In the intricate world of medical device manufacturing, where precision is paramount and innovation saves lives, a strong production process is key. From crafting tiny bone screws to fabricating complex hip replacement parts, there is high demand for accuracy, reliability and efficiency.

The medical device industry encompasses a wide array of products designed to diagnose, monitor and treat medical conditions. From diagnostic equipment to implantable devices, the industry caters to the evolving needs of healthcare professionals and patients worldwide.

Today, there are an estimated two million different kinds of medical devices on the global market, categorised into more than 7,000 device groups, according to the World Health Organisation (WHO).

Challenges In Medical Device Manufacturing

Manufacturing components for medical devices presents unique challenges that demand advanced machining solutions. End-users rely on these devices being manufactured to the highest quality, as any products that do not meet quality standards can cause huge disruption. Entire operations could be paused, products recalled, organisational reputation damaged and patients’ lives at risk.

Additionally, regulatory requirements are stringent in this field. There are international standards around quality management — such as ISO 13485, risk management and ISO 14971 — as well as regional regulations such as 21 CFR and FDA for the US and EU MDR for the European Union, which all devices must meet.

The requirement for such high accuracy means manufacturers cannot risk even a millimetre of differentiation between components, so having a robust machining setup is paramount.

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The post Precision In Medical Practice — Machining Medical Device Components appeared first on Asia Pacific Metalworking Equipment News | Manufacturing | Automation | Quality Control.

Source: Medical Xpress

While the world ravels at the wonders of Artificial intelligence (AI) being the cornerstone of prognosis breakthroughts, a multi-institutional team of researchers at the UNC School of Medicine, Duke University, Ally Bank, Oxford University, Colombia University, and University of Miami have been on a mission to build public trust and evaluate how exactly AI and algorithmic technologies are being approved for use in patient care.

Together, Sammy Chouffani El Fassi, a MD candidate at the UNC School of Medicine and research scholar at Duke Heart Center, and Gail E. Henderson, Ph.D., professor at the UNC Department of Social Medicine, led a thorough analysis of clinical validation data for 500+ medical AI devices, revealing that approximately half of the tools authorized by the U.S. Food and Drug Administration (FDA) lacked reported clinical validation data.

AI has practically limitless applications in health care, ranging from auto-drafting patient messages in MyChart to optimizing organ transplantation and improving tumour removal accuracy. Despite their potential benefit to doctors and patients alike, these tools have been met with skepticism because of patient privacy concerns, the possibility of bias, and device accuracy.

“Although AI device manufacturers boast of the credibility of their technology with FDA authorization, clearance does not mean that the devices have been properly evaluated for clinical effectiveness using real patient data,” said Chouffani El Fassi, who was first author on the paper.

“With these findings, we hope to encourage the FDA and industry to boost the credibility of device authorization by conducting clinical validation studies on these technologies and making the results of such studies publicly available.”

Since 2016, the average number of medical AI device authorisations by the FDA per year has increased from two to 69, indicating tremendous growth in commercialisation of AI medical technologies. The majority of approved AI medical technologies are being used to assist physicians with diagnosing abnormalities in radiological imagining, pathologic slide analysis, dosing medicine, and predicting disease progression.

AI is able to learn and perform such human-like functions by using combinations of algorithms. The technology is then given a plethora of data and sets of rules to follow, so that it can “learn” how to detect patterns and relationships with ease.

From there, the device manufacturers need to ensure that the technology does not simply memorise the data previously used to train the AI, and that it can accurately produce results using never-before-seen solutions.

Regulation During a Rapid Proliferation Of AI Medical Devices

Following the rapid proliferation of these devices and applications to the FDA, Chouffani El Fassi and Henderson et al. were curious about how clinically effective and safe the authorised devices are. Their team analysed all submissions available on the FDA’s official database, titled “Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices.”

“A lot of the devices that came out after 2016 were created new, or maybe they were similar to a product that already was on the market,” said Henderson. “Using these hundreds of devices in this database, we wanted to determine what it really means for an AI medical device to be FDA-authorised.”

Of the 521 device authorisations, 144 were labeled as “retrospectively validated,” 148 were “prospectively validated,” and 22 were validated using randomized controlled trials. Most notably, 226 of 521 FDA-approved medical devices, or approximately 43%, lacked published clinical validation data.

A few of the devices used “phantom images” or computer-generated images that were not from a real patient, which did not technically meet the requirements for clinical validation.

Furthermore, the researchers found that the latest draft guidance, published by the FDA in September 2023, does not clearly distinguish between different types of clinical validation studies in its recommendations to manufacturers.

