AI Research

Legal Considerations for Artificial Intelligence in the Life Sciences Sector

Published

4 weeks ago

August 8, 2025

Life sciences, the AI Act and Medical Device Regulation

As discussed in the first part of our series, the EU Artificial Intelligence Act (Regulation 2024/1689) (AI Act) proposes a risk-based framework for AI. The AI Act applies horizontally across all sectors and therefore equally applies to AI systems used in the life sciences sector.

The AI Act generally distinguishes four categories of AI systems and models:

AI systems used as part of prohibited AI practices;
High-risk AI systems;
Limited-risk AI systems which interact directly with individuals or are capable of generating realistic content;
General purpose AI-models.

Under the AI Act, various AI systems used in the life sciences sector will be classified as “high-risk”. These include AI systems that are, or are safety components of, medical devices already subject to the Medical Device Regulation (Regulation 2017/745) (“MDR”) and the In Vitro Diagnostic Medical Device Regulation (Regulation 2017/746) (“IVDMR”). The MDR applies to devices (including stand-alone software) that are intended for medical purposes such as diagnosis, prevention, or treatment. An AI system qualifies as a medical device, or as a part thereof, if it serves a medical purpose on its own and is intended for use with individual patients. The MDR and the IVDMR distinguish various classes of devices, some of which are subject to a third-party conformity assessment. Where medical devices are required to undergo such third-party conformity assessment, they fall under the scope of the AI Act if they incorporate an AI system. For example, external hearing aids or remote monitoring devices for active implantable devices which include an AI system will be classified as “high-risk” and will therefore be subject to the stringent obligations under the AI Act.

The AI Act does not duplicate the obligations already included in the MDR and the IVDR, but rather provides requirements complementing the obligations under the MDR and the IVDR. For example, the AI Act does not introduce a separate or parallel conformity assessment process for such systems. Instead, it mandates that AI-specific requirements—such as those related to data governance, transparency, and human oversight—be addressed within the framework of existing MDR/IVDR procedures. This ensures that manufacturers are not subject to conflicting or redundant obligations.

Apart from AI systems as medical devices, other AI systems used in the life sciences sector may also fall within the scope of the AI Act. AI systems intended to be used for biometric categorisation could equally qualify as “high-risk”. This may for example include access systems in hospitals based on facial recognition. In addition, as in other sectors, the use of chatbots based on AI in the life sciences sector will be subject to the rules regarding transparency for AI systems which interact directly with individuals or are capable of generating realistic content.

Under the AI Act, the primary responsibility for compliance lies with the provider of the AI system. The AI Act specifies that, in the case of high-risk AI systems that are safety components of medical devices, the product manufacturer shall be considered the provider of the high-risk AI system. However, the AI Act also imposes certain obligations on users, referred to as “deployers”. In the case of high-risk AI systems in the life sciences sector, users—such as healthcare professionals, hospitals, medical practitioners or pharmaceutical researchers —must use the system in accordance with the provider’s instructions, monitor its operation, and report any serious incidents or malfunctions. These users must therefore remain vigilant when deploying and using the system and ensure compliance with their respective operational and reporting duties under the AI Act.

Data Protection Considerations

AI systems in life sciences typically rely on vast datasets, including health and genetic data. This may include:

Direct sensitive personal data (e.g. medical history, test results, treatments, disabilities, etc.);
Indirect sensitive personal data, i.e. personal data that seem a priori non-sensitive but that might imply sensitive personal data (e.g. location data relating to hospital visits or certain specific dietary restrictions).

Under the General Data Protection Regulation (“GDPR”), such types of personal data are qualified as special categories of personal data, the processing of which is in principle prohibited unless an exception under article 9.2 of the GDPR applies.

In many cases, health data are processed for a primary purpose, such as the provision of medical treatment, and organisations often seek to repurpose such data for a secondary purpose, e.g. the training or use of an AI model for pharmaceutical research. When contemplating the processing of data for other purposes than the ones for which the data were originally collected, the GDPR installs a compatibility assessment. Only if the new purpose is deemed ‘compatible’ with the original purpose, no separate legal basis is required. The closer the new purpose approximates the initial purpose, the more likely it is deemed compatible. The reasonable expectations of the data subjects concerned should also be considered as part of this assessment.

