Category: 4. Technology

We recently learned that Samsung started working on Android 16-based One UI 8 for the Galaxy A55, and now we hear that the Korean brand has started testing One UI 8 for the Galaxy S24 FE as well.

Samsung Galaxy S24 FE

Samsung is testing One UI 8 on the Galaxy S24 FE with firmware version S721BXXU6CYG1/S721BOXM6CYG1/S721BXXU6CYG1, which isn’t publicly available yet. And considering how Samsung directly rolled out the stable build of One UI 7 for the S24 FE without releasing an open beta, the smartphone may not be a part of the One UI 8 beta program either.

As far as the stable build of One UI 8 for the Samsung Galaxy S24 FE is concerned, we hope it will be released by the end of this year since the smartphone received One UI 7’s stable update in April.

Samsung Galaxy S24 FE

Via

Syntho has been quietly building a name for itself in electronic-music production circles with its service providing courses in mixing, mastering, sound design and arrangement.

Now the UK-based startup, founded by musician Josh Baker, is taking some of those courses to an even bigger platform: Spotify. The ‘bite-sized’ lessons are part of the streaming service’s ‘Courses’ hub in the UK, which launched in March 2024.

Syntho is the second dedicated music-edtech provider for the hub, following PlayVirtuoso (since rebranded as Whatclass), although another of the partners – BBC Maestro – has some music courses alongside other topics.

A curated selection of Syntho courses – described by CEO Hazel Savage as “a highlight reel” of its full archive – can be accessed by Spotify Premium listeners as part of their subscription, for no extra cost.

“Spotify already have 15 million UK subscribers. Literally, a better go-to-market plan could not have landed in my lap!” Savage told Music Ally ahead of the launch. “Being able to put our content in front of that volume of people is incredible.”

Savage joined Syntho as CEO in February this year, but has a long history in music-tech that includes roles at Shazam, Pandora and BandLab before founding AI-metadata startup Musiio in 2018, then selling it to SoundCloud four years later.

She spent nearly three years as SoundCloud’s VP of music intelligence while also angel investing in startups including AudioShake, un:hurd Music, Masterchannel, Flossy and AI|Coustics, before taking the Syntho job.

“This is a company that did $700k in revenue last year, yet a lot of people haven’t heard of them. Anything that puts us more in the public spotlight is exciting,” said Savage.

“We will be able to bring people back from Spotify into the Syntho ecosystem as well. They’re very non-restrictive: the Syntho brand and the clickback to our website will be there. They’re not trying to gatekeep the content, so it works well for us.”

While the terms of the agreement are confidential, Savage said that the Spotify deal does generate revenue for Syntho – it’s not pure promotion – nor does it rely on the startup charging Spotify users to watch the courses.

“We already have 700 videos of paywalled content on Syntho, so I didn’t feel the need to paywall it on another platform. For me it’s a shop window,” she said.

On its site, Syntho charges £69.99 a month for an all-you-can-eat subscription, although the price falls to £43 a month and $33.33 a month for people who pay quarterly or annually respectively.

The courses are presented by working artists and producers, with Baker joined by Liquid Earth, Sweely, Alisha and a growing number of other musicians on its roster.

“The majority of Syntho users on our platform are on web. We must be the only music-tech company who are not mobile-first!” said Savage.

“If you’re sitting down to do Ableton or music production, you’re going to be at a desktop or a laptop, and probably multi-screening with two monitors. We do have iOS and Android apps, but we massively skew towards web. Spotify massively skews towards mobile, so it’s a complementary platform for that reason too.”

Savage thinks Syntho is part of a tech trend that isn’t being talked about much in public yet: of investors starting to move away from investments in AI startups in favour of “things that cannot be replicated by AI, which they see as a safer bet in retaining value”.

This may sound counterintuitive at a time of fears in all sorts of industries that AI is coming for people’s jobs. But as Savage points out, AI is also commoditising itself to some degree. When she started Musiio, building proprietary AI models was hard work. Now, with open-source and off-the-shelf models “there is not so much core IP around AI that’s investable”.

