This chapter focuses on the enhancement of medical services through the integration of unmanned aerial vehicle (UAV) technology and machine learning algorithms. It explores the broad spectrum of applications and benefits that arise from combining these two technologies. By employing UAVs for automated delivery, medical supplies can be efficiently transported to remote or inaccessible regions, thereby improving access to vital items. Remote patient monitoring, facilitated through UAVs and machine learning, enables real-time data collection and analysis, enabling the early identification of health issues. UAVs equipped with medical equipment and machine learning capabilities enhance emergency medical response by providing immediate assistance during critical situations. Disease surveillance and outbreak management can benefit from the use of UAVs and machine-learning algorithms to identify disease hotspots and predict the spread of illnesses.
Top1. Introduction
Unmanned aerial vehicles (UAVs) and machine learning can completely change the way medical services are delivered. UAVs are remotely controlled or self-flying aircraft. Machine learning is the ability of computer systems to learn from experience and make improvements without being explicitly programmed (Pathak, Damle, Pal, & Yadav, 2019). These two technologies working together can have a huge impact on the medical industry. Figure 1 illustrates how machine learning algorithms can be used to evaluate vast volumes of medical data and find patterns that can be utilized to enhance illness detection, treatment, and prevention.
Figure 1. Working steps for implementing medical service through UAV
UAVs, on the other hand, can be used to carry patients and deliver medical supplies in locations that are challenging to access with conventional transportation. The use of UAVs and machine learning in emergency response is among the medical industry's most promising uses. Time is of the essence in emergencies, and a quick response could be the difference between life and death. UAVs can be used to quickly deliver medical supplies to remote locations or disaster situations, such as blood and medication. To estimate the severity of the problem and prioritize response times, machine learning algorithms can also be utilized to analyze data from emergency response calls (Motlagh et al., 2023).
Application in disease surveillance and outbreak response is yet another promising area. To find possible disease outbreaks, machine learning algorithms can be used to examine data from a variety of sources, including social media and news reports. Then, to gather samples and transport medical supplies to the impacted areas, UAVs can be used.
Machine learning and UAVs can also be used to enhance patient care in addition to these applications. For instance, patient data can be analyzed using machine learning algorithms to determine the best courses of action for specific patients. Patients can obtain care without having to go far thanks to the usage of UAVs for transporting medical supplies and equipment (Estrada & Ndoma, 2019).
1.1 Overview of UAVs and Machine Learning
Drones, commonly referred to as unmanned aerial vehicles (UAVs), are aircraft that are flown remotely or automatically without a human pilot present. Due to their adaptability and capacity for a variety of activities, including aerial surveillance, mapping, package delivery, and search and rescue missions, UAVs have grown in popularity in recent years (Motlagh et al., 2023).
A branch of artificial intelligence called machine learning (ML) involves teaching computer systems to recognize patterns and make predictions based on data. Numerous applications, such as image identification, natural language processing, and predictive analytics, use machine learning (ML) methods.
There are several methods to combine UAVs and machine learning to allow new capabilities and enhance already existing ones. Machine learning techniques, for instance, can be used to analyze data from UAV sensors, like cameras and lidar, to automatically detect and classify items of interest, like structures, moving vehicles, and people (Yao, Qin, & Chen, 2019). UAVs may be able to carry out activities more effectively and precisely as a result, including monitoring traffic, inspecting infrastructure, and carrying out search and rescue missions.
By allowing UAVs to make decisions based on real-time data from their sensors, machine learning can also be utilized to increase their autonomy. A UAV with machine learning algorithms, for instance, may automatically modify its fly route to avoid obstacles or navigate through difficult settings.