Advancements in Artificial Intelligence: A Growing Powerhouse in Healthcare

The landscape of artificial intelligence (AI) is rapidly evolving, showcasing its ability to learn and improve through vast data inputs. This evolution is exemplified by recent accolades in the field, such as the 2024 Nobel Prizes awarded to pioneering researchers in AI, including John Hopfield and Geoffrey Hinton for their foundational work in the field, and David Baker, Demis Hassabis, and John M. Jumper for their innovative developments in protein structure research. Their contributions, particularly through AI-driven computational models like AlphaFold and RosettaFold, have revolutionized drug development processes.

AI's current capabilities stem from extensive advancements in natural language processing and artificial neural networks (ANN), which simulate the data processing of human neurons. These networks enhance their outputs by optimizing data exchanges through weighted connections, achieving greater complexity through multiple layers and high-quality training datasets.

OpenAI's breakthrough with ChatGPT 4.0 in 2023 demonstrated the potential of AI to utilize a vast array of publicly available online data, ranging from social media platforms to academic resources, significantly surpassing earlier models in performance. This leap was made possible by substantial investments in computing power and strategic marketing efforts.

However, the computational demands for training AI models like ChatGPT 4.0 are significant, with estimates suggesting a consumption of around 62 GWh per model, comparable to the electricity usage of a small city over a few weeks. The International Energy Agency currently estimates the global energy consumption for all AI applications at approximately 500 TWh annually, a figure that could escalate with increased adoption of AI technologies.

Despite the advantages, the surge in AI-generated content also leads to a proliferation of low-quality information, akin to spam. This phenomenon, referred to as 'AI Slop,' necessitates the implementation of filters to distinguish valuable information from the noise generated by both AI and human sources.

AI applications have found their way into various sectors, including marketing, customer service, and business operations. Virtual influencers and AI-driven chat systems are increasingly used by companies to engage with customers. However, the prevalence of AI-generated content raises concerns about information reliability and authenticity, prompting some users to seek out platforms that ensure higher content quality.

In the medical and pharmaceutical fields, AI has made significant strides. For instance, AI systems have demonstrated diagnostic capabilities in dermatology that rival those of human specialists. Applications like a skin type assessment app developed by pharmacist Marc Kriesten highlight AI's potential to enhance patient care by providing tailored recommendations based on individual skin types.

With the advent of explainable AI (XAI), which aims to clarify the decision-making processes of AI systems, user acceptance may increase further. In research environments, AI is beginning to play a role in drug repurposing through a 'human-in-the-loop' approach, allowing for substantial human oversight before final outcomes are determined.

As AI systems become more integrated into educational frameworks, students report that they can serve as effective learning partners, often providing accurate information when used correctly. However, the quality of AI responses heavily relies on the underlying data and the specificity of user prompts.

Future developments in AI may lead to the creation of specialized models that outperform larger, generalized systems, potentially improving both efficiency and resource utilization. Advances in retrieval-augmented generation (RAG) technology illustrate how AI can provide tailored responses by integrating specific contextual information from various sources.

Yet, the current limitations of AI include its inability to critically assess information or recognize biases in its training data. The journey toward achieving artificial general intelligence (AGI) remains uncertain, as researchers explore whether biological intelligence mechanisms can be adapted for AI systems.

In practical terms, AI can assist pharmacies by automating routine tasks such as call screening and invoice processing, ultimately streamlining operations and reducing the burden on human staff. The potential for AI to enhance customer interactions in pharmacies is recognized, though it is suggested that AI should primarily serve in a supportive role for human pharmacists.

Regulatory frameworks, such as the EU's Artificial Intelligence Act, are being established to ensure the responsible deployment of AI technologies, particularly in healthcare settings, emphasizing the need for safety and transparency in AI applications.