Invisible Autonomous Intelligence in the field of MedTech

What is Invisible Autonomous Intelligence in MedTech?

This new engine of innovation is the result of three emergent technologies coming together:

1. Invisible Artificial Intelligence, refers to the autonomous use of artificial intelligence (AI) and machine learning (ML) technologies, with no direct human intervention required. In medicine, these AI-based systems work seamlessly in the background, making decisions and carrying out tasks without drawing much attention to themselves from clinicians or surgeons. Gen AI will enable these systems to evolve over time and adapt to new environments while ensuring the safe delivery of diagnosis and treatment.

2) The Power of Data, which lies at the heart of this transformation. Medical devices, electronic health records, wearable sensors, clinical trials, and countless other sources generate vast amounts of information. This data is now fuelling invisible autonomous intelligence, which is able to dissect and interpret at a speed and scale never before possible.

3) Medical Technology as a whole, encompassing robotics, sensor technology, software algorithms, devices, and other solutions that are leveraged to design medical devices in areas like SAMD, wearables and imaging technologies to extract meaningful data and improve results.

When we implement autonomous AI and ML applications – fuelled by medical data – into medical technologies, the result is Invisible Autonomous Intelligence. Here’s how it’s changing the world of MedTech.

Invisible Autonomous Intelligence in use

A rising STAR

One application of Invisible Autonomous Intelligence has arisen in surgery technology, specifically suturing and knot-tying. The smart tissue autonomous robot (STAR) from the Johns Hopkins University has demonstrated that it can outperform human surgeons in some surgical procedures such as bowel anastomosis in animal studies. This was possible due to the high level of repetition and precision required for such surgical procedures. STAR can adjust its surgical plan in real time, helping it to adapt to changing conditions during surgery. And as a self-learning AL, STAR’s abilities are likely to improve.

Enhanced enhancement

We’re all familiar with the scene from crime dramas: a detective spots a tiny reflection on a car mirror, or someone’s sunglasses. The technician enhances… enhances… and we watch as 4 pixels magically turn into a clear image. Impossible, right? But with Gen AI, that’s now coming fairly close to reality.

In medical imaging, generative AI can use probability and inference to enhance image quality even where information is missing. It can denoise scans, and even generate images of anatomical structures from different angles. This can aid in diagnosis, treatment planning, surgery and education.

Personalized assistants

AI-powered chatbots and virtual assistants are providing patients with personalized health information and treatment plans, answer questions, and offer guidance on managing their conditions, thus improving patient engagement and adherence to treatment plans.

Enhanced analysis

Leveraging Generative AI we can analyze patient data, including medical records, genetic information, and treatment outcomes, to generate personalized treatment plans with the ability for these plans to adapt over time based on new data inputs, thus optimizing patient care.

Drug discovery

Extending to drug discovery and development, there are now AI algorithms which can analyze vast amounts of genomic, proteomic, and chemical data to predict potential drug candidates. This could accelerate the drug discovery process and lead to more effective treatments for various diseases.

Design of new molecules

In the field of drug discovery, generative AI is transforming the way new molecules are designed. By understanding the intricate relationships between molecular structures and their effects on the human body, AI models can generate novel drug candidates that hold promise for treating diseases more effectively. This significantly accelerates the drug development process.

Virtual patients

Generative AI can even be used to create patient-specific models for simulations, treatment planning, and predicting disease progression. This means doctors can, in a sense, test an intervention in a virtual world first, before applying it to a living patient. Patients, too, gain access to an incredible tool, which demonstrates how a condition might progress, what they might expect from a treatment, and how their own behavior will likely impact their health.

Personalized medicine

The era of generalized treatments is gradually making way for precision medicine. Invisible autonomous intelligence fuels this shift, allowing medical professionals to tailor therapies to an individual’s unique genetic makeup, physiological responses, and lifestyle. AI algorithms today can assist is in designing custom prosthetics or implants tailored to an individual’s anatomy, minimizing interventions.

Challenges going forward

The word “disruption” is overused, but it is accurate. Existing norms and structures will be forced to change as the impact of Invisible Autonomous Intelligence reverberates across disciplines. This will include the need for large and diverse datasets, ethical considerations, and ensuring that the generated content is accurate and safe for clinical use. Collaborations between AI researchers, medical professionals, and regulatory bodies are essential to harness the benefits of such solutions while maintaining patient safety and regulatory compliance.

Some questions we’ll face for sure include:

• How much oversight will automated systems require?
• How can transparency be maximized, and who will have access to what data, when?
• As automated surgeries take on more and more decision-making, who will be liable in cases of harm?
• In an area growing faster than regulation, what guidelines should MedTech companies follow?

The life sciences industry is being rocked by so many waves, it’s hard to identify the tsunamis. Gen AI has the potential to change everything – most notably in the form of Invisible Autonomous Intelligence. The possibilities for patient care, medical analysis and research are hard to imagine.

The future is invisible.