“Investors are putting a thesis around ‘how do we shore up our investments so that they’re profitable in the long run?’ I see this in other music verticals too. What are the hyper-specific, hyper-personalised, cannot-be-replicated [by AI] elements of our industry?” she explained.

“You can fake some things with a chatbot, but the minute people figure it out or it falls over, it’s worth less than nothing. You could type into ChatGPT ‘How do I use Ableton?’ and it will churn out a lot of text. But is it accurate? Will it really help you?”

“Or you can learn from someone like Josh Baker who’s doing a million streams a month on Spotify. That experienced, authoritative voice is what’s valuable… VCs are starting to spend their money in stuff that’s very specifically not AI, and not replicable. And investors are always two years ahead of where everyone else is…”

That’s not to say that Syntho is a not-AI company. Savage said that it’s looking for sensible ways to use AI technologies in its business: for example dubbing its courses into other languages. And as AI finds its way into more electronic musicians’ workflows, Syntho may well provide some courses about that.

Savage has followed the recent news of GenAI music firm Suno’s acquisition of browser-based DAW WavTool with interest – “So they made creation as easy as possible, and now they’re going to make it harder again?” – but thinks there’s a question that isn’t being asked loudly enough about Suno, Udio and their peers.

“The court cases are so huge, the raises of 100-mil plus are so big, we’ve never actually addressed the real question with Suno and Udio: is there any actual traction? Who is using those platforms,” she said. “Would anyone pay for this, and if so, are they actually going to use it?”

However, Savage’s priority is building more traction for Syntho, and plotting a careful course for its expansion both geographically and in terms of the content it offers.

“As CEO I’m looking at what does this ecosystem look like in 12-24 months. Is it a white-labelled Syntho? What does it look like if we want to roll out D’n’B (drum’n’bass) and techno? I’m looking at all of this,” she said. Over-expansion will be avoided: for example rushing to move beyond electronic music at too breakneck a pace.

“I’ve seen a lot of other music-tech platforms. If you try to do too much too quickly, what you are and what you do becomes confusing. You can throw in 20 features and suddenly people are like: ‘I don’t know what any of this is’,” she said.

“You see so many platforms do this and that. Now they’re a distributor. Now they do mastering. Now they’re also a label. Jesus, what IS the product?!” continued Savage.

“As much as I’d love to grow Syntho at lightning speed and become the biggest platform and branch out into all the genres, I want to do it in a way where we never lose the core of who we are, and who our community is. How do I grow this without losing what was good about it in the first place?”

Syntho is not actively raising funding right now, although Savage is “always open to conversations”. Her experience with Musiio, which raised $2m over its lifetime, taught her that there is a value in only raising money when you need it – and not raising more than you need to.

“There are some companies out there who’ve raised so much money, an exit is almost impossible. Recorded music is a $29.6bn market globally. It’s small compared to pharma or finance, and in an industry this size your chances of a billion-dollar exit are slim,” she said.

“Companies who’ve raised $10m or $20m need an exit of $100m to make anything they do worthwhile, and. the number of exits out there at that scale is very small. You either do it the Musiio way, and raise a little bit and sell at a good price, or you go full Kobalt and you’re a $1.2bn exit. There’s very little happening in that space in between.”

“Don’t get me wrong: I’m a big believer in this industry. I’ve personally invested in nine companies, all in music-tech. But you can really screw yourself over if you raise too heavily. You price yourself out of the market.”

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Participant characteristics

A total of 147 medical oncologists completed the survey, corresponding to approximately 11% of the estimated 1340 medical oncologists practicing in Türkiye [4]. The median age of participants was 39 years (IQR: 35–46), and 63.3% were male. Respondents had a median of 14 years (IQR: 10–22) of medical experience and a median of 5 years (IQR: 2–14) specifically in oncology. Nearly half (47.6%) practiced in university hospitals, followed by 31.3% in training and research hospitals, and the remainder in private or state settings (Table 1). In terms of academic rank, residents/fellows constituted 38.1%, specialists 22.4%, professors 21.1%, associate professors 16.3%, and assistant professors 2.0%. Respondents were distributed across various urban centers, including major cities such as Istanbul and Ankara, as well as smaller provinces, reflecting a broad regional representation of Türkiye’s oncology workforce.

