AI-generated summary
Precision medicine, which tailors treatments using genetic and molecular data, promises more effective healthcare with fewer side effects and less invasive testing. Experts from the Bankinter Innovation Foundation’s Megatrends 2024 highlight the role of technological tools as co-pilots for doctors in implementing this medical revolution. Combining genome sequencing, gene editing, nanotechnology, and artificial intelligence (AI) can lead to more accurate diagnoses and faster drug discovery while maintaining the economic sustainability of healthcare systems. Although a fully autonomous virtual doctor is still a distant goal, human-technology partnerships are expected to reduce errors, enhance productivity, and provide better, more affordable patient care.
Key enabling technologies include Big Data analytics, AI, and machine learning (ML), which process vast amounts of genetic, clinical, and environmental data to identify health risks and predict treatment responses. Advanced radiological techniques like radiomics extract detailed insights from imaging, while 3D printing and biomaterials enable customized drugs, organs, and devices. Wearable devices support continuous monitoring and personalized treatment. Successful applications are evident at Madrid’s La Paz University Hospital, where AI improves diagnostic accuracy and patient management across various specialties. Ultimately, realizing precision medicine’s full potential requires collaboration between public and private sectors, ethical data use, and involving health professionals to ensure technology acts as a supportive co-pilot rather than a replacement, fostering gradual adoption and broad accessibility worldwide.
In the field of health, the goal is to take advantage of technology and integrate it into medical care towards personalized medicine.
The promises of precision medicine, which uses genetic and molecular data to create personalised and more effective treatments, with fewer invasive tests and side effects, are remarkable, and technological tools will act as co-pilots for doctors in the practical application of this revolution, as pointed out by the experts consulted by the Bankinter Innovation Foundation’s Megatrends 2024.
The technology, in fact, has the potential to transform medicine, with the combination of genome sequencing, gene editing, nanotechnology, and artificial intelligence (AI) that will enable more accurate diagnoses and accelerate the discovery of new drugs. All this, preserving the economic sustainability of the health system.
That said, we are still far from the infallible virtual doctor, capable of replicating the human ability to empathize with the patient to perform what in medical jargon is called anamnesis. However, the partnership between humans and technology will reduce error rates, improve productivity, and generally provide better, less expensive care for patients.
Enabling technologies
The data-driven evolution, the Big Data and advanced analysis techniques are the main enablers of precision medicine , which is in fact based on the valorisation of huge volumes of genetic, omics, clinical and environmental data. The sources of this data can be complex biomolecular analyses and clinical analyses, but also portable sensors and diagnostic imaging.
Artificial intelligence and machine learning (ML), on the other hand, is essential to efficiently process the large volumes of data coming from bioinformatics systems, helping, for example, to identify correlations between risk factors (genetic) and specific health conditions. Machine learning is also valuable in developing predictive models of response to treatments, while AI applied to biological data can help fight antibiotic-resistant bacteria, making it easier to create new drugs.
On the other hand, advanced radiological image analysis techniques (e.g., CT scan and MRI) can revolutionize not only the diagnosis but also the management of the disease. Radiomics, in particular, uses advanced analytics to extract quantitative and qualitative information from radiological images, enriching the understanding of pathology with information that goes far beyond that visible to the radiologist’s eye.
3D printing and biomaterials, on the other hand, make it possible to create ad hoc drugs, but also organs, tissues and personalized devices that improve the efficiency of transplants and reduce the risk of rejection. Finally, wearable devices play a fundamental role in opening up a personalized perspective in the preventive, diagnostic and therapeutic fields. Some of these tools provide a continuous flow of data, becoming a pillar of the monitoring activity, but also of the definition of Personalized treatments.
Examples of success
All these technologies will improve health monitoring and provide real-time advice, favoring the prevention and early detection of diseases, while reducing costs and extending access to personalized care, extending people’s healthy lives. In general, technology is transforming medicine into a more predictive, preventive, personalized, equitable and participatory discipline and there are already several examples of success.
The La Paz University Hospital in Madrid is leading the integration of artificial intelligence in healthcare in Spain. The institution attends more than one million consultations, 230,000 emergencies and 49,000 admissions each year, and AI automates routine processes improving efficiency in patient care, for example through the generation of pre-reports of CT scans and a project to reduce the time to diagnosis of rare diseases that reaches an accuracy of 80-90%.
The hospital also uses AI in diagnostic imaging, personalized medicine, advanced genomics, and neurotechnology. In addition, in neurology, tools are being developed for movement analysis and language recovery in patients with aphasia, while in oncology, La Paz has created predictive survival models for hematopoietic transplants and AI is used to determine the pathogenicity of genetic variants in cancer and predict specific treatments.
The perspectives of the precision medicine are very encouraging; However, only the collaboration and joint commitment of public and private actors will make it possible to transform this vision into a reality and, above all, into a practice accessible to all. In fact, all of these innovations together have the potential to reduce the costs associated with hyper-personalized healthcare and thus extend it to the maximum number of people around the world.
Of course, leveraging AI to maximize the value of healthcare involves ensuring data quality and the ethics in the implementation of algorithms, involving health professionals in the design of technological solutions, which can well improve diagnosis, personalize treatments and support clinical decisions, but always in the role of co-pilot and with the final criteria of doctors. A transformation of this magnitude inevitably generates some resistance, which must be addressed gradually, progressively and continuously.