Types Of Clinical Validation And A New Standard

In the realm of clinical validation, there are three different methods by which researchers and device manufacturers validate the accuracy of their technologies: retrospective validation, prospective validation, and a subset of prospective validation called randomized controlled trials.

Retrospective validation involves feeding the AI model image data from the past, such as patient chest X-rays prior to the COVID-19 pandemic. Prospective validation, however, typically produces stronger scientific evidence because the AI device is being validated based on real-time data from patients.

This is more realistic, according to the researchers, because it allows the AI to account for data variables that were not in existence when it was being trained, such as patient chest X-rays that were impacted by viruses during the COVID pandemic. Randomised controlled trials are considered the gold standard for clinical validation. This type of prospective study utilises random assignment controls for confounding variables that would differentiate the experimental and control groups, thus isolating the therapeutic effect of the device.

For example, researchers could evaluate device performance by randomly assigning patients to have their CT scans read by a radiologist (control group) versus AI (experimental group).

Because retrospective studies, prospective studies, and randomised controlled trials produce various levels of scientific evidence, the researchers involved in the study recommend that the FDA and device manufactures should clearly distinguish between different types of clinical validation studies in its recommendations to manufacturers.

In their Nature Medicine publication, Chouffani El Fassi, Henderson and others lay out definitions for the clinical validation methods which can be used as a standard in the field of medical AI.

“We shared our findings with directors at the FDA who oversee medical device regulation, and we expect our work will inform their regulatory decision making,” said Chouffani El Fassi.

“We also hope that our publication will inspire researchers and universities globally to conduct clinical validation studies on medical AI to improve the safety and effectiveness of these technologies. We’re looking forward to the positive impact this project will have on patient care at a large scale.”

Algorithms Can Save Lives

Chouffani El Fassi is currently working with UNC cardiothoracic surgeons Aurelie Merlo and Benjamin Haithcock as well as the executive leadership team at UNC Health to implement an algorithm in their electronic health record system that automates the organ donor evaluation and referral process.

In contrast to the field’s rapid production of AI devices, medicine is lacking basic algorithms, such as computer software that diagnoses patients using simple lab values in electronic health records. Chouffani El Fassi says this is because implementation is often expensive and requires interdisciplinary teams that have expertise in both medicine and computer science.

Despite the challenge, UNC Health is on a mission to improve the organ transplant space.

“Finding a potential organ donor, evaluating their organs, and then having the organ procurement organization come in and coordinate an organ transplant is a lengthy and complicated process,” said Chouffani El Fassi.

“If this very basic computer algorithm works, we could optimize the organ donation process. A single additional donor means several lives saved. With such a low threshold for success, we look forward to giving more people a second chance at life.”

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The post Research Reveals Almost Half Of FDA-Approved AI Medical Devices Lacked Clinical Validation appeared first on Asia Pacific Metalworking Equipment News | Manufacturing | Automation | Quality Control.

Source: Manufacturing Asia

“The potential applications of 3D printing in medicine are vast, from surgical planning and dental restoration to remote patient monitoring and telemedicine,” according to Technavio, quoted by Manufacturing Asia revealed. Meanwhile, a trend towards the field of bioprinting serves as another growth factor.

Additionally, findings from MarketsandMarkets Research revealed the global 3D printing bioprinting market vlued at US$1.3 billion in 2024, is expected to reach US$2.4 billion in 2029, growing at a compound annual growth rate (CAGR) of 12.7%. This growth is driven by advancements in 3D bioprinting technology, increased public-private partnerships, and its integration into pharmaceutical and cosmetic industries.

Key market drivers include adopting 3D bioprinting for precise tissue and organ fabrication, facilitating drug testing and personalised medicine. Opportunities lie in the rising demand for organ transplants, while biocompatibility issues and stringent sterilisation protocols present challenges.

Unlike traditional 3D printers that use plastics or metals, bioprinters utilise a computer-guided pipette to deposit living cells, known as “bioink,” to produce artificial living tissues. This capability has notable implications for organ replacement, potentially addressing donor shortages and reducing rejection risks by creating organs fit for specific needs.

Moreover, the market is also witnessing advances in other areas, including prosthetics, implants, and surgical equipment. Solutions such as stereolithography (SLA) and digital light processing are widely used in the production of customised medical devices, including orthopaedic implants and wearable medical devices. 

Furthermore, the fields of dentistry and orthodontics benefit from 3D printing, which can be utilised for dentures, bone scaffolds, and hearing aids.

“Complex medical procedures, such as implantable and non-implantable medical devices, drug testing, and organ and tissue production, are also being revolutionised by this technology,” the report added.

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