“Scientific research” however is granted a special status, as the GDPR positions as a general rule that further processing for scientific research purposes is not deemed incompatible with the original purpose. In this regard, the European Data Protection Supervisor (EDPS) distinguishes ‘genuine research’ that aims to expand society’s collective knowledge and wellbeing from research that primarily serves private or commercial ends. According to the EDPS, only genuine research benefits from the aforementioned special status. This distinction is still subject to discussions on where to draw the line in medical and pharmaceutical research serving commercial ends. The training of an AI-model for a number of purposes, some of which rather commercially driven, may therefore not benefit from the compatibility-exception for research. In any event, data controllers may also rely on a separate legal basis for a new purpose, such as the data subject’s consent, the necessity for the purpose of a legitimate interest of the data controller or a third party, or the necessity for the performance of an agreement with the data subject.

Data controllers should also ensure compliance with the other general data protection principles when processing (sensitive) data as part of the use or training of AI systems. This includes implementing privacy-enhancing techniques, such as pseudonymizing – or even anonymizing – personal data prior to importing the dataset for training AI models. In addition, appropriate technical and organisational security measures should be taken to adequately protect personal data.

Intellectual Property Challenges

The invention of AI applications in life sciences often requires substantial investments. Inventors might feel the need to secure their investments by applying for a patent to obtain an exclusive right to commercialize.

European patent law, generally governed by the European Patent Convention (EPC), allows for the protection of (AI-related) inventions provided they meet the criteria of novelty, inventive step, and industrial applicability. However, the EPC excludes mathematical methods and computer programs “as such” from patentability, unless they contribute a “technical effect”. The EPO examines this criterion on a case-by-case basis. For example, a technical effect is established in case of an AI system improving the control of industrial hardware or allowing a more secure way of handling data. Therefore, where the AI-system provides a technical solution to a technical problem, it may be eligible to be patented. This is for example the case for a system for measuring blood glucose variability based on an AI-application. On the contrary, the patenting of AI-systems performing purely non-technical tasks, such as AI systems merely improving aesthetics, is not accepted. Nonetheless, the line between patentable subject matter and unpatentable abstract algorithms remains blurred, particularly in AI-driven drug discovery.

In addition, questions arise regarding the patentability of substances or treatments developed by AI without direct human input. The European Patent Office (EPO) currently requires a human inventor, complicating the protection of AI-generated inventions.

Another complexity relates to the requirement of disclosure when applying for a patent. The boundaries of the scope of disclosure are determined by the fictional notion of the person skilled in the art (the “PSA”). The PSA is described as “a skilled practitioner in the relevant field of technology who is possessed of average knowledge and ability and is aware of what was common general knowledge in the art at the relevant date”. Disclosure of the invention is sufficient when it enables the PSA to carry out the invention him or herself. On several occasions, the EPO has already indicated that this would also require the disclosure of the training data, oftentimes highly sensitive or commercially valuable information. For example, in a case where the applicant filed an invention related to a method for determining the volume of blood pumped by the heart per unit at a time, the Technical Board of Appeal of the EPO ruled that the training data set to develop the neural network had to be disclosed.

Entities in the life sciences sector should therefore carefully consider the optimal strategy to protect their investments when developing or using AI.

Conclusion and Future Outlook

As AI continues to reshape the life sciences sector, the interplay between innovation and regulation becomes increasingly complex. The AI Act introduces a sector-agnostic but risk-based approach that directly affects life sciences applications, particularly in the realm of medical devices. At the same time, organisations must navigate overlapping legal frameworks such as the GDPR and intellectual property law, each presenting its own set of compliance challenges and interpretative uncertainties.

In our upcoming articles, we will further explore the intersections between the AI Act and other sectors and regulations.

