A groundbreaking advancement in photonic technology promises to redefine the landscape of medical artificial intelligence (AI) diagnostics, as a research consortium led by Professor Han Zhang at Shenzhen University has unveiled a novel all-fiber photonic AI platform utilizing black phosphorus (BP)-based tunable modulators. This innovative approach, which hinges on photon-based computing, surmounts the intrinsic constraints posed by traditional electronic processors, delivering unprecedented energy efficiency and ultrafast performance crucial for real-time clinical decision-making.

The prevailing paradigm in medical AI, deeply reliant on electronic architectures such as GPUs, faces severe limitations related to energy consumption, heat dissipation, and processing latency. These factors collectively impede the integration of AI into time-sensitive diagnostic workflows and exacerbate systemic carbon footprints. Photonic computing, with its reliance on photons rather than electrons, offers dramatically accelerated data transmission speeds and parallelism through wavelength multiplexing, alongside near-zero heat generation. Until now, however, the practical implementation of photonic AI had been hampered by the bulkiness, inefficiency, and fabrication challenges synonymous with conventional optical modulators.

Professor Zhang’s team has ingeniously engineered an atomically thin van der Waals heterostructure by stacking black phosphorus and molybdenum disulfide (MoS2), leveraging the extraordinary optoelectronic properties of these 2D materials. This heterostructure is meticulously integrated onto a microfiber knot resonator (MKR), an ultra-fine looped optical fiber with a diameter narrower than a human hair. This configuration significantly intensifies the interaction between light and material, enabling modulation of the refractive index with minimal applied voltage, which shifts the light’s wavelength to encode computational information with remarkable precision and energy economy.

The fabrication process commences with mechanical exfoliation to obtain pristine BP/MoS2 bilayers, followed by a dry transfer technique to position these heterostructures onto the MKR. The MKR’s torsional geometry enhances evanescent field coupling, ensuring that the optical mode overlaps maximally with the active layered materials. To broaden the modulator’s linear response and suppress nonlinear distortions, the researchers incorporated a Ring-Assisted Mach–Zehnder Interferometer (RAMZI) design. This integration extends the operational voltage range, ensuring fidelity and robustness essential for medical-grade AI computations.

The team transcended single-device innovation by architecting a full all-fiber photonic neural network (PNN). By deploying two RAMZI modulators in tandem with a highly sensitive photoreceiver within a sophisticated time-division multiplexing schema, they realized a closed-loop, coherent computing unit capable of executing neural network operations entirely in the optical domain. This milestone substantiates the transition of photonic neural networks from theoretical constructs to viable, deployable platforms for clinical diagnostics.

Clinical performance validation was conducted in collaboration with esteemed hospitals. The PNN demonstrated expert-level diagnostic accuracy in two stringent medical tasks: detecting retinal detachment from B-scan ultrasound imagery and diagnosing hepatocellular carcinoma (HCC) using multiphase liver computed tomography (CT) scans. Diagnostic accuracy and specificity metrics paralleled those of seasoned radiologists, underscoring the system’s potential for integration into frontline healthcare.

Beyond accuracy, the photonic system showcased transformative efficiency advantages. While a conventional NVIDIA A100 GPU requires approximately 85 milliseconds to process a liver CT study, the photonic platform accomplished the same in a mere 0.8 milliseconds. Additionally, the energy expenditure per operation plummeted to an astonishing 0.608 femtojoules, a staggering 246-fold decrease compared to electronic counterparts consuming 150 femtojoules. This synergy of speed and efficiency paves the way for deploying AI diagnostics in resource-constrained or mobile medical environments like rural clinics and ambulances, dramatically expanding access to critical health services.

The system’s environmental implications cannot be overstated. Conventional AI’s sizable energy footprint contributes appreciably to healthcare’s carbon emissions. The BP/MoS2-based photonic platform fundamentally disrupts this model by combining ultrafast processing with near-negligible thermal dissipation, forging a sustainable pathway for green AI implementations in medicine. This breakthrough aligns with expanding global commitments to environmentally responsible technologies without sacrificing computational power.

Acknowledging the infancy of current implementations, the authors outline avenues for scaling. Presently limited to a single processing layer realized through two modulators, the system’s complexity and image resolution capacity necessitate expansion. A promising strategy involves wavelength-division multiplexing (WDM), exploiting the device’s ∼30 nm bandwidth resonances to channel up to 40 distinct wavelengths. This parallelism would amplify computational density approximately 40-fold while maintaining the existing clock rate, facilitating thousands of multiplications per network layer—an enhancement critical to tackling high-dimensional medical image data.

Device longevity and stability pose additional challenges, given the sensitivity of BP to environmental degradation. While the MoS2 layer affords interim encapsulation, the team plans industrial collaboration to pioneer large-scale protective coatings via atomic layer deposition of Al2O3 and scalable chemical vapor deposition processes. These advancements will bolster device durability, uniformity, and manufacturability, accelerating clinical adoption and commercial viability.

