Telemedicine is the use of technology to provide healthcare services and information remotely, without requiring physical proximity between patients and healthcare providers. The coronavirus disease 2019 (COVID-19) pandemic has accelerated the rapid growth of telemedicine worldwide. Integrating artificial intelligence (AI) into telemedicine has the potential to enhance and expand its capabilities in addressing various healthcare needs, such as patient monitoring, healthcare information technology (IT), intelligent diagnosis, and assistance. Despite the potential benefits, implementing AI in telemedicine presents challenges that can be overcome with physician-guided implementation. AI can assist physicians in decision-making, improve healthcare delivery, and automate administrative tasks. To ensure optimal effectiveness, AI-powered telemedicine should comply with existing clinical practices and adhere to a framework adaptable to various technologies. It should also consider technical and scientific factors, including trustworthiness, reproducibility, usability, availability, and cost. Education and training are crucial for the appropriate use of new healthcare technologies such as AI-enabled telemedicine. This article examines the benefits and limitations of AI-based telemedicine in various medical domains and underscores the importance of physician-guided implementation, compliance with existing clinical practices, and appropriate education and training for healthcare providers.

BACKGROUND: Ensuring the security and privacy of patient data is a critical concern in the healthcare industry. The growing utilization of electronic data transmission and storage in medical records has amplified apprehensions about data security. However, due to varying stakeholder interests, not all data can be freely shared, necessitating the development of secure protocols. MATERIALS AND METHODS: This study presents a highly secure protocol that integrates blockchain technology, patient biometric information, and robust cryptographic algorithms (elliptic curve cryptography (ECC) and advanced encryption algorithm (AEC)) to facilitate data encryption and decryption. The protocol encompasses secure login, secure key sharing, and data sharing mechanisms among miners, offering comprehensive security measures. To validate the effectiveness of the proposed protocol, both informal and formal security analyses are conducted. The security protocol description language in Scyther is utilized to evaluate the protocol’s resilience against attacks. RESULTS: The culmination of this research is a secure protocol that leverages blockchain technology and ECC for the secure storage and sharing of medical records. The protocol covers all stages, including system setup, user registration, login mechanisms, key exchange between users and blockchain, communication between blockchains, and interaction with other miners, with a steadfast emphasis on security. Furthermore, the protocol’s communication and computation costs are assessed, with a comparison to existing blockchain-based schemes. Informal proofs establish the protocol’s security against common attacks faced by medical institutions. Formal simulation of the protocol using the Scyther tool provides definitive evidence of its resistance to attacks. CONCLUSIONS: As a result, this protocol presents a viable real-time implementation solution for safeguarding patient data within the healthcare domain, representing a significant contribution to data security.

Background: Artificial intelligence (AI) is revolutionizing medical diagnosis and healthcare, providing constant support to medical practitioners. Intelligent systems alleviate workload pressure while optimizing practitioner performance. AI and deep learning have also improved medical imaging and audio analysis. Material and Methods: This research focuses on predicting respiratory diseases using audio recordings from an electronic stethoscope. A convolutional neural network (CNN) was trained on a Respiratory Sound Database, augmented to generate 1,428 audio files. Techniques such as pitch shifting, time stretching, noise addition, time and frequency masking, dynamic range compression, and resampling were employed to increase the diversity and size of the training data. Result: Features were extracted from mono audio files, creating a four layer CNN with 90% accuracy. The software, developed using the CNN model and Streamlit python library, offers a new tool for early and accurate diagnosis, reducing the burden on medical practitioners and enhanci ng their performance. The study highlights AI’s potential in respiratory disease detection through audio analysis. Conclusion: The software, developed using the CNN model and Streamlit python library, offers a new tool for early and accurate diagnosis, reducing the burden on medical practitioners and enhancing their performance.

The Internet and communications have rapidly expanded, leading to a significant rise in data generation and heterogeneity. Intrusion detection systems play a crucial role in ensuring the security and integrity of computer systems. These systems have been developed by researchers, academicians, and practitioners to effectively detect and mitigate network attacks. Intrusion detection systems are designed to analyze network traffic and compare it with a baseline of normal behavior, allowing them to identify any deviations or inconsistencies that may indicate an intrusion. Furthermore, the cooperative and distributed architecture of intrusion detection systems enables them to effectively detect attacks and protect the network from unauthorized access. Additionally, to enhance the performance of intrusion detection systems, techniques such as resampling the dataset and utilizing classifier ensemble are used to improve the classification accuracy. Moreover, intrusion detection systems have been integrated with intrusion response systems to ensure a timely and effective response to detected attacks. AI-based Intrusion Detection Systems have emerged as a crucial tool in ensuring network security and combating cyber threats. These systems utilize artificial intelligence algorithms to analyze network traffic, identify patterns of malicious activity, and detect potential cyber-attacks. They have proven to be highly effective in improving the detection accuracy, reducing false alarms, and even detecting previously unknown types of attacks. In summary, the development of accurate and efficient intrusion detection systems is crucial for ensuring network security. In today’s rapidly changing world, the significance of accurate intrusion detection systems cannot be overstated.