Artificial Intelligence has left the realm of science fiction, becoming an integral part of our lives. It is the ability of computers to perform actions that were previously only performed by humans that has brought about this change. This has resulted in greater efficiency in the use of resources and greater speed and precision in certain tasks.

Choosing a better route in traffic, scheduling banking transactions, or using specific voice commands such as "Hello, Siri," "Ok, Google," or "Alexa, turn off the lights" to activate and interact with virtual assistants are already familiar activities. However, when we introduce the opportunity to automate processes in healthcare, we are looking at the transformation of an entire industry.

AI is the driving force behind the most disruptive innovations on the market. From personalized experiences to accelerated new product development, it is redefining the way companies create and deliver value to their customers. According to the McKinsey Global Survey, 60% of organizations worldwide have introduced AI in, at least, one of their business processes.

In the medical field, this tool enables the completion of tasks related to patient diagnosis, the implementation of public health interventions, such as pandemic monitoring, the acceleration of the development of new and more efficient drugs, and the timely and efficient distribution of these drugs to the appropriate locations.

The implementation of AI in the healthcare sector is becoming increasingly important. Frost & Sullivan projects that by 2025, 90% of hospitals and insurers in the United States will be using these systems. AI is no longer a futuristic concept; it is a catalyst that benefits a network of governments, hospital institutions, researchers, the medical community, the pharmaceutical industry, and other stakeholders in the healthcare ecosystem, all while keeping people at the center.

For instance, AI's capacity to analyze hundreds of patient data sets and clinical test results enables more accurate diagnoses. Additionally, it can identify patterns and details in medical images that are not perceived by the human eye, thus detecting early lesions in cancer or cardiovascular disease. Another avenue is through medical devices or wearables (watches, bracelets, and glasses) that monitor a person's vital signs, sending alerts to their doctor about their state of health in real time. This can be the difference between life and death. 

AI can also help people connect with healthcare. Today, there are new generations of chatbots (programs that automatically interact with people) with generative AI capabilities, such as ChatGPT, that not only adapt to the conversational style of a human user, but also retain and learn preferences to answer questions that arise outside the hours of a medical consultation. They can also store information to relate it in case of an emergency, schedule an additional review, and help the doctor make decisions about the treatment to be followed.

The future looks bright, and this is just the beginning. The best is yet to come, even for the pharmaceutical industry, which has always been quick to adapt to new technology and trends. For example, in R&D, it has been using artificial intelligence to:

1. Discover and develop new drugs. Translational medicine, which combines genomics, bioinformatics, AI, and big data, among other tools, has the potential to reduce the time and high costs involved in the discovery of new molecules and the development of first-in-class therapies for complex diseases with unmet needs or for which there are no available treatments. One illustrative example is the new generation of immunotherapies, such as CAR-T, in which the patient's T cells are modified and reprogrammed to identify and attack cancer cells. These have improved the prognosis of patients with hematological diseases where other treatments have not been effective.

2. Improve drug adherence and dosing, as well as creating personalized therapies. knowmad mood, a technology consultancy, points out that improved adherence and dosing is an achievement brought by the machine learning model (a branch of AI that allows machines to learn without being specifically programmed to do so) to help monitor use and increase therapeutic adherence rates of prescribed drugs. 

With AI, we can also identify at-risk populations and apply personalized therapies based on their disease biology — even before they show symptoms. Computer modeling allows us to predict which treatments will be most effective by adjusting for each patient. This avoids the frustration of trying multiple treatment schemes and, of course, reduces the associated costs.

3. Identify patients for clinical trials. Recruiting the right people is complex and often delayed, but AI is helping to speed things up by focusing on diversity and inclusion. Its algorithms analyze historical data and medical records to optimize design and planning, which are crucial for success. In addition, platforms like blockchain can revolutionize the way patient data is stored, accessed, and protected thanks to their cryptographic and decentralized architecture.

4. Make the supply chain more efficient. AI can help in drug manufacturing by detecting machine failures, reducing waste, and ensuring quality. In distribution, warehousing, and marketing, machine learning can predict demand, which helps optimize logistics and inventory.

In its study AI in Health: Huge Potential, Huge Risk, the Organization for Economic Cooperation and Development (OECD) found that last year, in Europe, 163,000 people could have died because of medical errors due to communication failures. AI has the potential to improve these processes by providing the right information to the right people at the right time and in the right context, which could save lives.

When used correctly and in accordance with best practices (WHO, 2023), AI enhances evidence-based medicine, improving health outcomes and person-centered care. In addition, up to 36% of healthcare activities can be automated, so doctors can spend more time with patients, improving the quality of care and human interactions.

In my opinion, to make this process faster, it is crucial to promote digital literacy and develop talent in emerging technologies. A Pharma Market study indicates that healthcare data scientists, biometric engineers, bioinformatics, regulatory and ethical experts in AI, and machine learning specialists will be needed.

Undoubtedly, Information and Communications Technology (ICT) is changing the rules of the game. It is now imperative to construct new ecosystems that facilitate the adoption of innovations capable of addressing all that is not possible in a "traditional environment.” Agility allows us to anticipate and meet the challenges of digital health 4.0. How are you preparing for this transformation?