In this blog post, we aim to provide a high-level outlook of what’s in store for AI in healthcare, offering a comprehensive perspective on its transformative role. So, let’s take a close look of the upcoming advancements and potential future developments in the field.
Regulation for Adaptive Algorithms: The regulation of adaptive algorithms has always been a challenge due to their ever-changing nature. Currently, only locked algorithms have received approval. However, this year, the U.S. Food and Drug Administration (FDA) has pledged to address this issue by releasing regulatory descriptions or recommendations to guide the field. To further aid this regulatory process, we will soon be launching a database where users can search for AI healthcare patents, providing valuable insights into future healthcare landscapes.
Specialty-based AI Recommendations and Guidelines: The American College of Radiology has taken a significant step by introducing instructions on submitting studies and papers related to AI technology, along with guidance on how radiologists should approach reading AI studies. Implementing such recommendations and guidelines across all medical specialties would greatly enhance professionals’ understanding and utilization of AI technologies in their respective fields.
Regulation for Large Language Models (LLMs): Given the widespread daily use of large language models like ChatGPT and Google’s MedPaLM by millions of people, including doctors and patients, it is inevitable that some form of regulation will be implemented this year. However, regulating LLMs poses unique challenges, such as interpretability, fairness, and unintended consequences. Tokenization, a crucial aspect of LLMs in natural language processing, currently lacks healthcare regulation and demands attention. A diverse and tailored regulatory framework is essential, considering the broad applicability of LLMs across various domains, with healthcare being the most intricate and critical.
The Debate over Banning Large Language Models: Some countries or regions may opt for a ban on large language models as a response to potential concerns. However, such measures may not effectively address the underlying issues. Users with moderate motivation can easily find ways to circumvent regulations, rendering bans ineffective. A better approach involves leveraging the potential of LLMs through appropriate regulations and user education on working with AI responsibly, rather than resorting to extreme measures.
Exploring Undiscovered Medical Areas for AI Solutions: While AI solutions will continue to flourish in established areas like radiology and oncology, we are also eager to witness the emergence of new fields, including mental health. This exciting prospect indicates that AI adoption will extend beyond automation-prone domains, creating innovative models that combine AI chatbots with human therapists to deliver personalized support to paid subscribers. The integration of AI in these novel areas promises transformative outcomes.
AI in the Everyday Lives of Healthcare Professionals: Millions of doctors, nurses, and healthcare workers are set to explore various AI-based tools that boost efficiency, such as voice-to-text applications for reviewing output. The healthcare sector will benefit from a wide array of AI-driven tools designed not only for medicine but also for a broad range of tasks, from website building to content creation. These tools empower professionals, enabling them to focus more on patient care.
AI’s Trojan Horse in Pharma: Drug Discovery: AI is going to break into the pharmaceutical industry through its application in drug discovery. This area offers substantial potential for cost savings and profit generation, making it an attractive target for AI. With the ability to create new molecules virtually and significantly accelerate the drug discovery process, AI can revolutionize early-stage research, potentially saving years and millions of dollars.