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Ketryx Closes $39M Series B Round to Power the Future of Regulated Artificial Intelligence for Life Sciences

Published

26 minutes ago

September 5, 2025

James Dargan

Insider Brief

Ketryx raised $39M Series B led by Transformation Capital, with participation from Lightspeed, MIT’s E14 Fund, Ubiquity Ventures, and 53 Stations, bringing total funding to over $55M; Vinay Shah of Transformation Capital joins the board.
Its AI-native compliance platform automates validation, traceability, and regulatory workflows (FDA/EU MDR-ready), enabling life sciences teams to achieve up to 90% faster documentation and 10x quicker release cycles without sacrificing safety.
Already used by three of the top five global medtech companies and innovators like DeepHealth and Heartflow, Ketryx is positioning itself as the key AI infrastructure layer for regulated product development in healthcare and beyond.

Ketryx, the AI-powered compliance platform helping life sciences companies ship safer products faster, has announced a $39 million Series B led by Transformation Capital, with participation from existing investors including Lightspeed Venture Partners, MIT’s E14 Fund, Ubiquity Ventures, and 53 Stations. This latest round brings the company’s total funding to over $55 million, and Vinay Shah, Partner and Founding Team Member at Transformation Capital, will join Ketryx’s board.

Ketryx is solving one of the most difficult challenges in the life sciences: the need to accelerate product innovation without compromising safety or compliance. This challenge is more urgent than ever with teams racing to incorporate AI into regulated workflows and products.

“I’ve spent the last decade at the intersection of AI and life sciences, watching it evolve from an emerging tool to a critical application for patients,” said Erez Kaminski, CEO and founder of Ketryx. “It’s now time to accelerate adoption and ensure AI is safe, reliable, and ready for regulated environments.”

Life sciences teams are struggling to balance rigorous compliance requirements amid the rapidly accelerating pace of innovation. While cloud-based tools and rapidly evolving LLMs are transforming what’s possible, these regulated teams are still operating on infrastructure not designed for this velocity of change.

Ketryx is an AI-native compliance platform built to meet this challenge. It automates validation, traceability, and regulatory workflows — including FDA/EU MDR-ready documentation — across the product development lifecycle to help teams release safer products faster. Customers report up to a 90% reduction in documentation time and over 10x faster release cycles.

“In Medtech, long-term success depends on balancing innovation with the uncompromising demands of safety and compliance,” said Bill Hawkins, former CEO of Medtronic and new Ketryx investor. “This balance has historically been hard to achieve. Ketryx has built the infrastructure that allows both to advance together. Their ability to deliver this level of rigor at true enterprise scale is why I’m proud to support them as they shape the future of regulated software.”

The company’s platform is built for the enterprise and already used by three of the top five global medtech companies, several Fortune 500 organizations, and AI-powered companies such as DeepHealth, Heartflow, and Aignostics. With adoption accelerating, Ketryx is emerging as the key AI infrastructure layer for product development in regulated industries.

“Medtech teams are leading the way in applying artificial intelligence to improve patient outcomes, creating products that meet the highest safety and regulatory standards,” said Vinay Shah, Partner and Founding Team Member at Transformation Capital. “In our diligence, Fortune 500 giants and fast-growing innovators consistently praised Ketryx for proving that compliance can accelerate, rather than slow, technological progress. We believe Ketryx is defining the future of regulated infrastructure across industries and are proud to back them in their next stage of growth.”

Kaminski continued, “Having Transformation Capital, the pre-eminent digital health VC & growth equity firm, as our lead partner, gives us more than just capital. They understand exactly what it takes to build and scale healthcare technology companies. With their backing and industry connections, we’re continuing our global expansion, accelerating our product roadmap, and hiring rapidly in both Boston and Austria.”

With real-time traceability and documentation, Ketryx brings zero-lag compliance to the heart of product development, empowering teams to release more products, more safely, and faster than ever before.

About Ketryx
Ketryx transforms the product lifecycle of life science teams to deliver safer products, faster. Trusted by three of the world’s top five medical device manufacturers, its AI-powered compliance platform overlays existing tools to automate documentation, create traceability, and accelerate release cycles — without disrupting existing workflows. Ketryx AI Agents cut manual work by 90 percent and close compliance gaps, elevating speed and quality across the entire product lifecycle. For more information, visit www.ketryx.com.

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How could an OpenAI partnership with Broadcom shake up Silicon Valley’s chip hierarchy?

