Healthcare generative AI is predicted to reach $22 billion by 2032, driven by strategic partnerships and AI solutions in drug discovery, diagnostics, and patient care. The Generative AI Tracker, a collaboration between Pymnts and AI-ID, values the market at $1 billion in 2022. It highlights the potential for AI to revolutionize healthcare, reshape diagnostics, and accelerate drug development. However, challenges include data access, regulation, and a lack of resources and expertise. Promising startups like Epic and Huma are making strides, while experts believe generative AI will greatly impact clinical decision-making and healthcare policy in the future.
A new analysis predicts that the market for healthcare generative AI will grow rapidly, reaching an estimated $22 billion by 2032. This expansion is linked to the rise in businesses developing strategic alliances to incorporate artificial intelligence (AI) technologies into a variety of healthcare fields, such as medication discovery, diagnostics, patient care, and claims administration.
The Generative AI Tracker, a collaborative effort between Pymnts and AI-ID based in Reno, has evaluated the healthcare generative AI landscape. As of 2022, the market is already valued at over $1 billion, with technology companies and investors playing a pivotal role in its development. These stakeholders are joining forces with healthcare providers, payers, and other industry players to train large language models (LLMs) using healthcare-specific data and establish robust performance benchmarks.
Generative AI is poised to revolutionize healthcare by enhancing efficiency, personalization, and effectiveness in care delivery. It has already begun to reshape areas such as diagnostics, treatment planning, and care delivery, prompting healthcare providers to consider its far-reaching implications. Furthermore, it is accelerating medical research and drug development, offering promising prospects for the industry’s future.
Despite its potential, generative AI in healthcare faces challenges. It requires further refinement, particularly in terms of training LLMs on healthcare data and setting rigorous standards. Regulatory frameworks are still evolving, presenting another hurdle to widespread adoption.
The report highlights key market factors and discusses the transformative potential of startups in healthcare, the positive impact of generative AI on medical research, and the need for continued technological advancements. It also addresses emerging regulations and spotlights pioneering startups like Epic, Huma, Tempus, Medwise.ai, Innovacer, Babylon Health, and Google’s MedPaLM 2 LLM trained on medical data.
Dr. Robert C. Garrett, CEO of Hackensack Meridien Health, emphasizes the potential of generative AI to revolutionize care delivery, making it more efficient and personalized.
However, generative AI in healthcare faces challenges such as a lack of resources, expertise, and regulatory clarity, with data access and quality, as well as organizational resistance, being additional obstacles, according to a survey by Bain & Company.
Generative AI, which encompasses machine learning models trained on extensive text, audio, or image data to create new content, holds immense promise for data-rich healthcare settings. Dr. Shiv Rao, CEO of Abridge, foresees its adoption in assisting, augmenting, and automating healthcare processes, potentially improving clinical decision-making and healthcare policies.
Looking ahead, generative AI is expected to contribute significantly to clinical decision-making, healthcare workforce utilization, and population health optimization, providing real-time insights into patient care and treatment efficacy.
Dr. Marc Succi, from Mass General Brigham, notes that large language models like ChatGPT have the potential to augment medical practice, offering impressive accuracy and support for clinical decision-making, akin to a recently graduated medical professional.