Artificial intelligence is increasingly transforming the health care industry, pushing major companies toward partnerships, acquisitions and innovation. AI solutions designed to enhance the comfort of both doctors and patients are reshaping clinical practices.
Software-driven imaging solutions, including AI, cloud platforms and integration systems, now account for 60% of the sector’s total revenue. This segment is growing at an annual rate of 43%, making it the fastest-expanding area within health care AI.
The global market for artificial intelligence-powered diagnostics, valued at $1.9 billion in 2024, is projected to surge to $46.6 billion by 2034, growing at an annual rate of 33.7%, according to recent research.
While AI in imaging leads the charge, AI-driven patient management tools and clinical decision support systems are also expanding rapidly, allowing startups to capture significant market share from industry giants.
Partnerships and acquisitions
Machine learning remains the most widely adopted AI technology in health care, used by 48% of organizations, followed by investments in computer vision, natural language processing (NLP), cloud-based systems and 3D/4D imaging technologies.
Siemens Healthineers has emerged as one of the most active players, striking a partnership with Google Cloud to expand its AI and cloud capabilities. Siemens has also acquired nuclear medicine startup Advanced Accelerator Applications (AAA) from Novartis and bought Dotmatics, a specialist in life sciences software.
Other tech companies are increasingly pivoting toward life sciences. France’s Dassault Systemes, for instance, now generates a significant portion of its revenue from the health care sector. Meanwhile, GE continues to invest in AI-driven startups, and Philips has ramped up restructuring and compliance-focused investments.
Over 100 startup collaborations
Amira Romani, senior vice president responsible for innovation and strategy ecosystems at Siemens Healthineers, stated that as part of their corporate culture, they believe they cannot solve all health-related problems alone.
“Innovation is born not in isolation, but through collaboration. Our focus is to identify win-win scenarios that create greater impact for patients. This could involve startups or large tech companies, depending on the use case,” said Romani.
Amira Romani, senior vice president at Siemens Healthineers. (Courtesy of Siemens Healthineers)
“We are currently collaborating with more than 100 startups worldwide, integrating their solutions into our product portfolio or adopting new innovations. At the same time, we are forming partnerships with major tech firms that can drive transformation in healthcare,” she noted.
“Let me emphasize again: what matters most is the patient. The goal is not just to bring companies together, but to deliver tangible solutions that improve patient outcomes.”
Binging various technologies
Romani described AI as “the glue” that binds various technologies in health care together, saying that when used correctly, it enables early diagnosis, detection and personalized treatments for diseases.
“For me, AI acts as the glue that connects all these technologies. But doing AI just for the sake of AI is meaningless. The real goal is to integrate it into every aspect of healthcare, devices, workflows and solutions, to increase efficiency for both patients and healthcare professionals. We are accelerating this transformation by investing in scalable AI applications,” she noted.
Investments in AI-powered healthcare startups are also rising in Türkiye.
One of the country’s leading telecoms and technology companies, Türk Telekom’s venture capital arm, TT Ventures, has backed several healthtech startups, including Virasoft and Albert Health, as well as accelerator alumni Aivisiontech and Hevi AI.
These startups are driving advances in early diagnosis, chronic disease management and preventative health care.
Sharing Türkiye’s solutions with world
Romani noted that Siemens Healthineers opened an innovation center in Istanbul two months ago.
“The energy of the local ecosystem here is inspiring. This passion for innovation and solution-driven thinking convinced us that we must have a deeper presence in Türkiye,” she noted.
“We are now collaborating with startups, government bodies, and other sectors to better understand the Turkish ecosystem. Our goal is to scale solutions developed here to the global market. After all, many health care challenges are universal – if we can solve them here, why not bring those solutions to the world?”
AI startup count in Türkiye grows 17-fold in 8 years
The number of artificial intelligence startups in Türkiye has jumped 17-fold over the past eight years, reaching 411 as of the second quarter of 2025, according to the latest update of the Türkiye AI Startup Map by the Türkiye Artificial Intelligence Initiative (TRAI).