Published

1 hour ago

September 5, 2025

Dina Bass

Broadcom Inc. is helping OpenAI design and produce an artificial intelligence accelerator from 2026, getting into a lucrative sphere dominated by Nvidia Corp. Its shares jumped by the most since April.

The two firms plan to ship the first chips in that lineup starting next year, a person familiar with the matter said, asking to remain anonymous discussing a private deal. OpenAI will initially use the chip for its own internal purposes, the Financial Times reported earlier.

Broadcom’s shares surged as much as 16% in New York trading on Friday, adding more than $200 billion to the company’s market value. Nvidia’s stock was down as much as 4.3% at $164.22, its biggest intraday decline since May.

Chief Executive Officer Hock Tan made veiled references to that partnership on Thursday when he said Broadcom had secured a new client for its custom accelerator business. Tan said the company has secured more than $10 billion in orders from the new customer, which the person identified as OpenAI.

Accelerators are essential to the development of AI at big tech firms from Meta Platforms Inc. to Microsoft Corp. Bloomberg News has previously reported that OpenAI and Broadcom were working on an inference chip design, intended to run or operate artificial intelligence services after they had been trained.

“Last quarter, one of these prospects released production orders to Broadcom,” Tan said, without naming the customer.

Broadcom is among the chip designers benefiting from a post-ChatGPT boom in AI development, in which companies and startups from the US to China are spending billions to build data centers, train new models and research breakthroughs in a pivotal new technology. On Thursday, Tan told investors the chipmaker’s outlook will improve “significantly” in fiscal 2026, helping allay concerns about slowing growth.

Tan had previously said that AI revenue for 2026 would show growth similar to the current year — a rate of 50% to 60%. Now, with a new customer that he said has “immediate and pretty substantial demand,” the rate will accelerate in a way that will be “fairly material,” Tan said.

“We now expect the outlook for fiscal 2026 AI revenue to improve significantly from what we had indicated last quarter,” he said.

Broadcom’s quarterly results initially drew a tepid reaction from investors, a sign they were anticipating a bigger payoff from the AI boom. After fluctuating in the wake of the report, the stock gained more than 3% during the conference call.

Sales will be about $17.4 billion in the fiscal fourth quarter, which runs through October, the company said in an earlier statement. Analysts had projected $17.05 billion on average, though some estimates topped $18 billion, according to data compiled by Bloomberg.

Expectations were high heading into the earnings report. Broadcom shares more than doubled since hitting a low in April, adding about $730 billion to the company’s market value and making them the third-best performer in the Nasdaq 100 Index.

Investors have been looking for signs that tech spending remains strong. Last week, Nvidia gave an underwhelming revenue forecast, sparking fears of a bubble in the artificial intelligence industry.

Though Broadcom hasn’t experienced Nvidia’s runaway sales growth, it is seen as a key AI beneficiary. Customers developing and running artificial intelligence models rely on its custom-designed chips and networking equipment to handle the load. The shares had been up 32% for the year.

During the call, Tan said he and the board have agreed that he will stay as Broadcom CEO until 2030 “at least.”

In the third quarter ended Aug. 3, sales rose 22% to almost $16 billion. Profit, excluding some items, was $1.69 a share. Analysts had estimated revenue of about $15.8 billion and earnings of $1.67 a share.

Sales of AI semiconductors were $5.2 billion, compared with an estimate of $5.11 billion. The company expects revenue from that category to reach $6.2 billion in the fourth quarter. Analysts projected $5.82 billion.

Other AI-focused chipmakers have stumbled in recent days. Shares of Marvell Technology Inc., a close Broadcom competitor in the market for custom semiconductors, plunged 19% on Friday after the company’s data center revenue missed estimates.

Broadcom’s Tan has been upgrading the company’s networking equipment to better transfer information between the pricey graphics chips at the heart of AI data centers. As his latest comments suggest, Broadcom is also making progress finding customers who want custom-designed chips for AI tasks.

Tan has used years of acquisitions to turn Broadcom into a sprawling software and hardware giant. In addition to the AI work, the Palo Alto, California-based company makes connectivity components for Apple Inc.’s iPhone and sells virtualization software for running networks.

