Med Lasers 2023; 12(4): 212-219  https://doi.org/10.25289/ML.23.043
Quantum technology: a beacon of light for next-generation healthcare
Andrew Padalhin1, Phil-Sang Chung2,3, Seung Hoon Woo2,3
1Beckman Laser Institute, Cheonan, Republic of Korea
2Medical Laser Research Center, Dankook University, Cheonan, Republic of Korea
3Department of Otorhinolaryngology-Head and Neck Surgery, Dankook University College of Medicine, Cheonan, Republic of Korea
Correspondence to: Seung Hoon Woo
E-mail: lesaby@hanmail.net
ORCID: https://orcid.org/0000-0001-7560-1140
Received: November 21, 2023; Accepted: December 13, 2023; Published online: December 21, 2023.
© Korean Society for Laser Medicine and Surgery. All rights reserved.

This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Quantum theory diverges from classical physics by identifying discrete states instead of a continuous spectrum. While quantum phenomena have evolved independently from biology, the fields are converging, particularly in medical science. Understanding quantum principles is crucial for advancing medical technologies, as evidenced by the current developments outlined in selected articles from the past decade. Despite decades of existence, the full realization of the benefits of quantum physics has yet to be achieved. Recent technological advances, rooted in quantum principles, include quantum computers, artificial intelligence (AI) quantum algorithms, quantum-based lasers, and nanoparticles. Quantum computing, a potential foundation for robust infrastructures, faces challenges, such as the need for highly controlled conditions and specialized algorithms, prompting researchers to explore possibilities and pitfalls. Hence, optimizing existing AI tools and exploring quantum computing possibilities are priorities. Advances in quantum cascade lasers (QCLs) operating at ambient temperatures and producing hyperspectral images are sought for biomedical applications. Potential breakthroughs in infrared microscopy based on QCL technology could enable the submicron resolutions of molecules. The convergence of quantum physics and biology in medical science is in its early stages. Resolving the challenges in practical quantum technology within its fundamental principles is crucial for future progress.
Keywords: Artificial intelligence; Lasers; Quantum dots; Theranostics
INTRODUCTION

Before going into depth with the advancements and future directions of quantum technology and it potential use in the field of medicine, it is crucial to provide a brief background on what is quantum physics and why its used for advancing technology. Being centered on the governing principles of physical laws in structures like an atom’s nucleus or the subatomic particles within it, quantum theory possesses distinctive properties that set it apart from the majority of other physics domains [1-4]. It does not represent variables across a continuous spectrum and instead identifies discrete states that these variables are anticipated to exhibit, making it divergent from classical physics. While classical mechanics one can usually ascertain results and calculations which describes a particle’s motion, quantum mechanics cannot forecast the particle’s position and instead provide a probabilistic standpoint is employed to where a particle could be found [2,4,5]. In quantum theory, Erwin Schrödinger’s wave equation is the basis for establishing specific, isolated energy states by defining a wave function with particular limitations [1]. This process aids in calculating the particular energy states with other specific quantities associated with this wave function. An alternative framework for analysis can then be achieved when combining the core aspects of quantum mechanics and quantum theory generally recognized as quantum physics [1-4].

Having introduced the rudimentary difference between classical physics and quantum physics, it is also important to present some key principles that considerably govern the field of quantum mechanics. The first of these principles is interference which states that at sub atomic levels, particles begin to exhibit wave like properties. Based on the particle’s property associated with its location, it can either contribute to larger wave height or cancel out waves depending on their symmetry and synchronization [1,4,6]. Another principle is superposition which refers to the concept that an electron can be in all location at all times at varied probabilities and that the position of this elementary particle is based on a probability distribution. The principle of entanglement is also crucial for understanding quantum physics. This states that photons or electrons that are closely connected can reveal information about the other based on relative information regardless of their special distance from each other. This phenomenon opposes Albert Einstein’s theory that information cannot travel faster than light [2-4]. Within this ideological framework, information about distant linked elemental particles becomes instantaneous. Lastly, quantum tunneling is the phenomenon in which an elemental particle can pass through a potential energy barrier given the suitable conditions due to the wave nature of matter. With this concept in mind, detectable sub atomic waves can be transmitted through very narrow barriers and thus can be used for various measurements [1-7].

