However, these apprehensions often stem from a lack of understanding of this “new” technology. Despite AI’s prevalent integration AI into our daily lives, it is far from capable of entirely replacing human labour. While historical trends suggest that new technologies may displace existing jobs, they also invariably create new ones. Nonetheless, it is indisputable that Artificial Intelligence is set to fundamentally transform numerous industries, including healthcare and pharmaceuticals.
Credit: AITAD
Artificial Intelligence encompasses a spectrum of variations rather than one single concept. It is necessary to differentiate between “generative AI”, which creates something new based on available datasets, like ChatGPT, and “discriminative AI”, which identifies and analyses correlations within large datasets. Recently, the focus has shifted towards decentralised, autonomous AI applications functioning independently of continuous server connectivity. In contrast to its centralized counterparts, which run on servers and thus rely on connectivity, the “Embedded-AI” approach offers a variety of new application possibilities.
A deeper data evaluation through Embedded-AI solutions
In simplified terms, a decentralized or Embedded Artificial Intelligence operates locally on chips at the data generation site, thus eliminating the need for cloud connectivity. Although initially developed on large servers, this AI is then compressed through a complex and sophisticated process so that it can run on minimal computing units. Such chips with an integrated Embedded-AI only take a few centimetres in size and thus can be implemented in all kinds of systems.
Once trained, such decentralized AI can work autonomously on the device without depending on its original server connection. Through profound data analyses, the AI can then significantly enhance device performance and efficiency. While the processed data packages can hardly be transmitted to a server due to their immense size, an Embedded-AI enables advanced functionalities that significantly improve safety and precision, especially in medical applications.
Concerns about doctors losing their profession to machines are unfounded, asserts Viacheslav Gromov, founder of Embedded-AI developer AITAD: “While Artificial Intelligence stirs groundless fears about its potential of replacing our entire middle class, its real value lies in addressing more pressing and tangible problems. Particularly in healthcare, the new megatrend can address challenges like an ageing population and a shortage of skilled professionals.”
Subsequently, these concerns raise the question of how this technological progress can be slowed down or regulated when it should actually be encouraged. Embedded-AI, particularly in medical and pharmaceutical fields, already offers solutions to counteract these challenges. The technology can lead to significant societal advancements, such as in the realm of recurring routine tasks, through functions such as intelligent user interaction, predictive maintenance, and other advanced functional innovations that were previously unattainable.
Transforming Healthcare with Embedded-AI
Embedded AI redefines the interaction between humans and machines, allowing for seamless operation through gestures or voice commands. While large numbers of healthcare professionals still must engage in simple routine tasks, these operations can be assumed by Embedded-AI with higher precision and reliability. For example, the AI can adjust surgical devices, thus freeing up professional staff to focus on more critical aspects of patient care. “Through Embedded-AI, more patients can be treated with the same or even higher quality by fewer staff, thereby solving two pressing issues simultaneously,” Gromov is convinced.
Moreover, Embedded-AI’s predictive maintenance capabilities are revolutionary, providing detailed insights into equipment conditions. While the processed data is often incomprehensible for humans or regular algorithms, the AI can detect impending malfunctions via vibration patterns, sound waves, or pressure trends. Such predictions not only save manufacturers millions in maintenance services costs but also enhance patient safety, as device failures during surgeries can be avoided. Additionally, maintenance services can be planned in the most resource-efficient way.
Besides these applications, decentralised Artificial Intelligence allows for further innovative functionalities supporting real-time evaluations and critical decision-making in medicine. AI can safeguard surgeons to avoid even minor mistakes and thus serve as a safety net for both medical staff and patients. While the surgeon must still lead the entire surgical process, the AI can prevent operational errors. In turn, a central AI cannot perform this safeguarding function, as even the little time required for the data transmission from device to server will be too long to prevent the mistake.
Viacheslav Gromov further emphasizes, “Embedded-AI’s potential extends across the medical technology spectrum, offering unprecedented precision, safety and reliability. While data can be autonomously evaluated at their point of origin – without the need to transfer these data sets – these solutions are not only remarkably precise and robust, but they also safeguard data integrity and patient confidentiality. Embedded-AI can therefore serve as a strategic response to the shortage of skilled workers while also providing an additional safety net for patients and staff.”