Most of the participants completed the survey from Central Anatolia Region of Türkiye (34.0%, n = 50), followed by Marmara Region (27.2%, n = 40), Eagean Region (17.0%, n = 25) and Mediterranian Region (10.2%, n = 15). The distrubution of the participants with regional map of Türkiye is presented in Fig. 1.

AI usage and education

A majority (77.5%, n = 114) of oncologists reported prior use of at least one AI tool. Among these, ChatGPT and other GPT-based models were the most frequently used (77.5%, n = 114), indicating that LLM interfaces had already penetrated clinical professionals’ workflow to some extent. Other tools such as Google Gemini (17.0%, n = 25) and Microsoft Bing (10.9%, n = 16) showed more limited utilization, and just a small fraction had tried less common platforms like Anthropic Claude, Meta Llama-3, or Hugging Face. Despite this relatively high usage rate of general AI tools, formal AI education was scarce: only 9.5% (n = 14) of respondents had received some level of formal AI training, and this was primarily basic-level. Nearly all (94.6%, n = 139) expressed a desire for more education, suggesting that their forays into AI usage had been largely self-directed and that there was a perceived need for structured, professionally guided learning.

Regarding sources of AI knowledge, 38.8% (n = 57) reported not using any resource, underscoring a gap in continuing education. Among those who did seek information, the most common channels were colleagues (26.5%, n = 39) and academic publications (23.1%), followed by online courses/websites (21.8%, n = 32), popular science publications (19.7%, n = 29), and professional conferences/workshops (18.4%, n = 27). This pattern suggests that while some clinicians attempt to inform themselves about AI through peer discussions or scientific literature, many remain unconnected to formalized educational pathways or comprehensive training programs.

Self-assessed AI knowledge

Participants generally rated themselves as having limited knowledge across key AI domains (Fig. 2A). More than half reported having “no knowledge” or only “some knowledge” in areas such as machine learning (86.4%, n = 127, combined) and deep learning (89.1%, n = 131, combined). Even fundamental concepts like LLM sand generative AI were unfamiliar to a substantial portion of respondents. For instance, nearly half (47.6%, n = 70) had no knowledge of LLMs, and two-thirds (66.0%, n = 97) had no knowledge of generative AI. Similar trends were observed for natural language processing and advanced statistical analyses, reflecting a widespread lack of confidence and familiarity with the technical underpinnings of AI beyond superficial usage.

Attitudes toward AI integration in oncology

When asked to evaluate AI’s role in various clinical tasks (Fig. 2B), respondents generally displayed cautious optimism. Prognosis estimation stood out as one of the areas where AI received the strongest endorsement, with a clear majority rating it as “positive” or “very positive.” A similar pattern emerged for medical research, where nearly three-quarters of respondents recognized AI’s potential in academic field. In contrast, opinions on treatment planning and patient follow-up were more mixed, with a considerable proportion adopting a neutral stance. Diagnosis and clinical decision support still garnered predominantly positive views, though some participants expressed reservations, possibly reflecting concerns about reliability, validation, and the interpretability of AI-driven recommendations.

Broadening the perspective, Fig. 2C illustrates how participants viewed AI’s impact on aspects like patient-physician relationships, social perception, and health policy. While most believed AI could improve overall medical practices and potentially reduce workload, many worried it might affect the quality of personal interactions with patients or shape public trust in uncertain ways. Approximately half recognized potential benefits for healthcare access, but some remained neutral or skeptical, perhaps concerned that technology might not equally benefit all patient populations or could inadvertently exacerbate existing disparities.

