilinx zynq ultrascale+. The system utilizes different logic and signal processing blocks to detect the result. The resource utilization analysis proves the efficacy of the selected FPGA board. The power utilization analysis depicts that the designed prototype requires 1.403 W of power, which is sensible due to the requirement of large number of logic blocks. The accuracy of the system of detecting multiclass diseases is 79.83%. In the future, the system can be further extended to detect other types of CVDs.

The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%).

Artificial intelligence (AI) is increasingly used in healthcare to support clinical decision-making through clinical decision support systems (CDSS). Human-in-the-loop (HITL) approaches introduce clinician oversight to improve model interpretability, reliability, and adaptability, while explainable AI (XAI) helps clinicians understand model behaviour. This review categorises HITL AI approaches in healthcare into pre-deployment and post-deployment stages and provides a dedicated review focusing specifically on post-deployment HITL systems. It also introduces the concept of closed-loop AI, where real-time expert feedback can refine AI outputs without requiring model retraining. A systematic review following PRISMA guidelines was conducted using the Scopus and PubMed databases for studies published between 2020 and July 2025. From 3466 identified records, 3012 remained after duplicate removal. After title and abstract screening, 1630 articles were assessed through full-text review, and 15 studies met the predefined inclusion criteria related to HITL, post-deployment adaptation, and interactive XAI in healthcare. The selected studies indicate growing interest in post-deployment HITL systems that allow clinicians to refine AI outputs, provide real-time feedback, and support adaptive CDSS. These findings highlight a shift toward human-centred, closed-loop AI frameworks that integrate expert feedback into deployed systems to improve transparency, trust, and responsiveness in clinical decision-making.

Hypertension is one of the leading causes of cardiovascular disease morbidity in the world. If remains untreated, it may cause severe damage like heart attack or even death. Early detection is required to prevent the development of other cardiac abnormalities. Photoplethysmogram (PPG) is a bio signal that can be obtained optically by a sensor. It is studied to monitor the change of volume of blood and detect heart conditions. Previous studies have already applied PPG to detect hypertension at the software level. In this article, along with software-based detection, we have implemented a digital hardware-based system for detecting hypertension from signals recorded using PPG sensor. Xilinx ZedBoard Zynq-7000 field programmable gate array (FPGA) board is utilized for designing the embedded system. The hypertension detection accuracy is 98.02% at the software level while for the digital system, it is 96.05% consuming 0.374 W power. The study can be analyzed for other cardiac disease detection and medical equipment development.