Early detection needs are one area where Deep Tech can, on the face of it, provide instant support to healthcare professionals. Image Credit: Shutterstock

In today’s rapidly evolving innovation landscape, Deep Tech - which integrates cutting-edge sciences, engineering prowess, and visionary design - has emerged as the catalyst to revolutionize industries.

The healthcare domain is poised for an extraordinary transformation by leveraging AI, biotechnology, robotics, and quantum computing to reimagine patient care and streamline healthcare delivery.

Use cases

Deep Tech is revolutionizing the field of diagnostics by enabling the development of new - and more accurate - tests, enhancing the precision of diagnoses, and improving treatment outcomes. Additionally, Deep Tech is making it possible to develop portable and affordable diagnostic tools that can be used in remote or underserved areas, improving access to healthcare for people worldwide.

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For example, the Fortitude Kit by DxD Hub was designed to diagnose COVID-19. This real-time RT-PCR test swiftly detects the SARS-CoV-2 virus in respiratory samples with high accuracy, offering results in approximately one hour.

Early detection

AI-powered medical imaging transforms visual diagnosis, enabling swift and precise detection of potentially cancerous lesions in radiology images. This technology, particularly impactful in breast cancer detection, analyses mammograms to identify subtle patterns, reducing false positives and allowing for earlier treatment.

The use of AI in the detection of breast cancer has shown improvements in cancer detection rates of 5-13 per cent compared to standard reading by radiologists. AI also improves accuracy in detecting lung, colon, and prostate cancers by analyzing CT and MRI scans. Beyond cancer, AI aids in diagnosing conditions like heart disease and neurological disorders, analyzing diverse imaging modalities for abnormalities.

Generative AI is making a significant impact in the fight against antibiotic-resistant superbugs. AI models can identify novel antibiotic compounds that can combat these dangerous pathogens by screening vast amounts of data. One such example is abaucin, an antibiotic discovered using AI that has demonstrated remarkable efficacy against Acinetobacter baumannii, a critical threat to human health.

In this instance, the AI model analyzed 6,680 previously unseen compounds. The entire process was completed in just 90 minutes, identifying 240 compounds suitable for lab testing. This analysis unveiled nine potential antibiotics, including the noteworthy candidate abaucin.

Deep Tech startups are creating robotic surgical systems with enhanced precision and control, enabling minimally invasive procedures. This results in decreased surgical trauma, quicker recovery, and enhanced patient outcomes. The application of these robotic systems is reshaping surgical fields such as neurosurgery, cardiology, and orthopedics.

The Niobe ES Remote Magnetic Navigation system employs magnetic navigation to guide surgeons in diverse procedures, enhancing precision and efficiency in patient operations. The Niobe ES system significantly decreased procedure duration and minimized X-ray exposure by at least 30 per cent, owing to the system's heightened responsiveness.

Challenges to adoption

Unlike typical tech firms, Deep Tech ventures demand extensive R&D efforts, given the intricate nature of the scientific or engineering challenges they seek to address. Early-stage research and development in Deep Tech demands significant capital investment.

Deep Tech relies on high-quality data to train algorithms and develop predictive models. Data quality and availability in healthcare can be challenging due to siloed data, privacy concerns, and data standardization.

Different companies often develop Deep Tech solutions using different proprietary technologies. This can create challenges in integrating them with existing healthcare systems, resulting in isolated data silos and a lack of communication between different systems.

The absence of well-defined regulatory guidance for Deep Tech in healthcare presents uncertainty for healthcare organizations. Determining the safety and effectiveness of deep tech solutions becomes challenging, and obtaining compensation for deep tech services becomes complex.

The road ahead

Deep Tech in healthcare is still in its infancy, but the potential benefits are already becoming evident. As the healthcare industry faces increasing challenges, Deep Tech will become increasingly important in resolving these challenges and improving patient care.

It can enhance the efficiency of care delivery, reduce the cost of care, and improve patient outcomes. Deep Tech is also creating new opportunities for startups in the healthcare sector. It plays a pivotal role in fostering economic development and holds the potential to deliver impactful solutions to critical societal challenges, rendering it an area of considerable interest and investment.