Artificial intelligence (AI) has burst onto the current scene with tools such as ChatGPT, which has surprised with its everyday uses (automatic text generation, providing information and answers, entertainment, programming…) and has led large companies (such as Microsoft and Google) to invest in it or to promote their own services.
However, AI’s capabilities, which have the potential to offer even more groundbreaking and disruptive applications, are only just beginning to be harnessed. For example, it has the potential to respond to the healthcare sector’s challenges: improving people's health, accelerating clinical and biomedical research, and providing more efficient services by reducing costs and diagnosis time.
With this objective in mind, TartaglIA has been launched, an ambitious project which seeks to accelerate AI implementation in the Spanish healthcare system and help both patients and healthcare professionals by reinforcing the prevention and early diagnosis of diseases.
Named after the Italian Niccolò Fontana 'Tartaglia', one of the leading mathematicians of the 16th century, this healthcare project brings together 16 leading healthcare and technological institutions – including Opinno – who are working together to maximize the use of artificial intelligence in medicine.
In Opinno’s case, its role focuses on 3 main areas: research and discovery of current processes (data and variables that will be taken into account to generate the algorithms, patient journey, pain points and user needs…), design of the user experience and interface, and front-end development.
“Our role in the project is to transform this algorithm, which is something quite abstract, into an experience and a tool that can be interacted with and that really provides value or solves problems” on a day-to-day basis, especially for medical specialists, who are the main beneficiaries, explains Laura García, who oversees the project at Opinno.
In addition to the healthcare challenges, the design of algorithms or AI training, “the great challenge at the technological level is to generate federated networks through which the data are made available for this research, but they never leave the networks to which they belong,” explains García.
In this way, for the first time, the obstacle that slows down the adoption of AI tools in healthcare (the lack of a large volume of data, as these are usually isolated in multiple different centers) is overcome, and at the same time, the privacy and security of this sensitive information is guaranteed.
From there, the ultimate and ideal goal would be to find “the earliest, least invasive, easiest and cheapest diagnosis possible” for various diseases: all in a long-term project, with a timeframe from the end of 2021 to the end of 2024.
Opinno is involved in four areas of work in TartaglIA: clinically significant prostate cancer (early diagnosis through AI), Alzheimer's (detection through computational analysis of spontaneous language), complex chronic (a person with several chronic diseases simultaneously) and diabetic retinopathy (research into predictive models for automatic screening using AI).
“The future that is envisioned is for hospitals and healthcare centers to be able to share data on their patients in a much more agile, secure and private way. That way, research in any field would advance much faster,” García concludes about the project's mission.