Although application and understanding of these non-classical but observable quantum phenomena have advanced independently from biology, these fields of sciences are now converging towards a similar trajectory for medical science. Thus, it is important to consider the how quantum principles relates to the properties of both biological and non-biological materials to further advance the development of technologies for medical applications. This paper will outline some current developments related to the use of quantum physics in the medical field based on selected articles in the past decade. Fig. 1 outlines the three main aspects of quantum technology that will be discussed concerning medical science.

Figure 1. Quantum technologies such as quantum computers, quantum cascade lasers, and quantum dots can be utilized to increase the efficiency, accuracy, and efficacy of modern medicine. Medical analytics processed through artificial intelligence running on a quantum computer can potentially provide personalized precision medicine in conjunction with data cross-referencing. Quantum cascade lasers can provide power-efficient lasers that can be used for non-thermal theranostic scans. Quantum dots can provide better imaging fluorescent biomedical imaging and increase the efficacy of photon-based cancer therapies.
QUBITS AND ARTIFICIAL INTELLIGENCE IN MEDICINE

Due to its transformative capability to process information, quantum computing has become an emerging interdisciplinary. Compared to conventional computer components found in current devices, the quantum computer employs quantum physical systems that involves sub-atomic particles such as photons and electrons and their characteristics (spin and/or orientation) to create a qubit, the fundamental unit of quantum computing [5,8-14]. With its basic units rooted in sub-atomic elemental particles, quantum computing relies on the aforementioned principles of quantum physics such as superposition, interference, and entanglement [8,14].

Through superposition, qubits can represent the basic binary 0s and 1s and all the values in between under superposition states. Computational capacity of quantum computers increase exponentially by qubits existing in both states allowing it to surpass its classical counterparts [8,11]. On the other hand, possible outcomes of a binary computation are determined based on the interaction of qubits used. Hence, the amount of qubits exponentially increases the possible outcome and this is grounded on the principles of quantum interference where it is utilized to affect probability amplitudes when measuring the energy level of qubits [5,9-11]. In addition, quantum entanglement is essential for the exponential speedup of quantum algorithms compared to classical counterparts. This principle greatly increases efficiency by instantaneously providing information from several qubits entangled qubits by just referencing an individual qubit [5,10]. Although increased number in qubit does not result in higher entanglement, it instead provides another point of observational output which is relative to the qubit being observed. Merging these observable concepts results to what it now known as quantum supremacy which characterizes a device that can perform better than a classical computer both in terms of complexity with shorter timeframe [8,11,12,14].

Since computers are essentially composed of two major components, a quantum computer’s advantage over its current equivalent would be muted without a comparable preforming software and algorithm. This is where artificial intelligence (AI) comes in. Initially computer was only capable of providing output based on the inputs but cannot store information and process it. AI have existed since the mid 1950’s, after the very first AI concept dubbed “Logic Theorist” was initialized by Herbert Simon, Cliff Shaw, and Allen Newell [15-17]. Since then AI has made remarkable advancements, excelling in data analysis, pattern recognition, and prediction accuracy. The current landscape in AI tech has boomed within the past 4 years encompassing the COVID-19 pandemic [17]. AI, especially machine learning and deep learning, has proven effective in processing large datasets, learning, and improving over time. These tools are accessible and practical, operating on widely available classical computers. They provide immediate benefits to businesses and society, solving complex problems. Given the current state of AI models, the necessity for assistance from quantum computers is now in question. Integration of AI with quantum computing is touted as the next breakthrough due to the complexity and efficiency this combination offers. Quantum AI computing essentially circumvents Moore’s law by essentially providing major leaps in compute power in substantially shorter period of technological development [5,11].

One area where the quantum AI computing would excel is in generative modeling [18,19]. Quantum computers currently excel in generating true randomness, a capability not easily achievable in classical computers. Generative modeling is an unsupervised machine learning scheme, can benefit from this randomness. Statistical correlations that are otherwise very difficult to replicate can be created through quantum computers. A use-case for this is for enhancing data portfolio which is currently being implemented for drug discovery [20-23]. Drug development commonly follows a workflow which involves extensive chemical testing, manufacturing optimization, and biological testing even before it enters any level of trials. This step significantly stagnates the pipeline for drug development and considerably increases its inherent cost. Complex simulation can be performed consisting of complex correlations and well-connected structures of molecules with interacting electrons. The computational requirements for simulations and other operations in this domain naturally grow exponentially with the problem size, with time always being the limiting factor [24,25]. Using quantum powered AI generative algorithm and simulations, a library of can essentially be created for reference in conjunction with cutting down on resource intensive phase of drug development [22,23,26]. Thus a quantum-computing-based system is a natural fit for the use case.

