The synergy between artificial intelligence and life sciences: a revolutionary interplay

Monday 29 April 2024

Ricardo Costa Macedo
Caiado Guerreiro, Lisbon

Inês da Silva Henriques
Caiado Guerreiro, Lisbon

Throughout history, technological developments have played a fundamental role in revolutionising the provision of healthcare. Corroborating this premise, artificial intelligence (AI) is currently leading the Fourth Industrial Revolution, opening an unprecedented era of advances in healthcare technology through the introduction of technological tools into the life sciences. Seeking to enlighten the synergy between this disruptive technology and the life sciences, this article explores the major developments arising from the integration of AI into the various branches of the life sciences, as well as the main challenges and ethical considerations that accompany these developments.

Technology is characterised by its transformative nature, with technological developments challenging realities that, in each epoch, were considered immutable. The field of life sciences has not escaped this transformative impetus, with the narrative demonstrating that technological developments have played a fundamental role in revolutionising the provision of healthcare. From the discovery of penicillin in the 1940s to the development of implantable artificial organs, the introduction of technological tools in the life sciences early on presented unprecedented opportunities for advances in healthcare technology.

At the moment, AI and machine learning (ML) are leading the Fourth Industrial Revolution, significantly impacting areas that are central to the day-to-day life of the 21st century citizen. These technologies are not only capable of bringing innovations that were thought to be reserved for science fiction, such as autonomous vehicles or virtual assistants, into the realm of reality, they are also drastically changing the quality of healthcare. De facto, disruptive technologies are revolutionising not only the way we live, work and interact with each other but also the way we research, develop, test and manage data in the life sciences. This revolution is enabling a pace of discovery that has no historical precedent, driven by the integration of AI tools into the various branches of life sciences.

Considering the possibilities that can be extracted from AI, it is not surprising that research teams are increasingly turning to AI tools to assist them in accomplishing their tasks. Through its enhanced analytical capabilities and its ability to automate tasks that were previously subject to human intervention, AI is making it possible to optimise time-consuming tasks from research and development teams that have traditionally relied on inefficient and time-consuming manual processes, in turn allowing researchers to focus on other tasks. Furthermore, the introduction of AI into the life sciences enables the development of medicines more efficiently and less expensively, makes robot-assisted surgery possible, identifies patients ready for treatment and improves patient outcomes. The aforementioned potential that AI presents is already being harnessed by large pharmaceutical companies, rendering investment in AI a priority for many.

Vis-à-vis the rapid evolution of scientific discoveries, the intersection of AI and life sciences has emerged as a transformative force that is gradually reshaping the way biological systems are understood, analysed and manipulated. This synergy promises to accelerate advances in medicine, genomics, drug discovery and personalised healthcare. In this article, we aim to briefly explore the interconnection between AI and life sciences, exploring how these two domains collaborate to unlock new frontiers.

Genomics and AI

The decoding of the human genome was a monumental achievement in life sciences, providing a complete map of our genetic constitution. Nevertheless, the enormous complexity of genomic data requires advanced computational methods to interpret it exhaustively. This is where AI and its ability to analyse extensive data sets, identify intricate patterns and extract significant knowledge from the analysed data comes in. AI does this faster and more efficiently than a human being, given the latter's inherent limitations.

AI may help to analyse genomic data to identify genetic variations, understand disease mechanisms and develop personalised medicine approaches. This approach presents the possibility of speeding up many aspects of the research and development process in the life sciences field because genomic data is incredibly complex and traditionally requires the allocation of a lot of time and resources to be analysed thoroughly. De facto, AI and ML algorithms are capable of rapidly processing these data sets, identifying patterns and correlations much faster than human analysis alone. This analysis, in turn, permits the identification of genetic variants associated with specific diseases, accelerating the pace at which new diagnostic and treatment strategies may be developed.

Therefore, one of the most promising applications of AI in genomics is in the field of precision medicine and personalised treatment. By analysing individual genomes, AI is able to predict susceptibility to certain diseases, adapt treatment plans based on genetic markers and optimise responses to drugs. This truly personalised approach increases the effectiveness of medical interventions, minimising adverse reactions and maximising therapeutic results.

