Cancer is a global disease. As every year on February 4, its world day is celebrated. The laws of statistics, implacable, predict that with a very high probability this disease will affect us or one of our loved ones during the course of our lives.

Thanks to the generous contributions of many, the encouragement of civil institutions and work by companies and public and private organizations from research, the 20th century has witnessed impressive advances in the knowledge of the causes of cancer. It has also seen improvements in the diagnosis and treatment of cancer and, consequently, a longer and better quality of life for those affected. In these advances, engineering has played a fundamental role through the development of new technologies that have made it possible to detect this devastating disease earlier, study it in greater depth and treat it more efficiently.

However, we perceive that the fight against cancer continues to be an unequal battle. In some ways it is, because the progress made is small compared to the extent of the disease and the seriousness of its consequences, both for individuals and for societies. I think that one of the possible reasons for this reality is the dispersion of the battle against cancer. Despite the fact that globalization is evident in other aspects of our lives, and despite the clear efforts of institutions to join forces and promote synergies between national and international research groups, the fight against the disease, from the point of view of management and the use of data and information, continues to be what in the military terminology is called a "guerrilla war".

In this field of the still "local" fight, a growing number of scientific publications issue show the surprising learning capacity of artificial intelligence methods, encompassed in what is known as Deep Learning. These methods, capable of learning and identifying patterns in poorly structured clinical data , have allowed the development of pilot diagnostic tools in Anatomy Pathology and Radiology. As an example of the potential of these tools, a recent competition (Camelyon 16), showed how Deep Learning based systems are able to detect breast cancer metastases in lymph nodes more accurately than a panel of expert histopathologists. Other similar programs of study have shown the potential of these systems for the diagnosis of cancerous skin lesions, or for the prediction of cardiovascular disease risk from retinal fundus images.

In the examples cited, the ability of these systems to "learn" has required the use of significant amounts of clinical information from more or less diverse sources. The improvement of these preliminary results, and the extension of these systems to other more complex pathologies, would require the use of a greater quantity and variety of clinical data , correctly annotated by specialists.

What is the key to ensuring that these evident 'partial' victories that have been achieved begin to translate into global progress? In addition to continuing efforts to develop quality, adequately financed research , engineering also has a fundamental role to play in this new phase.

I would venture to mention two key points. On the one hand, the consolidation and correct use of global clinical information, which in turn requires the improvement of the storage and transfer systems of data, and the overcoming of some legal and cultural barriers that prevent the 'socialization' of clinical data . And, on the other hand, the generalization of the use of learning tools based on artificial intelligence in clinical settings.

For the former, it is necessary that clinical information - with the consequent safeguarding of confidentiality - can be easily shared, so that it can be used to train and teach new systems based on artificial intelligence.

The latter requires the awareness of medical professionals. Far from seeing artificial intelligence as a possible 'competitor', they should see it as a powerful tool capable of improving the quality of their work. In this way, and thanks to the massive use of information, the globality of cancer would cease to be part of the problem and become part of the solution.