Artificial intelligence (AI) has become a significant factor in shaping the future of healthcare over the past few years, but its real-world application brings both great opportunities and serious challenges. Technology that can analyze vast amounts of data with incredible speed and accuracy has the potential to transform how we approach health and public safety. However, as AI tools are increasingly implemented in healthcare, the question arises of how to ensure that they genuinely improve health outcomes rather than create new problems.

One of the key challenges facing the creators of these systems is the collection of quality data. In many countries, particularly in less developed regions, health data collection systems are limited, inconsistent, or outdated. Without properly collected data, algorithms can produce inaccurate results, which may jeopardize patient health outcomes. Quality data is crucial for the proper functioning of AI, but collecting it can be challenging due to inconsistent standards and privacy concerns.

Another significant challenge is bias within algorithms. The data on which these systems are trained often do not represent a sufficiently broad spectrum of the population. For example, algorithms developed in highly developed countries may exhibit biases towards health data from affluent nations, ignoring the health challenges and needs of low-income countries. This can exacerbate inequalities in healthcare, as populations that are already vulnerable may be further neglected in an AI-driven medical system.

In the field of diagnostics, AI has shown great advantages. Algorithms capable of analyzing medical images and patient data can identify diseases earlier than the human eye can. For instance, AI tools for cancer diagnostics can assist physicians in detecting malignant cells at earlier stages, improving survival chances for patients. However, although these tools are promising, their integration into real clinical practice is not always straightforward. Healthcare workers often face challenges in adapting their workflows to new technologies, which can lead to frustration or misuse of the tools.

Another concern relates to the privacy and security of data. AI systems require access to large amounts of patient data, including sensitive information such as genetic data or medical history. If this data falls into the wrong hands, it could have catastrophic consequences for patient privacy. Despite advancements in security measures, there is always a risk of data breaches, making this technology vulnerable to cyber threats. Legal regulations to protect patients and healthcare systems from these threats are still being developed, meaning that many countries are unprepared for proper oversight of AI implementation in healthcare.

Collaboration and ethical considerations

One of the key factors for the success of AI implementation in healthcare is collaboration between various sectors—from technology creators to healthcare workers, political actors, and civil society. Only through inter-sectoral collaboration is it possible to develop ethical guidelines that will ensure technology serves patients, not just the industry. For example, collaboration between academic institutions, technology companies, and non-governmental organizations has already led to the creation of models tailored to the specific needs of marginalized groups. However, the lack of global regulations and standards makes it difficult to establish a unified approach to this technology.

The question of ethics is particularly important in the context of public health. Historically, many technological innovations in healthcare were first applied to the most vulnerable groups, often without their consent. To avoid repeating those mistakes, AI must be developed with respect for ethical standards that include patients' rights to privacy and informed consent. The application of AI in public health must be designed in a way that ensures equitable access to health services, regardless of patients' social or economic status.

Despite all the challenges, the potential of AI in healthcare remains enormous. Properly developed and regulated AI tools can drastically improve the efficiency of healthcare systems, reduce costs, and provide access to quality care for a larger number of people. The role of politicians, regulatory bodies, and civil society in the coming years will be crucial in ensuring that AI becomes a tool for progress, not a new source of inequality.

Source: University of California