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The advent of artificial intelligence (AI) ushered in a new era in healthcare – one marked by a degree of precision and efficiency that we had never deemed possible.
Equipped with the ability to analyse vast amounts of data in a matter of seconds, AI is reshaping diagnostics, treatment planning, and patient care with unprecedented accuracy and speed.
From predicting illnesses before symptoms arise to enabling life-saving surgeries guided by robotic precision, AI is turning science fiction into reality.
This transformative force is not just enhancing healthcare – it’s redefining it, offering the promise of a future where medicine is more personalised, proactive, and powerful than ever before.
When it comes to emerging trends and the future of AI in healthcare, it’s essential to view developments through both short-term and long-term perspectives.
In the near term, AI is poised to address pressing challenges like data overload and streamlining operational efficiencies. However, the long-term vision for AI in healthcare goes beyond immediate improvements, with the potential to revolutionise personalised medicine and predictive care, and even reshape how we approach disease prevention and treatment.
Leveraging the healthcare data surge
The immediate challenge in my opinion is centered around the current data surge. Over the past decade, the amount of health data available has surged dramatically. Surgery produces a large volume of data with each procedure, with video recordings contributing the largest data sets. This video data is highly valuable for surgical research, assessing clinical outcomes, ensuring quality control, and enhancing education.
What’s alarming is that 80 per cent of this data is unstructured and around 97 per cent goes unused – we are sitting on critical insights that could potentially elevate the quality of surgical performance. The operating room (OR) is filled with various medical technologies generating data, but these are often isolated point solutions from different companies. While it’s possible to extract this data if the device manufacturer allows it, and the hospital has the right software, most of the time the data remains locked within the individual devices.
Hospitals lack a unified platform where they can access all the tools and data healthcare providers use or need. Moreover, there is currently no technology that effectively integrates these disparate data sets to deliver valuable insights that could benefit both healthcare providers and patients.
At J&J MedTech, for instance, we have recognised this challenge and have developed a digital ecosystem designed to enhance surgical connectivity, encompassing software applications that are independent of data sources and aimed at providing critical insights precisely when and where they are needed. As we move forward, really is, the question we are constantly asking ourselves is how do we continue to bring forward and expand on the value of surgical data?
Scalable AI healthcare models
Scaling AI in healthcare is particularly challenging due to complex applications, strict regulations, and critical life-and-death decisions. While many organisations are ready to invest, healthcare often lacks the skilled staff, technical expertise, and resources needed, with readiness far below the 54 per cent average across other industries combined.
With AI forecasted to continue its upward swing well beyond 2024, it is essential to tackle several technical obstacles to the adoption of clinical AI.
There has never been a greater onus on providing intelligent insights to customers through digital solutions. For instance, while our current infrastructure supports rapid cloud-based deployment of AI models, it lacks the same scalability for monitoring and maintenance. To address this, we’re developing a system to track key metrics and detect issues before they affect customers or billing. Additionally, we’re scaling AI algorithms in operating rooms to offer surgeons real-time analysis, and we’re fostering a community for surgeon collaboration and ongoing education. Despite, and possibly because of these challenges, some of the most innovative and successful methods for deploying AI into production today are emerging from the healthcare sector.
AI-powered robotics in surgery
Surgical robots represent a significant leap forward, blending advanced technologies like AI with sophisticated mechanics. Robotic arms enable surgeons to perform operations through smaller incisions than traditional open or laparoscopic surgeries.
These robots enhance surgical skills by offering greater control, flexibility, and access to complex anatomical areas. Equipped with specialised instruments and controlled via a console, they translate the surgeon’s hand movements into precise, smooth actions, reducing tremors and improving accuracy.
These systems tackle ongoing challenges in surgery and hold even greater potential for future advancements.
The future of modern clinical trials
AI is increasingly seen as a key to sustainable and efficient drug development, with its applications in clinical trials (CTs) being actively explored.
While having access to data is crucial for personalised medicine, turning this data into actionable insights requires advanced AI models, which must be developed and trained with the right datasets to speed up and improve drug research.
Moreover, adaptive clinical trials offer the flexibility to reduce the number of participants or end a trial early if the results are promising. This not only speeds up patient access to treatments but also accelerates market entry. A recent example that comes to mind is the InspIRE study, where strong interim results led to an early termination for success. This was significant as it provided an early glimpse of our new pulse field ablation technology, expected to be a breakthrough in treating atrial fibrillation.
To fully harness the power of AI in healthcare, the industry must develop an ecosystem that enables speed, scalability, and widespread adoption. Currently, the most significant challenge lies in the complex and time-consuming process of integrating new software or digitally enabled medical technologies, as each addition requires rigorous approval from IT and security teams.
Until these barriers are addressed, the transformative potential of AI will remain out of reach for many healthcare providers.
Creating a streamlined, secure, and adaptable framework is essential to unlock AI’s full potential and drive meaningful advancements in patient care.
The writer is the general manager and lead – Surgery Franchise – MEA at Johnson & Johnson MedTech.
