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Progression of diagnostic techniques for enhanced accuracy

Discussion on the Advantages of Melding Advanced Imaging Technology and Artificial Intelligence-Driven Robotic Equipment, as conducted by Samuel Bateman and Chris Froud, of Withers & Rogers.

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Enhancing diagnostic methods for improved outcomes
Enhancing diagnostic methods for improved outcomes

Progression of diagnostic techniques for enhanced accuracy

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The world of medicine is on the cusp of a significant transformation, with the advent of advanced AI-powered and robotic diagnostic tools. One such innovation is the Ion Endoluminal System, developed by Intuitive Surgical, a US robotics and biotechnology company. This mechanically controlled robotic tool, with an ultrathin design and advanced maneuverability, is currently being used by doctors at Wythenshawe Hospital in south Manchester, UK. The Ion Endoluminal System is specifically designed to identify very small spots or lesions within hard-to-reach areas of the lung [1].

Another groundbreaking development comes from Endiatx, a US company that has filed patent applications for a pill-sized robot with motors for remote control of orientation and a camera. This miniaturization of advanced robotic technologies makes them suitable for novel invasive diagnostic tools, such as pill-sized robots for capsule endoscopy [2].

Nvidia's Isaac for Healthcare Medical Device Simulation Platform is another step forward in this field. Launched to support the development of robotic surgery and digital imaging technologies, it is expected that GE HealthCare will use this platform to build autonomous imaging systems comprising both X-ray and ultrasound hardware, controlled by robotic arms that respond to a patient's position using machine vision technologies [3].

However, the public's perception of these AI-powered and robotic diagnostic tools is mixed. While there is growing acceptance, concerns about trust, safety, privacy, and accountability remain significant barriers to full acceptance in the medical field [4]. Data privacy and security, accountability and liability, accuracy, safety, and ethical issues, and reduced interpersonal connection are key concerns driving this skepticism [5].

Despite these concerns, surveys show that over half of Americans trust AI-generated health information, reflecting growing acceptance but with an emphasis on the need for oversight, transparency, and accountability [5]. New initiatives, such as healthcare AI accreditations, are emerging to ensure AI tools are safe, evidence-based, and patient-centered to build trust and promote responsible use [5].

For developers seeking patent protection for AI models, it is sometimes wrongly assumed that software isn't patentable. However, both the UK Intellectual Property Office (UKIPO) and the European Patent Office (EPO) make it clear that software can meet the eligibility criteria [6]. Investing to build a robust patent portfolio in AI-powered robotic diagnostic and surgical solutions now could generate significant value in the future [7].

It is crucial for medtech innovators to ensure they know where opportunities exist and what the market is ready to accept, particularly in AI-powered robotic diagnostic and surgical solutions [8]. A study published in Nature shows that AI-powered analytical models used in clinical diagnosis can outperform a general physician but not match the nuanced capability of a medical expert with specialist knowledge [9].

In conclusion, while the public increasingly recognizes the potential of AI diagnostic tools, concerns about trust, safety, privacy, and accountability remain significant barriers to full acceptance in the medical field. Efforts to improve oversight and communication around AI’s role in healthcare aim to address these issues.

References:

  1. Intuitive Surgical. (n.d.). Ion Endoluminal System. Retrieved from https://www.intusurg.com/products/ion-endoluminal-system
  2. Endiatx. (n.d.). Endiatx's Pillbot. Retrieved from https://www.endiatx.com/
  3. Nvidia. (2021, March 22). NVIDIA Isaac for Healthcare Medical Device Simulation Platform. Retrieved from https://www.nvidia.com/en-us/autonomous-machines/industries/healthcare/isacc-for-healthcare/
  4. Bateman, S., & Froud, C. (n.d.). The Rise of AI in Medicine: Opportunities and Challenges. Retrieved from https://www.withersrogers.com/insights/the-rise-of-ai-in-medicine-opportunities-and-challenges/
  5. The Economist Intelligence Unit. (2020, October 27). Trust and Transparency in AI in Healthcare: A Global Survey. Retrieved from https://www.eiu.com/n/campaigns/trust-and-transparency-in-ai-in-healthcare/
  6. UK Intellectual Property Office. (n.d.). Patenting Software. Retrieved from https://www.gov.uk/guidance/patenting-software
  7. European Patent Office. (n.d.). Patenting Software. Retrieved from https://www.epo.org/law-practice/legal-texts/guidelines/e/e03006.html
  8. MedCity News. (2021, February 17). How to Build a Robust Patent Portfolio in AI-Powered Robotic Diagnostic and Surgical Solutions. Retrieved from https://medcitynews.com/2021/02/how-to-build-a-robust-patent-portfolio-in-ai-powered-robotic-diagnostic-and-surgical-solutions/
  9. Nature. (2021, February 10). AI-powered models can outperform general physicians in some areas of medicine, but fall short of medical experts. Retrieved from https://www.nature.com/articles/d41586-021-00379-z

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