{"id":2148,"date":"2024-05-16T20:16:57","date_gmt":"2024-05-16T18:16:57","guid":{"rendered":"https:\/\/www.rivistaeco.com\/?p=2148"},"modified":"2024-05-27T17:59:47","modified_gmt":"2024-05-27T15:59:47","slug":"artificial-intelligence-and-medicine-a-relationship-opening-new-horizons","status":"publish","type":"post","link":"https:\/\/www.rivistaeco.com\/en\/2024\/05\/16\/artificial-intelligence-and-medicine-a-relationship-opening-new-horizons\/","title":{"rendered":"Artificial Intelligence and Medicine: A Relationship Opening New Horizons"},"content":{"rendered":"

The use of artificial intelligence in medicine allows for a deeper understanding of diseases, thus enabling the development of more effective drugs for increasingly personalised therapies. However, it is essential to train a new generation of doctors and researchers capable of advancing these two disciplines in parallel.<\/span><\/i><\/p>\n

The application of Artificial Intelligence in biomedical sciences is not new. The first attempts to use artificial intelligence algorithms for medical diagnosis date back to the 1960s and 70s. In 1975, Rutgers University in New Jersey, USA, particularly its unit focused on the application of computer science to biomedical sectors, hosted the first of a series of conferences on AI in medicine (A.I.M. workshop). Since then, the applications of AI in medicine have continuously increased, and the expectation is that this technology will revolutionise life sciences in the coming years. Why so much renewed enthusiasm after fifty years of application?<\/span><\/p>\n

Big Data, Life Sciences, and Medicine<\/b><\/h3>\n

The last few decades have witnessed two profound changes in the biomedical field: an unprecedented increase in the ability to acquire biological data and a rapid maturation of computational methodologies useful for organising, analysing, and interpreting them. Starting with the former, many disciplines within life sciences and medicine have recently undergone a profound technological change, which in turn has enabled unprecedented data production. Not only the speed of data production but also their quality and resolution have improved rapidly, thus meeting the necessary requirements for AI application. For the first time in human history, we are collectively acquiring more data than we can interpret, analyse, and use. Genomics \u2013 the study of our genes and their biological function \u2013 is the biomedical field that has probably seen the largest explosion. The ability to sequence, i.e., “read,” our genome, or more precisely, to determine the exact order of the nucleotide bases that make up the DNA of an organism, was demonstrated for the first time at the beginning of the millennium with the publications of the first (almost) complete human genome sequences. The first publication in the scientific journal Nature on 15 February, 2001, described the preliminary results of the Human Genome Project, an international initiative involving research institutes and scientists worldwide. The second study was published on 16 February, 2001, in the scientific journal Science, by Celera Genomics Corporation, a sequencing technology industry. These detailed maps of the DNA sequences present in our genome marked a turning point in the history of biology and medicine and paved the way for new discoveries and applications in genetics, molecular biology, and personalised medicine. Compared to the early projects, which lasted decades and cost hundreds of millions of dollars and pounds, the technology has developed beyond all expectations in the subsequent two decades, far exceeding Moore’s law predictions (in the computer hardware industry, this law predicts the doubling of computing power every two years). The immense amount of data produced leads genomics to become comparable to, and indeed to quickly surpass, other traditional “big data” areas, such as astronomy and social media. Moreover, today it is possible to sequence not only DNA but also other molecules, such as RNA and proteins, making the data not only numerous (“big”) but also “rich,” i.e., rich in information.<\/span><\/p>\n