Another area where quantum AI can contribute is in the development of precision medicine [27-30]. Precision medicine aims to understand and predict individualized causes and treatments, contrasting with umbrella diagnoses from traditional symptom-based approaches. By leveraging machine learning fed with electronic health records, individualized prevention, maintenance, and treatments can be planned out transcending the conventional symptomatic and diagnostic approach. Current diagnostic methods, like X-rays and computed tomography scans, face challenges with noise, data quality, and replicability [31-33]. Quantum-based AI computing can significantly contribute to these diagnostic tools by improving data quality and cross referencing information in an existing database. Equally, in the realm of single-cell diagnostics, conventional methods face challenges in integrating data from various techniques. Quantum machine learning, proves beneficial in addressing these challenges and facilitating single-cell diagnostic methods [34-39]. In conjunction to meta-analysis for cell-level diagnosis, quantum computing can support continuous monitoring, eliminate repetitive diagnosis and treatment. Significant reduction in healthcare costs and improved prognosis can be achieved through early disease diagnosis. Furthermore, disease discovery and drug inference models can significantly enhance the patient quality of life. Better treatment outcomes can be achieved by promoting patient engagement and adherence to preventative interventions. Both medical practitioners and patients will benefit from these data-driven approaches in terms of cost and efficiency [37-40].

QUANTUM CASCADE LASERS

Aside from the computational power that can be had with quantum computers, quantum physics also provide the next generation of energy-based medical treatments. On this front, a different type of lasers that work around the fundamental principles of quantum physics has already been developed-quantum cascade lasers (QCLs) [41-44]. Unlike conventional lasers that utilized electrons and holes, QCL exclusively uses electrons making it unipolar. A superlattice composed of ultrathin, alternating potential barriers and quantum wells. Electrons are meant to pass through these layers to produce photons. These lasers leverage the phenomena of quantum tunneling and quantum confinement. By allowing electrons to pass through these layers, the energy state of the electron “cascade” down yield photons. Hence, wavelength of this type of laser is not dependent on the material but rather on the design structure of the supper lattice [43,44]. Traditionally, wave lengths used for medical applications ranges from ultra violet (UV), visible and mid-infrared (IR). Quantum cascade lasers are capable of covering mid-IR (3-5 μm) wavelengths and terahertz frequencies (300 GHz-10 THz) making it more ideal due to higher penetration and suitable absorption spectra for biological tissues [41,43-46]. This can be controlled to have a selective action on pathological tissues can provide a minimally invasive option for different types of treatments and diagnosis [47,48].

Quantum cascade lasers can essentially be used for mid-IR spectroscopy, imaging, material characterization, and diagnostics. Compared to conventional lasers, these lasers are capable of higher energy output and non-dispersive output which translates to better sensitivity [49-52]. External cavity QCLs, composed of optical system build around a gain chip, are suitable for spectroscopy due to its broad band output and can even be adapter for FTIR applications [47-53]. Distributed Feedback QCLs on the other hand uses a grading system that can achieve single frequency at single wavelength suitable for absorption spectroscopy gas analysis [51,53]. These types of QCL systems demonstrate versatility of these lasers for spectral analysis which can also be adapted for diagnostic equipment. However, widespread adoption of this technology in medical practice is in its infancy and presents key challenges relating to hardware production, monitoring systems, and cost of commercialization. A concern regarding the use of lasers in medical applications is the issue of thermal effects that can damage non-target tissues [43]. This can potentially be addressed with distributed feedback QCLs with higher pulse energy which can be achieved by setting parameters for low-temperature cavitation fragmentation of the target tissue. However this is currently constrained by existing optics for directing lasers in a clinical setting.

QUANTUM DOTS

A different form of quantum technology making its way to the medical field are in the forms of nanoscale particles called quantum dots (QDs). In 1980’s, Alexei Ekimov documented that these minuscule semiconductor-based particles (1-20 nm) have noteworthy optical properties [54,55]. QDs are commonly composed of a bandgap semiconductor shell surrounding a heavy metal core intended to overcome surface deficiencies and enhance quantum yield [56-58]. These core-shell nano particles exhibit high emission brightness, intermittent light emission, and tunable composition and properties making them ideal components for biomedical applications which include imaging, drug delivery, and cancer therapy [58-63]. QDs employs the concept of quantum confinement phenomenon which enables it to possess high band gap energy stemming from its well separated electro energy levels. Since photon generation is dependent on the design implemented at a sub-atomic scale, electromagnetic radiation across a broad spectrum can be influenced by particle size [58,64]. Aside from being stable from photobleaching, QDs also show substantial separation between excitation and emission spectra which can lead to reduced optical overlap and superior detection sensitivity. These features make QDs excellent fluorescent probes for diverse biomedical imaging applications. Due to their nanoscale size, QDs can be utilized to visualize subcellular components from organelles down to individual proteins [56,57,65-68]. This is particularly useful regarding its potential application as polychromatic labels for fluorescent-activated cell sorting in which a single laser beam can excite different QD probes that emit different wavelengths [64].