Drug discovery and development

AI provides an opportunity to depart from conventional drug discovery methodologies, which are inherently time-consuming and costly, and usually require years of research and countless tests to complete. AI has emerged as an essential tool in this process, reformulating it in order to speed up the processes of drug discovery and development, from target identification to clinical trials. Specifically, AI facilitates the identification of potential drug candidates from compound libraries. AI algorithms are capable of analysing extensive amounts of data, including molecular structures, biological interactions and existing drug databases, which permits the prediction of potential drug candidates with higher success rates, extending the scope of exploration to a greater number of compounds and the interactions between them. This analytical capacity, in turn, allows the early stages of drug discovery to be accelerated, reducing the time and resources needed to identify and validate targets.

Simultaneously, AI, and especially ML, demonstrate the possibility of harnessing existing drugs for new therapeutic purposes. Through analysing large amounts of scientific literature, clinical trial data and patient records, AI algorithms are capable of uncovering concealed connections between drugs and diseases, offering an efficient alternative to developing new drugs from scratch. This approach may significantly speed up the drug development process because repurposing existing drugs removes the need for extensive safety and toxicity tests that have already been conducted.

Disease diagnosis and prognosis

In healthcare, a timely and accurate diagnosis is paramount for effective patient treatment. AI tools are increasingly becoming valuable allies in diagnostic processes, offering improved capabilities in analysing large amounts of medical imaging data and detecting patterns and anomalies in medical images, such as X-rays, computed tomography (CT) scans and magnetic resonance imaging (MRI scans), which in turn may help healthcare professionals to deliver more accurate and timely diagnoses.

Furthermore, AI's ability to analyse vast datasets, including electronic health records, genomic information and treatment history, enables the development of personalised prognostic models that estimate disease progression and patient outcomes. This information may guide treatment planning, optimise therapeutic strategies and provide patients with personalised prognostic information, while allowing healthcare professionals to implement proactive interventions, potentially preventing the progression of certain diseases.

Challenges and ethical considerations

Although the synergy between AI and life sciences presents enormous potential, this synergy is, as with any AI intervention in the most essential areas of life, fraught with challenges and ethical considerations.

The primary challenge arising from this synergy is data privacy and security. The integration of AI into the life sciences field requires analysing large amounts of sensitive data, which could raise concerns about safeguarding patient information and their genomic data, especially if this data is shared between different institutions or companies. Faced with this possibility, it is fundamental to ensure that data is anonymised and protected against unauthorised access. Data security is an equally important concern because AI systems may be vulnerable to cyberattacks, such as data theft or manipulation of algorithms, which could lead to incorrect diagnoses or inadequate predictions, jeopardising patients' health.

Another major concern centres on the biases that AI algorithms may demonstrate. AI algorithms are trained on the basis of existing data sets, which may contain implicit bias, and consequently, culminate in discriminatory decisions regarding the treatment of certain ethnic or social groups. It is therefore essential to monitor and mitigate these biases in order to guarantee fair and inclusive healthcare outcomes for all patients.

Furthermore, the 'black box' nature of certain AI systems renders it challenging for humans to understand how a specific result was achieved, which creates serious doubts regarding their reliability. Because transparency is crucial in healthcare, it is essential to ensure that AI algorithms are understandable and explainable for humans in order to gain the trust of healthcare professionals and patients.

Finally, the frenetic expansion of AI and the possibilities that this technology represents in the field of life sciences requires solid regulatory frameworks. Striking a balance between promoting innovation and ensuring patient safety is a complex challenge that requires collaboration between researchers, policy-makers and regulatory bodies. Nevertheless, vis-à-vis a scenario in which the use of AI tools is becoming commonplace in the life sciences, it is imperative to implement appropriate regulations to guarantee the ethical and responsible use of AI while harnessing the advantages that the application of this technology may represent for the advancement of healthcare and, in totum, of humanity. Only in this way will it be possible to guarantee that the synergy between AI and life sciences benefits the whole of humanity, without causing harm or injustice.