TRAI, which aims to foster institutional and societal AI awareness and strengthen the country’s AI ecosystem, added 20 new startups to its map this quarter. Back in 2017, there were only 24 AI-focused startups operating in Türkiye.
The explosive growth reflects both the rapidly evolving nature of AI technologies and the agility of startups in adapting to them.
A significant share of these ventures is concentrated in technology-driven segments such as generative AI, smart platforms, computer vision and machine learning, highlighting the shifting landscape of Türkiye’s AI ecosystem.
Generative AI leads momentum
Generative AI continues to dominate startup activity in Türkiye. Of the 20 startups added in the second quarter of 2025, six are focused on generative AI solutions, pointing to sustained momentum in the sector.
Three new startups were added under the smart platforms category, and two each in computer vision and machine learning.
The remaining startups are spread across various AI applications, including natural language processing (NLP), data analytics, IoT, infrastructure services and chatbots.
Local startups are increasingly developing solutions in content creation, large language models, AI-powered search assistants and personalized AI applications, demonstrating that these technologies are gaining both strategic and competitive significance within Türkiye’s AI ecosystem.
The growing adoption of generative AI also signals a broader transformation that is reshaping the local innovation landscape.
Maturity of Türkiye’s AI scene
Can Sinemli, general manager of TRAI, said the latest data underscores the increasing maturity of Türkiye’s AI startup scene.
“The number of startups surpassing 400 points not only signifies a quantitative increase but also a qualitative and specialized growth. Each new addition to our map enriches the diversity of technologies and application areas. This dynamic structure offers attractive opportunities for investors while also creating a strong foundation for collaboration,” said Sinemli.
Can Sinemli, general manager of Türkiye Artificial Intelligence Initiative (TRAI). (Courtesy of TRAI)
“The transformation driven by frontier technologies like generative AI marks a significant step toward enhancing the global competitiveness of Türkiye’s ecosystem.”
Corporates increase engagement
Interest from established corporations is also growing.
TT Ventures, the venture capital arm of Türk Telekom, one of Türkiye’s leading telecoms and technology companies, has invested in several AI startups through its accelerator program, Pilot and its portfolio fund.
Among the standouts are Segmentify, which provides AI-powered personalized recommendation engines for e-commerce, Mindsite, offering an AI platform for real-time marketing insights across digital commerce channels, and Mistikist, which combines neuroscience with AI to develop tools for reducing mental stress and anxiety.
Among others is the T4 People Analytics, an AI-driven HR platform that analyzes workforce data to deliver employee-centered insights.
Turkish 212 NexT fund backs French sustainable dye startup EverDye
Türkiye’s first vertical deep-technology fund, 212 NexT, has joined the 15 million euros ($17.57 million) Series A funding round of French textile technology startup EverDye, which develops environmentally friendly dyeing solutions.
The investment reflects 212 NexT’s strategy to support scalable deep-tech ventures tackling industrial sustainability challenges. EverDye, founded in 2021, produces bio-based pigments and low-energy, room-temperature dyeing technologies aimed at revolutionizing the textile industry’s heavy environmental footprint.
Strong investor consortium
The round was co-led by France-based venture capital firm Daphni and Credit Mutuel Innovation, the investment arm of Credit Mutuel Group. Other participants included the European Innovation Council (EIC), Ring Capital, and existing investors Asterion Ventures and Maki.vc, alongside 212 NexT.
EverDye CEO Philippe Berlan said dyeing processes account for nearly half of the textile industry’s greenhouse gas emissions.
“At EverDye, we’ve developed a solution that can reduce this impact in one-third of the time, using significantly less energy and environmentally friendly ingredients,” Berlan noted.
Gizem Yağız, managing partner at 212 NexT, emphasized the strategic importance of the investment.
“EverDye offers a scalable and industry-integrable high-tech solution to some of the most deep-rooted environmental challenges in the textile sector,” Yağız said.
“By meaningfully reducing energy and water consumption while providing cost advantages to manufacturers, the technology proves to be not only sustainable but also economically attractive.”