Another application in which QDs can be used is photodynamic therapy (PDT). Aside from being able to efficiently absorb and emit stronger, QD-based nanoscale photosensitizers can be readily absorbed by cell due to their size which enhances tissue accumulation [59-63]. Tuning the particle composition an also allow emission of near-IR wavelengths essential for reaching deeper into diseased tissues. Recent studies on graphene QDs [69] and graphene oxide QDs [70] demonstrated high potency and efficiency in killing melanoma and breast cancer cells within 5 minutes under UV irradiation. Additionally, carbon QDs show promise in PDT for COVID-19 through ROS generation and type I interferon response stimulation [71,72].

CONCLUSION

Even though quantum physics have existed for decades, the benefits of its concepts and observable phenomena are yet to be fully realized. Previous advancements our current technology is rooted in understanding its principles. Undoubtedly, quantum physics will eventually become the core basis of the next generation of technology. This is now evident with the dawn of increasing interest on developing quantum computers, AI quantum algorithms, quantum-based lasers, and quantum nanoparticles for various applications. However this does not come without hefty prerequisites. For one, the development of quantum computers is still lagging since it needs highly controlled conditions and super conductors which are predominantly maintained with super cooling. In addition, quantum computers would also need a drastically different set of algorithm for programming since its instruction set should match the exponential computational capacity of qubits. Quantum computing could be the foundation of tomorrow’s robust computing infrastructures, facilitating real-time processing of vast data volumes. As interest in quantum computing grows, researchers seek to elevate computational capabilities beyond the limitations of Moore’s law. However, a comprehensive survey is required to explore the possibilities, pitfalls, and challenges in this evolving field. In the meantime, optimizing existing AI tools and exploring quantum computing possibilities remain key priorities. Aside from developing QCL that can operate at ambient temperatures, production of hyperspectral images is also sought after for biomedical applications. It is conceivable that advancements in IR microscopy based on QCL technology would eventually allow us to visualize submicron resolutions of molecules. To enhance the clinical applicability of QDs, key considerations include adopting scalable and sustainable synthetic methods, transitioning to heavy metals-free QDs for biosafety, achieving a balance between retention and clearance, and establishing standardized protocols. The advancement of medical science through the convergence of quantum physics and biology is still at its very early stages. Hopefully the problems encountered in developing practical quantum technology are resolved within the context of its own fundamental principles.

ACKNOWLEDGMENTS

None.

AUTHOR CONTRIBUTIONS

Conceptualization: SHW. Data curation: AP. Formal analysis: AP. Funding acquisition: SHW, PSC. Investigation: all authors. Project administration: SHW, PSC. Validation: all authors. Visualization: AP. Writing–original draft: all authors. Writing–review & editing: all authors.

CONFLICT OF INTEREST

Seung Hoon Woo is the Editor-in-Chief, Andrew Padalhin and Phil-Sang Chung are editorial board members of the journal, but they were not involved in the review process of this manuscript. Otherwise, there is no conflict of interest to declare.

FUNDING

This work was supported by the Dankook Institute of Medicine & Optics in 2023. Funding was supported by the following grants: Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (RS-2023-00247651 and NRF-2020R1A6A1A 03043283); Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare (HI20C2088); Leading Foreign Research Institute Recruitment Program through NRF funded by the Ministry of Science and ICT (NRF-2023K1A4A3A02057280); and Korea Medical Device Development Fund grant funded by the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety (KMDF_PR_20200901_0027-03), Republic of Korea.

DATA AVAILABILITY

None.