Ethical and regulatory concerns

Tables 2 and 3, along with Figs. 3A–C, summarize participants’ ethical and legal considerations. Patient management (57.8%, n = 85), article or presentation writing (51.0%, n = 75), and study design (25.2%, n = 37) emerged as key activities where the integration of AI was viewed as ethically questionable. Respondents feared that relying on AI for sensitive clinical decisions or academic tasks could compromise patient safety, authenticity, or scientific integrity. A subset of respondents reported utilizing AI in certain domains, including 13.6% (n = 20) for article and presentation writing, and 11.6% (n = 17) for patient management, despite acknowledging potential ethical issues in the preceding question. However, only about half of the respondents who admitted using AI for patient management identified this as an ethical concern. This discrepancy suggests that while oncologists harbor concerns, convenience or lack of guidance may still drive them to experiment with AI applications.

Moreover, nearly 82% of participants supported using AI in medical practice, yet 79.6% (n = 117) did not find current legal regulations satisfactory. Over two-thirds advocated for stricter legal frameworks and ethical audits. Patient consent was highlighted by 61.9% (n = 91) as a critical step, implying that clinicians want transparent processes that safeguard patient rights and maintain trust. Liability in the event of AI-driven errors also remained contentious: 68.0% (n = 100) held software developers partially responsible, and 61.2% (n = 90) also implicated physicians. This suggests a shared accountability model might be needed, involving multiple stakeholders across the healthcare and technology sectors.

To address these gaps, respondents proposed various solutions. Establishing national and international standards (82.3%, n = 121) and enacting new laws (59.2%, n = 87) were seen as pivotal. More than half favored creating dedicated institutions for AI oversight (53.7%, n = 79) and integrating informed consent clauses related to AI use (53.1%, n = 78) into patient forms. These collective views point to a strong desire among oncologists for a structured, legally sound environment in which AI tools are developed, tested, and implemented responsibly.

Ordinal regression analysis of factors associated with AI knowledge, attitudes, and concerns

For knowledge levels, the ordinal regression model identified formal AI education as the sole significant predictor (ß = 30.534, SE = 0.6404, p < 0.001). In contrast, other predictors such as age (ß = −0.1835, p = 0.159), years as physician (ß = 0.0936, p = 0.425), years in oncology (ß = 0.0270, p = 0.719), and academic rank showed no significant associations with knowledge levels in the ordinal model.

The ordinal regression for concern levels revealed no significant predictors among demographic factors, professional experience, academic status, AI education, nor current knowledge levels (p > 0.05) were associated with the ordinal progression of ethical and practical concerns.

For attitudes toward AI integration, the ordinal regression identified two significant predictors. Those willing to receive AI education showed progression toward more positive attitudes (ß = 13.143, SE = 0.6688, p = 0.049), and actual receipt of AI education also predicted progression toward more positive attitudes (ß = 12.928, SE = 0.6565, p = 0.049). Additionally, higher knowledge levels showed a trend toward more positive attitudes in the ordinal model although not significant (ß = 0.3899, SE = 0.2009, p = 0.052).

Table 4 presents the ordinal regression analyses examining predictors of AI knowledge levels, concerns, and attitudes among Turkish medical oncologists.

Qualitative insights

The open-ended responses, analyzed qualitatively, revealed several recurring themes reinforcing the quantitative findings. Participants frequently stressed the importance of human oversight, emphasizing that AI should complement rather than replace clinical expertise, judgment, and empathy. Data security and privacy emerged as central concerns, with some respondents worrying that insufficient safeguards could lead to breaches of patient confidentiality. Others highlighted the challenge of ensuring that AI tools maintain cultural and social sensitivity in diverse patient populations. Calls for incremental, well-regulated implementation of AI were common, as was the suggestion that education and ongoing professional development would be essential to ensuring clinicians use AI effectively and ethically.

In essence, while there is broad acknowledgment that AI holds promise for enhancing oncology practice, respondents also recognize the need for clear ethical standards, solid regulatory frameworks, comprehensive training, and thoughtful integration strategies. oncology care.