As of 2024, the global market for AI in healthcare sits at around $23 billion.
However, analysts predict the industry will show extraordinary growth over the next decade as advancements and new technology applications lead to a transformative time in healthcare.
Polaris Market Research predicts the industry will be worth around $431 billion by 2032. Worldwide, governments and industries invest time and resources into figuring out how we use AI within medicine and care.
We explore what this new future of artificial intelligence in healthcare may look like — from data-driven insights in drug discovery to patient diagnosis and care being (at least partly) handled by AI.
From Bill Gates endorsing AI in healthcare to the emergence of multimodal AI technology, the future of healthcare holds possibilities. It represents a journey towards efficient and personalized healthcare.
Remember, this journey is not without hurdles, and we must also acknowledge issues such as minimizing algorithmic bias for the welfare of patients.
Improving Healthcare Decision-Making With Multimodal AI
Medical professionals must access vast information before making any diagnosis or treatment plans. However, the Achilles’ of traditional AI systems is that it typically focuses on one data type.
Multimodal AI addresses this problem by integrating different data types like images, text, and numerical information.
By combining modalities into one system, multimodal AI can improve precision while enhancing predictive capabilities and opening up unprecedented collaboration among healthcare professionals.
Embracing this approach opens up opportunities for precision health services, transforms digital trials, enables remote patient monitoring, and strengthens efforts in times of crisis, such as a rapidly unfolding pandemic.
However, as the healthcare industry embarks on this transformative journey, it must navigate challenges such as data integration processes, concerns about data privacy protection measures, the intricacies of understanding how models work, and the need for expertise in specific domains.
Despite these obstacles, leveraging multimodal AI integration offers rewards by enhancing patient care, advancing research endeavors, and empowering better predictive capabilities.
By leveraging insights from data sources, this approach presents opportunities to transform care, amplify medical research, and improve healthcare effectiveness.
Multimodal AI is not an advancement; it represents a significant shift that can potentially positively redefine the future of healthcare.
Generative AI in Healthcare
Healthcare providers are also beginning to explore a variety of use cases for generative AI in healthcare, such as personalized medicine techniques and follow-up care for their patients to boost their chances of success.
These two areas seem like a match made in heaven, with healthcare generating around 30% of data worldwide and generative AI’s strength in summarizing large volumes of text and visual data.
A great example is how Pfizer collaborated with AWS on a generative AI solution called Vox, which helped accelerate research while reallocating time and resources to speed up how they help and treat patients. Ultimately, it can summarize any volume of data in seconds and free up medical professionals to focus on their patients’ more complex needs.
Amazon Pharmacy also uses generative AI to provide a fast turnaround on prescriptions and offer transparent medication pricing.
However, generative AI algorithms trained on healthcare data that don’t know the community they serve could unwittingly create bias and discrimination. The World Health Organization is already responding to these challenges with recommendations for developing and deploying large language models (LLMs) for public health and health care.
Revolutionizing Diagnosis and Saving Lives
Imagine a scenario where your fate as a patient hinges on the swift analysis of a Computed Tomography (CT) scan. Typically, the results of such a scan can take one to two weeks to materialize. This kind of timeframe could be the difference between life and death.
In a remarkable incident at the Galilee Medical Center, AI detected the ominous signs of intracranial bleeding, a condition demanding immediate intervention. Thanks to AI’s speedy diagnosis, two precious weeks were shaved off the process, and the system was credited with saving the patient’s life. This example dispels the myth that AI’s role in healthcare is not a future pipe dream but a lifesaver today.
The formula of Data + Algorithms = Prevention suggests that big tech sees AI as a kind of proactive health guardian that identifies potential health issues before they occur. It’s something much bigger than AI. It’s a shift in healthcare from a traditionally reactive approach to a more preventative and holistic approach.
Bill Gates on AI Advancements in Healthcare
In his recent End of Year report, Bill Gates focused on AI in healthcare. Gates underscores how AI is set to accelerate the pace of discoveries, particularly in drug development, by harnessing the capabilities to analyze vast data sets effectively.
Beyond pharmaceutical discoveries, Gates’ report emphasizes using AI to address pressing health issues like AIDS, tuberculosis, and malaria. It highlights promising applications, including AI-driven solutions to combat antibiotic resistance, support high-risk pregnancies, assess HIV risk, and streamline medical record documentation.
One standout example is the work of Nana Kofi Quakyi in Ghana, who is developing an AI model to combat antibiotic resistance, a global health concern.
Additionally, the Indian non-profit ARMMAN’s initiative focuses on creating a language model to support healthcare professionals in treating high-risk pregnancies. Furthermore, Sophie Pasco’s efforts in South Africa highlight an app called “Your Choice,” utilizing AI-driven chatbots to assess HIV risk.
Healthcare is a unique and intricate field that blends the precision of science, the compassion of humanity, and the complexities of care delivery into a singular, dynamic discipline. This combination presents immense rewards and formidable challenges, demanding a delicate balance of analytical rigor, empathetic understanding, and economic viability.
It’s a sector that calls for continual improvement and adaptation, a challenge that attracts many talented individuals committed to refining this delicate balance. For AI to succeed in healthcare, it needs to be seen as much more than a technological leap but a tool for redefining and elevating the healthcare landscape to new heights of efficiency and empathy.