Artificial General Intelligence: A New Era In Healthcare Innovation
AGI is considered a transformative technology with significant potential, particularly in revolutionising healthcare
In its 2022 Hype Cycle for Artificial Intelligence, Gartner introduced the term Artificial General Intelligence (AGI), placing it at the beginning of the “Innovation Trigger” phase. By 2023, AGI had swiftly advanced to the end of this phase, moving towards the “Peak of Inflated Expectations.” This rapid progression indicates that the industry must evaluate AGI’s potential for disruption and reassess current strategies to identify necessary adjustments.
AGI is considered a transformative technology with significant potential, particularly in revolutionising healthcare. To understand this better, we need to explore the investments being made, projected growth, the transition from the current state, and the business cases the industry is considering today.
AGI Investments and Growth
Artificial Intelligence is projected to grow at a CAGR of over 37 per cent between 2022 and 2030. Currently, more than 80 startups are focusing on AGI, alongside major technology leaders like Microsoft, Google, and IBM. In 2023, AGI startups raised over USD50 billion globally, including corporate investments from companies like Alphabet, which has invested billions in its DeepMind subsidiary, and government funding. These figures highlight the significant financial commitment and growth potential in the sector.
The relevance of AGI in the world of Healthcare
The AI we commonly know is now referred to as Narrow AI, Classical AI, or Weak AI, to distinguish it from AGI. Narrow AI systems excel at specific tasks, often outperforming humans. For example, a weak AI system can identify tumours from X-ray or ultrasound images more quickly and accurately than a trained radiologist. AGI, on the other hand, represents a significant leap, capable of performing any intellectual task that a human can. For instance, an AGI system could not only diagnose a wide range of diseases from various medical images but also integrate patient history, genetic information, lifestyle factors, and real-time health monitoring to provide a comprehensive and personalised treatment plan. This makes AGI particularly promising for healthcare.
Transitioning from Narrow AI to AGI in Healthcare
To move from Narrow AI to AGI-based solutions, healthcare systems need to become more creative in analysing large amounts of patient data. This includes enhancing social and emotional intelligence, visual and audio perception, fine motor skills, and natural language processing (NLP). With advancements in large language models (LLMs) and Generative AI, we are closer to achieving creativity in AI. Improved encoding techniques that capture the depth of human emotions will further enable AI to develop emotional intelligence.
Use Cases of AGI in Healthcare
Diagnostics: Current AI systems are proficient in diagnosing diseases from medical images like X-rays and MRIs. AGI could integrate data from various sources—patient history, genetic information, lifestyle factors, and real-time health monitoring—to provide comprehensive and accurate diagnoses. This holistic approach could lead to earlier disease detection and more personalised treatment plans.
Personalized Medicine: AGI could predict how different patients will respond to various treatments based on their unique genetic makeup and other factors. This would enable healthcare providers to tailor treatments to individual patients, improving outcomes and reducing side effects.
Enhancing Patient Care: AGI has the potential to enhance patient care by providing continuous monitoring and intelligent support. Wearable devices and smart home systems already collect real-time data about a patient’s health and environment. AGI could analyse this data to detect anomalies and provide timely interventions, such as alerting healthcare providers if a patient with a chronic condition needs immediate attention or adjusting medication dosage based on real-time health metrics.
Additional Use Cases: AGI can also bring significant changes in other areas, such as building virtual health assistants to provide patients with real-time medical advice and monitor health conditions, enable robotic surgeries, bolster mental health support systems, and accelerate drug discovery.
The advent of AGI could herald a new era in healthcare, characterised by more accurate diagnostics, personalised treatments, and enhanced patient care. However, realising this potential will require addressing ethical, practical, and technical challenges. Integrating AGI into healthcare systems will also necessitate substantial investment in infrastructure and training.
As we stand on the brink of this technological revolution, we must ask ourselves if we are prepared to navigate the complexities and responsibilities that come with human-AI collaboration as medical data can often be incomplete or ambiguous and ensuring that AGI systems do not perpetuate or exacerbate existing biases in healthcare is paramount. The promise of AGI is immense and the stakes are higher than ever before – how we choose to develop, implement and regulate this technology will shape the future of healthcare for generations to come.
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