Bass writes for Bloomberg.

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Silicon Valley executives gather at White House dinner and pledge AI investments

Published

2 hours ago

September 5, 2025

Josh Wingrove Lauren Dezenski

Meta Platforms Inc.’s Mark Zuckerberg and Apple Inc.’s Tim Cook joined tech industry leaders in touting their pledges to boost spending in the US on artificial intelligence during a dinner hosted by President Donald Trump that highlighted his deepening relationship with Silicon Valley.

In his opening remarks, Trump addressed a key concern of tech companies: ensuring there’s enough energy to meet surging power demands from the data centers behind the AI boom.

“We’re making it very easy for you in terms of electric capacity and getting it for you, getting your permits,” Trump said in the White House State Dining Room. “We’re leading China by a lot, by a really, by a great amount.”

Thursday’s dinner marked a rare gathering in Washington of top executives and founders from some of the world’s most valuable tech companies — all vying for an edge in the emerging field of AI. Attendees also included OpenAI Inc.’s Sam Altman, Alphabet Inc.’s Sundar Pichai and co-founder Sergey Brin, and Microsoft Corp.’s Satya Nadella and Bill Gates.

The president went around the table asking executives to talk about their plans. Corporate leaders took turns highlighting their efforts to expand in the US, with each expressing gratitude for administration policies they see as bolstering efforts to advance AI. Trump asked Zuckerberg to speak first.

“All of the companies here are building, just making huge investments in the country in order to build out data centers and infrastructure to power the next wave of innovation,” the Meta CEO told Trump. Pressed by the president on how much his company was investing, Zuckerberg said “at least $600 billion” through 2028.

“That’s a lot,” Trump said. In recent days, the president has touted a massive data center Meta is building in Louisiana that will cost $50 billion.

Trump has drawn tech executives into his orbit with an agenda aimed at lowering tax and regulatory burdens for business in a bid to ramp up investments in the US and secure the country’s dominance in cutting-edge tech sectors. The burgeoning artificial intelligence field has been a centerpiece of that focus.

Trump’s White House AI czar, Silicon Valley venture capitalist David Sacks, in July helped unveil a sweeping action plan calling for easing regulation of artificial intelligence, stepping up research and development, and boosting domestic energy production to fuel energy-hungry data centers — all to ensure the US keeps an edge over rivals such as China.

The president has secured billions in corporate commitments to drive construction of AI infrastructure. On Thursday, the White House hailed Hitachi Energy’s announcement that it planned to invest more than $1 billion in electric grid infrastructure that could support AI’s growing power demands.

More broadly, companies have announced plans to bolster US investment as they look to avoid tariffs Trump is placing on imports to spur a shift toward domestic manufacturing of critical goods. Trump has indicated that some companies that commit to building in the US could get a break from some tariffs.

Cook, whose company last month committed to spending an additional $100 billion on domestic manufacturing for a total pledge of $600 billion, thanked Trump for “setting the tone such that we could make a major investment.”

The president indicated that Cook’s investment promise would help spare Apple from tariffs on semiconductor imports that the administration has plans to impose. “Tim Cook would be in pretty good shape,” Trump said.

Trump’s relationship with Silicon Valley took wing at his swearing-in ceremony in January, when Zuckerberg, Cook and Pichai each had prominent seats after having donated millions toward the inauguration. Trump and his allies will be eager to tap those pockets again ahead of next year’s midterm elections to determine control of Congress.

Earlier Thursday, many of the same executives joined first lady Melania Trump for a discussion on AI, where she hailed the business leaders as visionaries and urged their cooperation in helping responsibly guide the broader adoption of AI technology.

The first lady sat next to Trump during the White House dinner. Other attendees at the evening event included Oracle Corp. CEO Safra Catz and Lisa Su, the CEO of Advanced Micro Devices Inc.

The dinner was originally intended to be held in the newly renovated White House Rose Garden, where Trump installed stone pavers and furnished the space with patio tables and a sound system after complaining that the previous grass surface was unsuitable for large events. But inclement weather forced officials to move the event inside.

Wingrove and Dezenski write for Bloomberg.

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