The study also signifies the maturation of fiber-based photonic neural networks into practical diagnostic platforms rather than experimental novelties. Optical fibers’ inherently low transmission loss (<0.2 dB/km), synergized with the modulator’s efficient 0.25 V·cm electro-optic response and expanded linear range, addresses critical bottlenecks impeding prior photonic computing endeavors. Consequently, the technology promises to empower future applications in drug discovery, genomics, and real-time clinical imaging with unmatched energy footprints.

The pioneering work led by Professor Han Zhang not only foregrounds interdisciplinary collaboration across physics, materials science, and biomedicine but also sets a precedent for industry-academia partnerships catalyzing the commercialization of ultrafast photonic devices poised to transform healthcare AI. As research progresses towards integrated multi-layer architectures and industrial encapsulation, the healthcare sector stands on the cusp of embracing a new generation of diagnostic tools that are faster, greener, and broadly accessible.

This heralds a paradigm shift where medical AI transcends the limitations inherent to electronic systems, embracing photon-driven computing as a sustainable, high-performance alternative that aligns with the twin imperatives of clinical excellence and environmental stewardship.

Subject of Research: People

Article Title: Tunable phosphorene modulators – accelerating medical diagnosis with ultra-efficient photonic platforms

News Publication Date: 28-May-2026

References:
Zhang, H., Liu, Y., Wang, H., Zhu, H., et al. (2026). Tunable phosphorene modulators – accelerating medical diagnosis with ultra-efficient photonic platforms. Opto-Electronic Advances. DOI: 10.29026/oea.2026.250332

Image Credits: Professor Han Zhang of Shenzhen University

Keywords: photonic computing, black phosphorus, molybdenum disulfide, van der Waals heterostructure, microfiber knot resonator, electro-optic modulator, photonic neural network, medical imaging AI, energy efficiency, wavelength-division multiplexing, ultrafast diagnostics, sustainable healthcare

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Archaeologists believe a site in present-day northern Iraq may preserve the remains of an ancient city whose fall was recorded in texts from ancient Mesopotamia.

While ancient texts described the event, the physical traces were not discovered until recently. Excavations at Kurd Qaburstan in the Kurdistan region have now revealed the clearest archaeological record of Middle Bronze Age siege warfare found in the area.

During the 2024 and 2025 excavation seasons, a team led by Tiffany Earley-Spadoni, associate professor of history at the University of Central Florida and director of the Kurd Qaburstan Project, recovered 20 cuneiform tablets and more than 100 administrative sealings from destruction layers inside the Lower Town East Palace.

A Lost Archive

This collection represents the first substantial group of cuneiform texts found on the Erbil Plain, and their dating is unusually precise. Scribes wrote several tablets within days of one another, clustering around the period recorded as the city’s fall, which allows researchers to closely align the archaeological and historical records.

Epigraphers Paul Delnero and Parker Zane, from Johns Hopkins University and Yale University, are leading the translation effort with art historian Marian Feldman. The texts include palace administrative records and at least one letter that may mention a senior official from the ancient city of Qabra.

Some inscriptions may also correspond to destructions recorded on the Victory Stele of Dadusha, a monument that documents military campaigns in the region from the same period.

Burned Layers

The physical signs of the attack are clear. Collapsed buildings, burned layers, and numerous broken pottery pieces all show signs of a coordinated and sustained assault. The team found two layers of destruction, one above the other, which match the recorded events of Qabra’s conquest.

Within these layers of destruction, bioarchaeologist Andrea Zurek-Ost of Michigan State University identified the remains of 17 people. Several seem to have died where they fell, possibly including palace workers. Archaeologists found one individual face down over a stone basin. Researchers are now using isotopic and ancient DNA analyses to learn more about who these people were, where they came from, and how they might have been related.

Mapping The City Before It Fell

The destruction layers tell the story of the city’s final chapter. A magnetometer survey covering over 80 hectares provides a picture of what the city looked like before its fall.

Led by Andrew Creekmore III at the University of Northern Colorado, the survey detects buried structures by measuring changes in the Earth’s magnetic field. It revealed a monumental wall with bastions surrounding the site, fortifications that match those shown on the Victory Stele of Dadusha and support the identification of Kurd Qaburstan as ancient Qabra.

Researchers also found a preserved street with a drainage system, along with areas used for food processing and textile production. These features indicate that the city was more complex than researchers usually expect in northern Mesopotamia.

Mesopotamian history has often focused on southern cities such as Uruk, viewing the south as the birthplace of urban civilization. The evidence from Kurd Qaburstan challenges that view.

“The evidence from Kurd Qaburstan shows that northern cities could be large, complex, and politically significant, with administrative systems, fortifications, and infrastructure comparable to those of the best-known southern sites,” Earley-Spadoni says.

Findings from the Kurd Qaburstan Project were announced as part of a reports and proceedings release supported by the University of Central Florida and the U.S. National Science Foundation.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

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