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The challenge of artificial intelligence

After decades of living in a common internal market, many people seem to have forgotten how important it is, and how easy.

Therefore, she says, it is 'necessary to continue strengthening the internal market, in particular in a cross-border context, removing unjustified barriers and ensuring that the existing rules are properly and timely implemented and enforced.'

'Since emerging technologies, such as artificial intelligence and blockchain, are becoming key drivers for economic development and enhance the value of goods and services, we will be following with the utmost attention any developments in this area', she said, adding that this is important for both business and consumers.

But all in all, De Sutter thinks that the speedy growth of artificial intelligence will become the biggest challenge, needing 'legislation linked to transparency, liability, safety and ethical rules for digital platforms, services and products'.

On the other hand 'consumer protection rules have to ensure that consumers have clear information on how to use AI-enabled products and services, that they have control over data generated by such products and services, and how that data is used', she said.

Artificial intelligence for global health

Deep learning, a subset of machine learning based on artificial neural networks, has enabled applications with performance levels approaching those of trained professionals in tasks including the interpretation of medical images and discovery of drug compounds (1).

Health conditions in LMICs and HICs are rapidly converging, as indicated by the recent shift of the global disease burden from infectious diseases to chronic noncommunicable diseases (NCDs, including cancer, cardiovascular disease, and diabetes) (2).

Both contexts also face similar challenges, such as physician burnout due to work-related stress (3), inefficiencies in clinical workflows, inaccuracies in diagnostic tests, and increases in hospital-acquired infections.

With increasing smartphone penetration, patient-facing AI applications may guide lifestyle and nutrition, allow symptom self-assessment, and provide advice during pregnancy or recovery periods—ultimately allowing patients to take control of their health and reducing the burden on limited health systems.

This is particularly important in oncology, where lack of subspecialists may force an oncologist to manage tumors across multiple anatomical sites, and thus deliver care of inferior quality owing to the constantly varying scope of services.

In radiotherapy, for example, semi-automation of the treatment planning process may speed up treatment delivery, increase patient intake, and allow greater focus on the clinical nuances of patient management—all without requiring additional personnel.

The third application area relates to population health and allows public agencies to realize cause-and-effect relationships, appropriately allocate the often limited resources, and ultimately mitigate the progression of epidemics (8).

Automated registry curation, by extracting standard data from free-form text found in radiology and pathology reports, may help reduce labor costs that account for more than 50% of all registry activity expenses (9).

Other applications include identifying hotspots for potential disease outbreaks in unmapped rural areas by utilizing AI-powered analysis of aerial photography and weather patterns, as well as planning and optimizing CHWs' household visiting schedules.

Ethical concerns about the use of AI in health care include undermining patient data privacy protections and exacerbating the existing tension between providing care and generating profit, as well as introducing a third party into the patient-doctor relationship, which changes expectations of confidentiality and responsibility (10).

Data for AI training and validation must be context specific: Computer vision systems may be required to work with legacy data formats (e.g., film versus digital x-ray), whereas developing chatbots will require compiling corpora in local languages.

Infrastructure constraints should be assessed, including the availability of devices for serving AI applications, reliability of internet connectivity and bandwidth, electricity, and the amount and quality of existing digital data, as well as future digitization efforts.

Many of the challenges faced by integrating electronic medical records in LMICs, for example, are likely to also impede AI applications, including limited funding, poor infrastructure for reliably delivering technologies, and discontinuous participation from users (12).

AI-driven interventions should not be evaluated in isolation, nor should they be regarded as a universal panacea: Although sizable initial investments may be required, the marginal cost of providing an existing AI software service to one more user is minuscule, giving it economical scalability.

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