Textile sustainability breakthrough
EverDye aims to radically reduce the environmental footprint of the textile industry. The startup’s technology enables dyeing in one-third the time of traditional processes, using significantly less energy and environmentally friendly ingredients.
Its commercial maturity was demonstrated through a successful capsule collection with Adore Me, a leading U.S. apparel brand owned by Victoria’s Secret. With its new investment, EverDye plans to scale its pilot-proven technology to an industrial level and forge broader collaborations with global textile brands.
AI shift: Tech leaders, academics explore enterprise transformation
Leaders from PwC, Nvidia, Red Hat and OdineLabs, alongside academics from Georgia Tech and Istanbul Technical University, gathered at a high-level event this week to discuss the transformative impact of artificial intelligence on organizations.
In his opening remarks, Alper Tunga Burak, CEO and chairperson of Odine, emphasized that artificial intelligence has evolved beyond being merely a technical concept.
“We are all witnessing how AI is shaping our ways of working, communication infrastructures and corporate strategies. After progressing at a theoretical level for many years, AI began a tangible transformation in our daily lives and business world in the 2010s, driven by the rise of deep learning, growing data volumes, powerful processing capabilities and advancing cloud infrastructures. This transformation is not just a technical development but a strategic choice that offers institutions the opportunity to rethink their processes and strengthen their competitiveness,” said Burak.
People are seen at the Odine meeting. (Courtesy of Odine)
“Being aware of the opportunities and impacts created by this strategic evolution initiated by AI, and accelerating the process, will greatly contribute to success.”
Following the opening remarks, OdineLabs Inc. CEO Bülent Kaytaz shared the company’s AI-focused research and development (R&D) vision and technology investments with participants.
The event saw representatives from leading firms such as PwC, Nvidia, Red Hat and OdineLabs delivering presentations spanning a wide range, from AI-powered enterprise decision systems and sustainable network infrastructures to data-driven operations management and next-generation security solutions.
One surprising outcome is that AI might end up making the most critical functions of banking, insurance, and trading, or the creative functions that require human insights, even more valuable.
“What happens is there’s going to be a premium on creativity and judgment that goes into the process,” said John Kain, who is head of market development efforts in financial services for AWS, in an interview with ZDNET via Zoom.
By process, he meant those areas that are most advanced, and presumably hardest to automate, such as a bank’s risk calculations.
Amazon AWS
“So much of what’s undifferentiated will be automated,” said Kaine. “But what that means is what actually differentiates the business and the ability to serve customers better, whether that’s better understanding products or risk, or coming up with new products, from a financial perspective, the pace of that will just go so much more quickly in the future.”
Amazon formed its financial services unit 10 years ago, the first time the cloud giant took an industry-first approach.
For eight years, Kaine has helped bring the cloud giant’s tools to banks, insurers, and hedge funds. That approach includes both moving workloads to the cloud and implementing AI, including the large language models (LLMs) of generative AI (Gen AI), in his clients’ processes.
“If you look at what we’re trying to do, we’re trying to provide our customers an environment where, from a security, compliance, and governance perspective, we give them a platform that ticks the boxes for everything that’s table stakes for financial services,” said Kaine, “but also gives them the access to the latest technologies, and choice in being able to bring the best patterns to the industry.”
Kaine, who started his career in operations on the trading floor, and worked at firms such as JP Morgan Chase and Nasdaq, had many examples of gains through the automation of financial functions, such as customer service and equity research.
Early use of AWS by financials included things such as back-testing portfolios of investments to predict performance, the kind of workload that is “well-suited to cloud” because it requires computer simulations “to really work well in parallel,” said Kaine.
“That ability to be able to do research much more quickly in AWS meant that investment research firms could quickly see those benefits,” he said. “You’ve seen that repeated across the industry regardless of the firm.”
Taking advantage of the tech
Early implementations of Gen AI are showing many commonalities across firms. “They’ll be repeatable patterns, whether it’s document processing that could show up as mortgage automation with PennyMac, or claims processing with The Travelers Companies.”
