Despite recent hype in generative AI, Advanced AI is highly mature and established in the medical world, especially radiology. I recall the first use cases in computer-aided detection of cancer nodules in the early 2000s when I started my professional career in diagnostic imaging. Further scaling of deep learning enables us to implement AI earlier and later in the patient pathway, from early detection of stroke or heart failure, pre-natal organ screening, automation of imaging, and immediate detection of critical findings.
This article highlights how AI impacts the workforce and patient care, the surrounding challenges, and the future of AI in MedTech and healthcare.
AI in brief
Artificial intelligence (AI) seeks to create intelligent systems through algorithms and machines that assess learned contexts, communicate, acquire knowledge, and make informed decisions or diagnoses. Machine learning (ML) is central to AI, which encompasses methods enabling machines to autonomously learn and predict by identifying patterns within data. A specific approach within ML is deep learning, which utilizes complex models known as neural networks. When exposed to extensive datasets, these neural networks can identify statistical patterns, which can be applied to various tasks.
Task 1: Strengthening workforce impact
AI is revolutionizing the healthcare landscape by significantly supporting and enhancing the roles of medical staff. One of the primary benefits is automation, which streamlines routine tasks, allowing healthcare professionals to focus more on patient care. By handling administrative duties and data management, AI reduces staff fatigue and frees up valuable time, improving job satisfaction and better patient outcomes.
Moreover, AI-driven tools provide informed decision-making support that often mirrors the insights of experienced medical professionals by:
- Nearly instantly analyzing vast datasets and patterns
- Assisting in diagnosing conditions
- Suggesting treatment options.
This collaboration bolsters healthcare providers’ assessments with empirical data, enhances the accuracy of diagnoses, and mitigates the risk of human error, of which we know post-COVID-19 staff fatigue is still prevalent.
The demand for care is multiplying, and by 2040, 1 in 4 workers will have to be healthcare workers to meet demand. WHO put out a statement that “without immediate action, health and care workforce gaps in the EU region could spell disaster” (2022).
This challenge is exacerbated by critical workforce dynamics:
- 40% of medical doctors are nearing retirement age, posing a significant risk to healthcare capacity.
- Patient accessibility to care is already strained and is expected to worsen if these workforce gaps remain unaddressed.
Younger generations should be incentivized to enter the healthcare field, and better working conditions should be provided to healthcare staff to mitigate burnout and dropout. Nevertheless, more than ever, AI will be crucial to support hospitals facing staffing shortages. With its ability to manage workflows and optimize resource allocation, AI ensures that patient care remains consistent and efficient, even during peak times.
Task 2: Improving patient impact
AI is also transforming the patient experience by facilitating faster access to care and improving overall satisfaction; for example, minimizing call-backs due to missed or misdiagnoses or potentially reducing prolonged length of stay thanks to overall complication prevention ensures that patients receive timely and accurate care. The hope is that AI will reduce unnecessary admin and allow more patient time, furthering a more positive patient healthcare journey.
Task 3: Increasing hospital capacity
AI MedTech software and devices can potentially increase hospital capacity by streamlining the patient pathway. For example, Cerebriu’s AI MRI software has the potential to reduce time to treatment, which, combined with improved scheduling, can allow patients to receive care sooner and better plan their hospital visits.
Capital MedTech technology is also making inroads: new electrophysiology technologies, such as Boston Scientific’s FARAPULSE Pulsed Field Ablation (PFA), provide enhanced visualization capabilities to boost confidence in therapy delivery and streamline mapped workflows throughout pulse field ablation procedures, providing safer, more efficient atrial fibrillation exams.
On-going challenges in AI
While AI provides many benefits, challenges exist:
- Systems based on deep learning can exhibit biases and often struggle to apply their “learning” to unfamiliar situations.
- There are ongoing concerns regarding data privacy, bandwidth, and cybersecurity, so many AI Committees are sprouting across healthcare systems.
- For early startups, these AI committees and additional stakeholders from IT and cybersecurity may delay sales cycles and be perceived as a lot of “red tape”.
The point around IT/cybersecurity concerns and the difficulty of adopting new software companies within hospital systems, explains why proven marketplace platforms have started to dominate and why healthcare systems/hospitals will rely on them.
To accelerate go-to-market adoption, AI software will often need to be adopted on the more prominent marketplace platforms (e.g., Aidoc, Blackford Analysis, Microsoft Nuance, deepc, Incepto, etc.) and compete with other software. The space for post-processing and reporting tools is already getting crowded.
Overarching integration into electronic medical systems (EMR) and being able to plot the treatment plan of the care pathway will be important. We still see a lot of fragmentation of integration across care pathways, even within unique large corporations, let alone at the hospital system level. Nevertheless, we see this as an important area slowly ramping up.
Transforming healthcare with AI: shaping the future of medicine
AI in MedTech provides hope for better access, elevated healthcare standardization, and the possibility of providing expertise and knowledge across geographical areas with fewer healthcare experts. AI in pharma and diagnostics can identify potential drug candidates, optimize molecular structures, and predict drug efficacy. Here are the top 3 use cases already making a mark.
Use case No. 1: Medical imaging and diagnostics
As mentioned earlier, AI deep learning models have shown great success in analyzing medical images, helping to detect diseases early, and enhancing diagnostic speed and accuracy.
Use case No. 2: Predictive analytics and personalized medicine
AI can analyze vast amounts of data to predict health outcomes and recommend personalized treatment plans. For example, real-time analysis of patient data on the bedside and specialized cardiac monitors can predict before a patient “crashes” and support doctors when to provide more fluids or optimize drugs in the operating theatre (OR) and intensive care unit (ICU). Another day-to-day lifestyle application can have AI predict which patients are at higher risk for diabetes or cardiovascular disease based on lifestyle, environmental factors and physiological or genetic factors.
Use case No. 3: Drug discovery and development
AI may accelerate drug discovery by analyzing vast datasets to identify potential drug candidates and using modeling to predict patient outcomes. It enables faster delivery of new treatments, such as for cancer and rare diseases, reducing the time and cost of traditional drug development.
Wrapping up
In summary, while integrating AI in MedTech will require overcoming significant challenges and adapting existing systems (even reimbursement coding or pathways), the transformative potential is undeniable. AI-driven advancements in MedTech, personalized care, and drug discovery hold the promise of faster, more accurate treatments and a more patient-centered healthcare experience. The future of medicine is here, and AI, in synergy with healthcare professionals, is the key to unlocking its full potential for better, more accessible care worldwide.
To stay ahead, leveraging market intelligence in healthcare can uncover new opportunities, drive growth, and prepare your organization for the future. Ready to explore this transformative journey?
About the author
Aleksandra Lada-Gola is a seasoned MedTech Strategist and Commercial Leader with 17 years of expertise spanning sales, marketing, business development, and strategic planning. Her experience encompasses capital equipment, services, and software sales, cultivated through impactful roles at GE Healthcare, Edwards Lifesciences, and AI software scale-ups across the US and EMEA markets. Renowned for her collaborative leadership, entrepreneurial mindset, and sharp strategic acumen, Aleksandra has been instrumental in driving the adoption and growth of AI-powered solutions in the MedTech sector.
