US Smartphone Sales Slump in January on Fewer Device Upgrades, Counterpoint Finds

Logo of an Apple store is seen as Apple Inc. reports fourth quarter earnings in Washington, US, January 27, 2022. (Reuters)
Logo of an Apple store is seen as Apple Inc. reports fourth quarter earnings in Washington, US, January 27, 2022. (Reuters)
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US Smartphone Sales Slump in January on Fewer Device Upgrades, Counterpoint Finds

Logo of an Apple store is seen as Apple Inc. reports fourth quarter earnings in Washington, US, January 27, 2022. (Reuters)
Logo of an Apple store is seen as Apple Inc. reports fourth quarter earnings in Washington, US, January 27, 2022. (Reuters)

US smartphone sales plunged 10% in January on weak demand for cheaper Android devices and as customers delayed upgrades ahead of the launch of Samsung Electronics' Galaxy S24 series, according to data from Counterpoint Research.

The research firm said U.S. smartphone sales last month were nearly half of the record levels seen in the same period in 2017, underscoring fears that the market may have peaked.

"Tough times in the volume-driven low-end coupled with delayed upgrades in anticipation of new products drove the market lower," said Maurice Klaehne, senior analyst at Counterpoint Research.

Smartphone sales have waned after the pandemic-driven boom, as an uncertain economic outlook and lack of major new features led consumers to stick with their existing devices.

Samsung has tried to drum up interest for its new Galaxy smartphones, which went on sale on Jan. 17, by offering multiple artificial intelligence (AI) functions including a two-way voice translation in real-time.

Counterpoint said the S24 series has performed well in the US market during the initial 1-2 weeks of launch, and that it could spark a rebound in smartphone sales in February.

Apple, meanwhile, continued to gain market share in the US last month, thanks to promotional offers for its iPhone 15 series, and as cost-conscious consumers sought its older iPhone 11 and iPhone 12 devices, whose prices have come down.

"This combination is enabling Apple to maintain stability in a market experiencing double-digit declines," Counterpoint said.



SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI
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SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

The Center of Excellence for Data Science and Artificial Intelligence at the Saudi Data and Artificial Intelligence Authority (SDAIA) and King Abdullah University of Science and Technology (KAUST) have introduced the MiniGPT-Med model.

The large multi-modal language model is designed to help doctors quickly and accurately diagnose medical radiology using artificial intelligence techniques.

Dr. Ahmed Alsinan, the Artificial Intelligence Advisor at the National Center for Artificial Intelligence and head of the scientific team at SDAIA, explained that the MiniGPT-Med model is capable of performing various tasks such as generating medical reports, answering medical visual questions, describing diseases, locating diseases, identifying diseases, and documenting medical descriptions based on entered medical images.

The model was trained on different medical images, including X-rays, CT scans, and MRIs.

The MiniGPT-Med model, derived from large-scale language models, is specifically tailored for medical applications and demonstrates significant versatility across different imaging methods, including X-rays, CT scans, and MRI. This enhances its utility in medical diagnosis.

Dr. Alsinan highlighted that the MiniGPT-Med model was developed collaboratively by artificial intelligence specialists from SDAIA and KAUST.

The model exhibits advanced performance in generating medical reports, achieving 19% higher efficiency than previous models. It serves as a general interface for radiology diagnosis, enhancing diagnostic efficiency across various medical imaging applications.