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The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations.

Objective: To report the clinical validation of an innovative, artificial intelligence (AI)-powered, portable and non-invasive medical device called Wound Viewer. The AI medical device uses dedicated sensors and AI algorithms to remotely collect objective and precise clinical data, including three-dimensional (3D) wound measurements, tissue composition and wound classification through the internationally recognised Wound Bed Preparation (WBP) protocol; this data can then be shared through a secure General Data Protection Regulation (GDPR)- and Health Insurance Portability and Accountability Act (HIPAA)-compliant data transfer system. This trial aims to test the reliability and precision of the AI medical device and its ability to aid health professionals in clinically evaluating wounds as efficiently remotely as at the bedside., Method: This non-randomised comparative clinical trial was conducted in the Clinica San Luca (Turin, Italy). Patients were divided into three groups: (i) patients with venous and arterial ulcers in the lower limbs; (ii) patients with diabetes and presenting with diabetic foot syndrome; and (iii) patients with pressure ulcers. Each wound was evaluated for area, depth, volume and WBP wound classification. Each patient was examined once and the results, analysed by the AI medical device, were compared against data obtained following visual evaluation by the physician and research team. The area and depth were compared with a Kruskal-Wallis one-way analysis of variations in the obtained distribution (expected p-value>0.1 for both tests). The WBP classification and tissue segmentation were analysed by directly comparing the classification obtained by the AI medical device against that of the testing physician., Results: A total of 150 patients took part in the trial. The results demonstrated that the AI medical device's AI algorithm could acquire objective clinical parameters in a completely automated manner. The AI medical device reached 97% accuracy against the WBP classification and tissue segmentation analysis compared with that performed in person by the physician. Moreover, data regarding the measurements of the wounds, as analysed through the Kruskal-Wallis technique, showed that the data distribution proved comparable with the other methods of measurement previously clinically validated in the literature (p=0.9)., Conclusion: These findings indicate that remote wound assessment undertaken by physicians is as effective through the AI medical device as bedside examination, and that the device was able to assess wounds and provide a precise WBP wound classification. Furthermore, there was no need for manual data entry, thereby reducing the risk of human error while preserving high-quality clinical diagnostic data.

In July 1997, telemedicine services were established for two islands, Procida and Ischia, in the Bay of Naples. The telemedicine network allowed a 24 h consultation service to be provided between the islands' health centres and mainland hospitals in the towns of Pozzuoli and Giugliano. Realtime videoconferencing was used, over an ISDN link at 384 kbit/s; radiographs were digitized using a scanner. During 1999 and 2000, there was an average of 46 telemedicine consultations per year from Procida (60% radiology, 30% cardiology, 10% other), 35% of which were emergencies, and 16 training sessions per year; there was an average of 28 telemedicine consultations per year from Ischia (80% radiology, 10% cardiology, 10% other), 10% of which were emergencies, and 12 training sessions per year. The average duration of a telemedicine consultation was 15 min. The total fixed cost of the telemedicine system between the two islands and the two mainland hospitals was approximately Euro 48,000 per year. The total variable costs of the telemedicine service at the observed workload were about Euro 750 per year. The total savings due to telemedicine were approximately Euro 40,000 per year. There was therefore little difference in the overall costs of health-care delivery. The positive consequences of the introduction of the telemedicine service included more rapid diagnosis and treatment, improved quality of service, and the transfer of skills and education. A costs-consequences analysis suggested that the telemedicine service was worthwhile.