Such processes come with an extra degree of sensitivity, Kain said, given the regulated status of finance. “Not only do they have a priority on resilience as well as security, they have evidence that is in a far greater degree than any other industry because the regulations on financial services are typically very prescriptive,” he explained. “There’s a much higher bar in the industry.”
Finance has been an early adopter of an AI-based technology invented at AWS, originally called Zelkova, and that is now more generally referred to as “automated reasoning.” The technology combines machine-learning AI with mathematical proofs to formally validate security measures, such as who has access to resources in a bank.
“It was an effort to allow customers to prove that the security controls they put in place were knowably effective,” said Kain. “That was important for our financial services customers,” including hedge fund Bridgewater and other early adopters.
Now, automated reasoning is also being employed to fix Gen AI.
“You’re seeing that same approach now being taken to improve the performance of large language models, particularly with hallucination reduction,” he said.
To mitigate hallucinations, or “confabulations,” as the errors in Gen AI are more properly known, AWS’s Bedrock platform for running machine learning programs uses retrieval-augmented generation (RAG).
The RAG approach involves connecting an LLM to a source of validated information, such as a database. The source serves as a gold standard to “anchor” the models to limit error.
Once anchored, automated reasoning is applied to “actually allow you to create your own policies that will then give you an extra level of security and detail to make sure that the responses that you’re providing [from the AI model] are accurate.”
The RAG approach, and automated reasoning, are increasingly leading clients in financial services to implement “smaller, domain-specific tasks” in AI that can be connected to a set of specific data, he said.
Financial firms start with Gen AI use cases in surveys of enterprise use, including automating call centers. “From a large language model perspective, there are actually a number of use cases that we’ve seen the industry achieve almost immediate ROI [return on investment],” said Kain. “The foremost is customer interaction, particularly at the call center.”
AWS customers, including Principal Financial, Ally Financial, Rocket Mortgage, and crypto-currency exchange Coinbase, have all exploited Gen AI to “take those [customer] calls, transcribe them in real time, and then provide information to the agents that provide the context of why customers are calling, plus their history, and then guide them [the human call agents] to the right response.”
Coinbase used that approach to automate 64% of support calls, up from 19% two years ago, with the aim of reaching 90% in the future.
Coinbase presents its findings at AWS Summit.
Tiernan Ray/ZDNET
Finding fresh opportunities
Another area where automation is being used is in monitoring alerts, such as fraud warnings. It’s a bit like AI in cybersecurity, where AI handles a flood of signals that would overwhelm a human analyst or investigator.
Fraud alerts and other warnings “generate a large number of false positives,” said Kain, which means a lot of extra work for fraud teams and other financial staff to “spend a good chunk of their day looking at things that aren’t actually fraud.”
Instead, “customers can use large language models to help accelerate the investigation process” by summarizing the alerts, and then create a summary report to be given to the human investigator.
Verafin specializes in anti-money laundering efforts and is an AWS customer using this approach.
“They’ve shown they can save 80% to 90% of the time it takes to investigate an alert,” he said.
Another automation area is “middle office processing,” including customer inquiries to a brokerage for trade confirmation.
One AWS client, brokerage Jefferies & Co., has set up “agentic AI” where the AI model “would actually go through their inbox, saying, this is a request for confirming a price” of a securities trade.
That agent passes the request to another agent to “go out and query a database to get the actual trade price for the customer, and then generate the email” that gets sent to the customer.
“It’s not a huge process, it takes a human, maybe, ten, fifteen minutes to go do it themselves,” said Kain, “but you go from something that was minutes down to seconds through agents.”
The same kinds of applications have been seen in the mortgage and insurance business, he said, and in energy, with Canada’s Total Energy Services confirming contracts.
One of the “most interesting” areas in finance for Gen AI, said Kain, is in investment research.
Hedge fund Bridgewater uses LLMs to “basically take a freeform text [summary] about an investment idea, break that down into nine individual steps, and, for each step, kick off an [AI] agent that would go understand what data was necessary to answer the question, build a dependency map between the various trade-offs within an investment model, and then write the code to pull real-time data from the investment data store, and then generate a report like a first-year investment professional.”
