Thirona’s AI Tech is Creating Individualised Patient Care
Eva van Rikxoort is the CEO & Founder at Thirona, which specialises in advanced analysis of thoracic CT images with AI. Eva began her career as a scientist, but transitioned as she wanted to create a business model to make AI centred around how to help a patient. Eva believes that AI is a necessity in healthcare. In pulmonology, Thirona’s AI-based technology supports image enhancement and precision in segmenting the lung. These AI benefits directly impact the patient, as doctors can target lung segments in which the tumour resides, saving more healthy lung tissue of patients.
Eva takes a deep dive with us into AI medtech, to explore how AI is helping to bring more individualised treatment to patients.
How the implementation of AI can help to counter human error
The use of AI is less about correcting human errors and more about the ability to quantify volumes of data that would be almost impossible using human processing alone. While precision and accuracy in conducting measurements is crucial, AI can make it possible to deduce a logical assessment when sourcing from a database of thousands or millions of images.
“Prior to AI, imaging evaluation processes done by a visual assessment by the clinician could only be manually quantified requiring a certain level of guesswork,” shares Eva. “What AI in imaging does best is aiding doctors in their work, showing deeper insights captured on scans, but may not even be visible to the human eye. With the support of AI, doctors can do their job better and more efficiently, and access to this knowledge can be equalised globally in the long-term.”
The involvement of humans will always be essential in healthcare and Eva believes the role of AI will continue to play a large role in maximising their clinical impact moving forward. For example, in pulmonology, while AI algorithms can assess the diseased areas more precisely and spot patterns, there is still a clinician to validate and interpret the findings in the personal context of the patient.
“Across the healthcare field, AI can bring more confidence to decision making and enhance the expertise of clinicians,” she says.
AI is playing a critical role in advancing precision medicine in many ways, allowing for individualised care thanks to insights that show the full context of a patient’s specific condition. This more comprehensive insight enables physicians to choose the most optimal approach to treatment which could be pharmaceutical or interventional, including surgery.
“For interventional approaches, AI is already being used in a pre-operative planning phase to determine a patient's eligibility for a specific treatment, for example, in case of considering placement of endobronchial valves for lung volume reduction in COPD patients with severe emphysema. Also, for lung cancer biopsy or other bronchoscopic interventions, AI can play a guiding role in plotting out a path quickly, which the clinician can validate or modify,” Eva continues.
Looking towards the future, Eva anticipates that AI will also aid in real-time decision-making during procedures, providing clinicians with actionable insights and intra-operative guidance as they navigate through the human body to reach and treat more localised areas.
“Take the lungs for instance. By integrating AI-powered imaging systems into the procedural workflow, especially in case of bronchoscopic interventions, clinicians could receive continuous feedback and assistance helping guide the navigation, significantly enhancing the precision, accuracy and efficiency of the procedure,” she says. “In addition, for surgical lung interventions, AI can provide a deeper level of segmentation that can support early intervention and procedures, like for example, lung segmentectomy helping save more healthy lung tissue and lung function capacity.”
To summarise, AI offers excellent capabilities to help minimise the invasiveness of localised therapies while maximising the outcomes, ultimately helping minimise the harm to patients in the long run.
The potential impact of AI on radiologist labour shortages
The manpower shortage in radiology today continues to intensify, as the mismatch between supply and demand for radiologists widens. With the growing adoption of CT for diagnosis and disease assessment, there will be simply not enough manpower to process all images, Eva says.
“At the same time, with the rise of minimally invasive high precision techniques, the cognitive load required to precisely assess the images prior to and during intervention to maximise outcomes is becoming an even bigger challenge,” she says. “In fact, AI no longer becomes a nice-to-have, but instead a ‘must have’ to provide the best possible patient care.”
Especially with the rise in the number of lung screenings today, radiologists soon may not be able to handle this increased workload, creating more urgency for AI in the field. In many cases, AI can take on simple, routine tasks to speed up the diagnosis processes.
“As AI takes over the monotonous work, clinical teams can focus on providing patients with the best possible treatment by devoting human effort to the places it’s needed the most,” Eva shares.
When it comes to addressing gaps in clinical practice long-term, AI definitely has the potential to equalise access to high-quality healthcare by providing clinicians everywhere in the world with advanced tools and capabilities.
“While AI undergoes a staggered roll out across the globe, it’s clear the long-term implementation of AI will benefit clinicians everywhere,” Eva summarises. “For example, in Africa, where there is one radiologist for every million or so individuals, the healthcare system is quickly embracing AI, for example in screening for tuberculosis out of need and it is working and having positive outputs.”
This democratisation of AI in healthcare will ensure that all patients have access to the best possible diagnostic and treatment options, regardless of geographical or socioeconomic barriers.
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