Nowadays, hearing about Artificial Intelligence no longer refers to something abstract and impractical, but it is undeniable how it has entered into our daily lives. Looking specifically at the world of healthcare, the evolution of AI models can make a significant contribution to the research and development of revolutionary new technologies.
Based on these assumptions, one could arrive at the easy conclusion that at any moment the healthcare system could undergo a revolution in favour of AI, but, very often, the word innovation collides with the word difficulty.
If the initial scepticism, after the release of the first natural language models, such as OpenAI's GPT-3, is slowly fading away given the rapid evolution of the sector, on the other hand, one often comes across legacy systems that are not up to date. Just think of the fact that the majority of Italian healthcare facilities do not have cloud infrastructures.
From my experience as an IT consultant in the world of healthcare, I often notice that the desire to bring innovation is not always reflected in the national landscape.
An initial distinction, which is not insignificant, is found trivially when dealing with private or public bodies. Private structures, in fact, often lend themselves to innovation with the aim of becoming points of reference or carrying out research. Also from an economic point of view, it follows that the availability of resources is greater within these entities.
On the other hand, the search for innovation even in the public world often finds its way blocked by numerous obstacles. Undoubtedly, one of the main limits is economic availability.
This problem has undoubtedly, over time, inhibited the progressive evolution of infrastructures, favouring the creation of legacy systems that are difficult to dismantle. There is no doubt that the use of Artificial Intelligence tools, such as multi-agent or single-agent architectures, can benefit everyone, but it is quite complicated to implement such systems.
These systems are not easy to implement.
While this proves to be one of the main problems, other factors typical of the Italian system must also be considered, namely power games within companies and the timing of bureaucracy.
However, in order to make healthcare more efficient, scalable, sustainable and open to technological innovation, it is essential to address these complexities in a concrete manner, promoting solutions that integrate clinical, IT and organisational skills.
The healthcare system is a complex, complex and difficult one.
Clinical and scientific practices produce a significant amount of data on a daily basis, the potential of which risks not being exploited if adequate IT tools are not available.
One of the biggest problems in research is the lack of data: one of the main objectives of the restructuring of the NHS is to build state-of-the-art infrastructures that make data easily accessible and, above all, comply with the required standards, such as the FHIR (a model for health data that is flexible and adaptable, as well as being able to promote interoperability between different systems).
These are the main objectives of the restructuring of the NHS.
Through the construction of Data Platforms, an attempt is therefore being made to move in this direction. The advent of AI can facilitate access to data even more: through the use of LLM it is possible, starting from a small amount of data, to proceed to the creation of synthetic data, which can be used for the creation of Machine Learning models. In the case of rare diseases or even, for example, in cancer research, the available data is often small and there is also the problem of data privacy. The possibility of creating synthetic data is also fundamental in this respect: it becomes possible to train models using a large amount of data, which is, at the same time, consistent and usable for everyone.
An example of the concrete use of Large Language Models (LLM) and advanced AI architectures is not limited to specific applications, but opens up much broader scenarios. The real challenge today is to address the structural limitations of healthcare in a systemic way, turning them into opportunities.
The LLM and AI architectures are not limited to specific applications.
The adoption of artificial intelligence entails a profound rethinking of organisational models, data governance, the training of healthcare personnel and the doctor-patient relationship. The lack of interoperability between systems, poor digital knowledge on the part of staff and the absence of a unified strategic vision are recurrent obstacles. Cloud infrastructure, for instance, is an enabling condition to fully exploit the potential of AI, but its adoption is still sporadic and uneven.
At the same time, the lack of interoperability between systems, the lack of digital knowledge by staff and the lack of a unified strategic vision are recurrent obstacles.
At the same time, the systemic potentials of AI in healthcare are enormous: from personalised medicine to predictive care, from supported diagnostics to continuous remote monitoring of patients, and the automatic generation of clinical documentation. However, the key lies not so much in the availability of the technologies, but in their integration within a healthcare ecosystem that enhances data, protects privacy and fosters interdisciplinary collaboration.
Data privacy is precisely a fundamental principle within agent architectures. Indeed, AI agents cannot directly access sensitive data or make autonomous decisions concerning patients' health. Architectures defined as human-in-the-loop will be the future benchmark of healthcare. Within these architectures, AI agents will be able to quickly analyse data, enabling rapid feedback to healthcare personnel, reducing the current timeframes caused by a lack of staff or inefficient procedures.
Still on the subject of privacy and security, there are already protocols that could enable the creation of these architectures, such as MITRE's ATLAS Matrix or the OWASP framework.
Artificial intelligence represents one of the most relevant and promising challenges for the future of healthcare. Its deployment is not a purely technical issue, but a complex process that requires strategic vision, ethical responsibility and a profound rethinking of organisational models.
The potential is already before our eyes: faster diagnostics, personalised medicine, clinical decision support. But for this transformation to be truly incisive, we need to act today on culture, training and infrastructure. Only in this way can artificial intelligence become a true ally for a fairer, more accessible and human healthcare system. There is still a long way to go, but the evolution is already underway, and AI is emerging as an indispensable ally for the healthcare of the future.