Credit rating giant Moody’s is using agents to automate memos on credit ratings. However, credit ratings are usually for public companies because only these firms must report their financial data by law. Now, Moody’s peer, S&P Global, has been able to extend ratings to private companies by amassing snippets of data here and there.
“There’s an opportunity to leverage large language models to scour what’s publicly available to do credit information on private companies,” said Kain. “That allows the private credit market to have better-anchored information to make private credit decisions.”
These represent “just amazing capabilities,” said Kain of the AI use cases.
Moving into new areas
AI is not yet automating many core functions of banks and other financial firms, such as calculating the most complex risk profiles for securities. But, “I think it’s closer than you think,” said Kain.
“It’s not where we’ve completely moved to trusting the machine to generate, let’s say, trading strategies or risk management approaches,” said Kain.
However, the beginnings of forecasting and analysis are present. Consider the problem of calculating the impact of new US tariffs on the cash flows of companies. That is “happening today as partially an AI function,” he said.
Financial firms “are definitely looking at data at scale, reacting to market movements, and then seeing how they should be updating their positions accordingly,” he explained.
“That ability to ingest data at a global scale is something that I think is so much easier than it was a year ago,” because of Gen AI.
AWS customer Crypto.com, a trading platform for cryptocurrencies, can watch news feeds in 25 different languages using a combination of multiple LLMs.
“They are able to identify which stories are about currencies, and tell if that is a positive or negative signal, and then aggregate that as inputs to their customers,” for trading purposes. As long as two of the three models monitoring the feeds agreed, “they had conviction that there was a signal there” of value.
“So, we’re seeing that use of generative AI to check generative AI, if you will, to provide confidence at scale.”
Those human-centered tasks that remain at the core of banking, insurance, and trading are probably the most valuable in the industry, including the most complex functions, such as creating new derivative products or underwriting initial public offerings.
Those are areas that will enjoy the “premium” for creativity, in Kain’s view. Yet how much longer these tasks remain centered on human creation is an open question.
“I wish I had a crystal ball to say how much of that is truly automatable in the next few years,” said Kain.
“But given the tremendous adoption [of AI], and the ability for us to process data so much more effectively than even just two, three years ago, it’s an exciting time to see where this will all end up.”
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A consulting firm found that tech companies are “strategically overpaying” recruits with AI experience.
They found firms pay premiums of up to $200,000 for data scientists with machine learning skills.
The report also tracked a rise in bonuses for lower-level software engineers and analysts.
The AI talent bidding war is heating up, and the data scientists and software engineers behind the tech are benefiting from being caught in the middle.
Many tech companies are “strategically overpaying” recruits with AI experience, shelling out premiums of up to $200,000 for some roles with machine learning skills, J. Thelander Consulting, a compensation data and consulting firm for the private capital market, found in a recent report.
The report, compiled from a compensation analysis of roles across 153 companies, showed that data scientists and analysts with machine learning skills tend to receive a higher premium than software engineers with the same skills. However, the consulting firm also tracked a rise in bonuses for lower-level software engineers and analysts.
The payouts are a big bet, especially among startups. About half of the surveyed companies paying premiums for employees with AI skills had no revenue in the past year, and a majority (71%) had no profit.
Smaller firms need to stand out and be competitive among Big Tech giants — a likely driver behind the pricey recruitment tactic, a spokesperson for the consulting firm told Business Insider.
But while the J. Thelander Consulting report focused on smaller firms, some Big Tech companies have also recently made headlines for their sky-high recruitment incentives.
Meta was in the spotlight last month after Sam Altman, CEO of OpenAI, said the social media giant had tried to poach his best employees with $100 million signing bonuses.
While Business Insider previously reported that Altman later quipped that none of his “best people” had been enticed by the deal, Meta’s chief technology officer, Andrew Bosworth, said in an interview with CNBC that Altman “neglected to mention that he’s countering those offers.”
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