Empowering Healthcare Designers: Navigating AI's Emerging Frontier of Impact

A Recap from Design Wednesdays Journal Club

On October 4th, 2023, The Better Lab hosted a special edition of their Design Wednesday Journal Club, an initiative aimed at fostering discussion, critical thinking, and the exchange of ideas on health and design. For those unfamiliar, The Better Lab is a design-focused entity dedicated to improving healthcare through human-centered design. Their Journal Club offers a collaborative space for healthcare professionals, students, and innovators to critically appraise relevant literature and emerging trends within the health and design community.

This particular session featured an in-depth look at Artificial Intelligence (AI) in healthcare design and its potential implications for the healthcare system. Mariana Salvatore, the design director of The Better Lab, moderated the session, stepping in for Bella Shah during her clinical shift at Kaiser, Oakland. Mariana emphasized the importance of the Journal Club as a platform for professionals to connect, share ideas, and propose future speakers.

The featured speaker for the session was Soren DeOrlow, the founder of Resonance Partners, a San Diego-based innovation consultancy. With a rich portfolio that includes Johnson & Johnson, Stanford Medicine, Athena Health, Kenvue, and UCSF's Department of Surgery, Soren brings a wealth of experience in harnessing emerging technologies to drive innovation in healthcare. With multiple patents and a Master's in Integrated Design, Business, and Technology from USC, his insights into AI and human-centered design offer a unique perspective on how cutting-edge technologies can transform healthcare workflows, improve patient outcomes, and reduce clinician burden.

This session of the Journal Club began with a review of two articles that set the stage for a deep dive into AI. The first article from Stanford Medicine's Scope Blog, featured an interview with Dr. Chen discussing the promises and pitfalls of AI in healthcare, particularly the automation of administrative tasks and the risks posed by current AI systems that may generate inaccurate information. The second article from Anthropic, expanded on AI safety, exploring the challenge of building reliable and ethically steerable systems that will integrate seamlessly into all areas of society.

The conversation that followed, led by Soren, explored a wide range of critical topics in AI. He addressed ethical considerations, AI as a general-purpose technology, and foundational concepts like transformers and attention mechanisms that are advancing natural language processing (NLP). Soren also covered applications such as named entity recognition, computer vision, and the evolving role of large language models (LLMs) in enhancing performance, including through retrieval-augmented generation (RAG). Additionally, he highlighted the exciting potential of future multi-modal capabilities and autonomous agents, framing AI not just as a tool for human mimicry but as an exponential technology with vast implications. The discussion underscored the need for careful integration, balancing innovation with reliability, safety, and ethical responsibility.

Four Recommendations for Navigating AI's Emerging Frontier of Impact

Recommendation 1: Prioritize Ethical Considerations and Responsible Applications of AI

"AI should act with care & humility." — Ben Mann, Co-founder Anthropic

With any application of AI it is important to consider unintended consequences that might occur. There are numerous thought leaders who are leading the discussion around the ethics of AI. Anthropic is one of them. They have taken a robust approach to AI ethics and applied human-centered values in the way they are building their LLM, Claude. Claude is built with "Constitutional AI" prioritizing ethical principles, embracing personal privacy, respect and avoidance of toxicity, by focusing on principles of care, honesty and avoiding harm. Healthcare innovators should draw inspiration from Anthropic when developing with exponential technologies.

Recommendation 2: Understand AI as a General-Purpose Technology

One of the most compelling points to be made on this emerging domain is the importance of reframing AI as a "general-purpose technology" rather than focusing solely on its intelligence. Much like electricity or the internet, AI is a foundational technology that can be applied across industries, driving innovation and efficiency. For healthcare, this could mean everything from reducing administrative burdens (like filling out electronic medical records) to improving diagnostic accuracy and patient outcomes.

AI's potential economic impact is substantial, with some estimates predicting a global boost of 7% in productivity due to AI adoption. However, it is important to caution that while the technology is advancing rapidly, healthcare must proceed with caution to avoid repeating the AI winters of the past, where hype led to disappointment.

Recommendation 3: Think of Modern AI as Exponential Technology

For many decades, the Turing Test created a benchmark that all artificial intelligence endeavors should strive to re-create human intelligence. One of the key shifts in AI, or rather "exponential technology," is not just about replacing human intelligence, it's about augmenting it. "Narrow" applications of AI focus on specific tasks that can be implemented with strict parameters and perform great utility.

In healthcare, narrow AI can help clinicians work more efficiently by automating routine tasks, allowing them to focus on patient care. The combination of human expertise and AI's computational power presents an exciting opportunity to transform the healthcare landscape by augmenting care delivery and streamlining administrative tasks.

Recommendation 4: Develop a Curiosity for the Key Technology Components of Modern AI

Below is a brief summary of key technology components or "building blocks" within the field of AI:

• Natural Language Processing (NLP) — A sub-field of computer science and AI focused on enabling computers to understand human language. In 2017 a seminal paper, "Attention is all you need," dramatically changed the landscape of NLP and was the precursor to large language models.

• Named Entity Recognition (NER) — A subset of NLP that helps computers understand language by classifying words that have a hierarchical relationship to one another.

• Computer Vision — Uses convolutional neural networks (CNNs) to teach computers information through digital images. The power of computer vision has shown great promise in radiology where in some cases, computers have been able to outperform human experts.

• AI Autonomous Agents — Engineered to anticipate and perceive an environment around them, comprised of percepts which initiate a percept sequence.

• Generative AI — A significant breakthrough in NLP is the transformer and attention mechanism technology that emerged in 2017, paving the way for text generation (GPT, Llama, Claude), image generation (Stable Diffusion, DALL-E, Midjourney), and medical/scientific AI (Med-PaLM, AlphaFold).

• Retrieval Augmented Generation (RAG) — A way to improve the factual quality of LLMs without retraining them. LLMs can utilize RAG to search private data repositories to customize how they function.

Startups are exploring how to combine all of these exponential technologies to create bespoke multi-modal AI agents.