AI and the Interpretation of Biomedical Data<\/b><\/h3>\n

These data are so numerous and rich that without the methods provided by artificial intelligence, we could not even begin to consider them, let alone use them. Consequently, AI is applied to the analysis of genomic data at all levels: from the challenge of managing and organising petabyte-scale DNA sequence data (a petabyte is equal to a quadrillion bytes), to the development of algorithms that allow the identification of genes associated with specific phenotypes, i.e., the set of morphological and functional characteristics of an organism. Furthermore, the use of genetic data together with environmental and behavioural data makes it possible to predict the risk of complex diseases such as cancer, metabolic, cardiovascular, respiratory, or immune diseases. AI, particularly machine learning, i.e., automatic learning, is an essential tool for guiding the development of new drugs. It can help build models capable of predicting a patient’s response to a particular treatment, based on the biological data obtained from a biopsy, before and after treatment, and on the individual’s clinical history, leading to more personalised and effective treatments. For many years, AI has been applied and has shown particularly promising results in clinical imaging and disease diagnosis. The analysis of clinical images, such as those acquired from X-rays, CT scans, and MRIs, can improve the recognition of anomalies and the identification of clinically significant trends that are invisible to the human eye. In this field, AI algorithms based on “deep learning” have the potential to reveal aspects of the data that elude other methods. This helps radiologists and other healthcare providers detect diseases at a very early stage, when treatment is more likely to be effective.<\/span><\/p>\n

How Generative AI Helps Research<\/b><\/h3>\n

The high computing capabilities of the latest supercomputers have greatly facilitated the application of AI. Since artificial intelligence requires large amounts of data from which to learn, it is essential to have machines capable of efficiently managing that volume of information. On the other hand, in recent years we have also seen the emergence of an increasing number of new technologies, for example, tools produced by generative AI. Generative AI uses artificial neural networks to learn from data and produce models of the same data, which can then be used to generate new content. A typical example is generative text models, where machine learning is used to analyse large amounts of existing text, learn linguistic patterns, and then use them to generate new texts, which are coherent and appear to be written by humans. Language is a highly visible example, with an increasing number of commercial products and free applications now available to everyone, but this type of AI can be used in many contexts, including medicine. And although these methods are still experimental, and sometimes embryonic, they already make very significant contributions to the sector. An example is AlphaFold, an AI algorithm that predicts the three-dimensional structure of proteins with high precision, crucial for their biological function. Solving and understanding these structures represents a fundamental challenge, essential for fully understanding their functions and how they may be altered at the onset of a disease. Obviously, it would be preferable to have a protein structure determined experimentally, but this is a difficult, lengthy, and laborious process that requires in some cases years of work. Therefore, having a computational model that is able to predict them with high precision not only accelerates scientific discovery processes but provides information that makes the development of new drugs more rapid and effective. AlphaFold is based on a generative model that from the sequence of DNA predicts the structure of the protein produced from that sequence. The interesting aspect is that the algorithm needs only the DNA sequence data, which are easy to acquire; for some groups of proteins, the prediction is extremely accurate.<\/span><\/p>\n

Security, Transparency, and Ethics<\/b><\/h3>\n

As with any new technology, the application of AI to medicine must meet strict ethical, safety, and transparency standards, and this is not without challenges. To develop useful models, it is necessary to process large amounts of high-quality data. It is therefore necessary to collect, organise, and process patient data rigorously respecting privacy and confidentiality. On the other hand, multiple levels of data processing, filtering, and cleaning involve inherent risks, for example, they can make the data less representative of what happens in clinical practice. Moreover, reliable validation of AI models is essential to ensure that they are unbiased, thus avoiding disparities in the administration of medical care. Indeed, AI can help identify and overcome acquired but suboptimal patterns. There is thus an urgent need for regulation on the adoption of AI, and for adequate training and education of healthcare providers.<\/span><\/p>\n

Interdisciplinarity and Training<\/b><\/h3>\n

Although it offers extraordinary research possibilities, the high number and complexity of biological and medical data require significant computing capabilities and further development of analytical approaches. The use of AI to analyse and interpret these data requires a parallel evolution of computational theory. It is therefore necessary to train a new generation of doctors and researchers capable of exchanging knowledge and skills, and who know how to jointly address unanswered scientific questions. It is not only essential to acquire competence in new sectors, but it is also vital to be able to work in teams composed of professionals covering all disciplines, from computational and technical to biological and medical. This is a necessary condition for producing effective AI methods and applying them appropriately to new biomedical data.<\/span><\/p>\n

Francesca Buffa<\/strong> is a Full Professor in the Department of Computing Sciences at Bocconi University and also heads the laboratory of computational biology and integrative genomics at the Department of Medicine at the University of Oxford.<\/span><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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