References
  1. Birkhoff GD. Some remarks concerning Schrödinger's wave equation. Proc Natl Acad Sci U S A 1933;19:339-44.
    Pubmed KoreaMed CrossRef
  2. Giovannetti V, Lloyd S, Maccone L. Advances in quantum metrology. Nat Photonics 2011;5:222-9.
    CrossRef
  3. Loudon R, Scully MO. The quantum theory of light. Phys Today 1974;27:48.
    CrossRef
  4. Piron C. Survey of general quantum physics. Found Phys 1972;2:287-314.
    CrossRef
  5. Wu Y, Li T. Toward quantum computers by designing a new nano-scale arithmetic & logic unit. Optik 2022;271:170031.
    CrossRef
  6. Djordjevic IB. Quantum information processing fundamentals. In: Djordjevic IB, editor, Quantum communication, quantum networks, and quantum sensing. Academic Press; 2022. p. 89-124.
    CrossRef
  7. Torday JS. Quantum mechanics predicts evolution. In: Torday JS, editor, Quantum mechanics, cell-cell signaling, and evolution. Academic Press; 2023. p. 75-86.
    CrossRef
  8. Abdullah RM, Basher R, Alwan AA, Abualkishik AZ. Quantum computers for optimization the performance. Proced Comput Sci 2019;160:54-60.
    CrossRef
  9. Chen H, Zhao L. Quantum-dot cellular automata as a potential technology for designing nano-scale computers: exploring the state-of-the-art techniques and suggesting the opportunities for the future. Optik 2022;265:169431.
    CrossRef
  10. Lötstedt E, Yamanouchi K. Comparison of current quantum devices for quantum computing of Heisenberg spin chain dynamics. Chem Phys Lett 2023 Nov 15. [Epub]. https://doi.org/10.1016/j.cplett.2023.140975.
    CrossRef
  11. Möller M, Vuik C. A conceptual framework for quantum accelerated automated design optimization. Microprocess Microsyst 2019;66:67-71.
    CrossRef
  12. Phab L, Louise S, Sirdey R. First attempts at cryptanalyzing a (toy) block cipher by means of quantum optimization approaches. J Comput Sci 2023;69:102004.
    CrossRef
  13. Rennela M, Staton S. Complete positivity and natural representation of quantum computations. Electron Notes Theor Comput Sci 2015;319:369-85.
    CrossRef
  14. Villalba-Diez J, Ordieres-Meré J, González-Marcos A, Larzabal AS. Quantum deep learning for steel industry computer vision quality control. IFAC-PapersOnLine 2022;55:337-42.
    CrossRef
  15. Bringsjord S. The logicist manifesto: at long last let logic-based artificial intelligence become a field unto itself. J Appl Log 2008;6:502-25.
    CrossRef
  16. Pietronudo MC, Croidieu G, Schiavone F. A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management. Technol Forecast Soc Change 2022;182:121828.
    CrossRef
  17. Dobbe R, Krendl Gilbert T, Mintz Y. Hard choices in artificial intelligence. Artif Intell 2021;300:103555.
    CrossRef
  18. Wang Z, She Q, Smeaton AF, Ward TE, Healy G. Synthetic-Neuroscore: using a neuro-AI interface for evaluating generative adversarial networks. Neurocomputing 2020;405:26-36.
    CrossRef
  19. Zhu H, Dai E, Liu H, Wang S. Learning fair models without sensitive attributes: a generative approach. Neurocomputing 2023;561:126841.
    CrossRef
  20. Aggarwal L, Sachdeva S, Goswami P. Quantum healthcare computing using precision based granular approach. Appl Soft Comput 2023;144:110458.
    CrossRef
  21. Li RY, Gujja S, Bajaj SR, Gamel OE, Cilfone N, Gulcher JR, et al. Quantum processor-inspired machine learning in the biomedical sciences. Patterns 2021;2:100246.
    Pubmed KoreaMed CrossRef
  22. Prachalith NC, Vibha K, Shilpa KG, Ravikantha MN, Annoji Reddy R, Thipperudrappa J, et al. Quantum computations of non-steroidal anti-inflammatory drug molecules using Density Functional Theory. Chem Phys Impact 2023;7:100317.
    CrossRef
  23. Raajaraman BR, Sheela NR, Muthu S. Spectroscopic, quantum computational and molecular docking studies on 1-phenylcyclopentane carboxylic acid. Comput Biol Chem 2019;82:44-56.
    Pubmed CrossRef
  24. Chen Q, Luo H, Zhang C, Chen YPP. Bioinformatics in protein kinases regulatory network and drug discovery. Math Biosci 2015;262:147-56.
    Pubmed CrossRef
  25. Irfan A, Imran M, Khalid N, Hussain R, Basra MAR, Khaliq T, et al. Isolation of phytochemicals from Malva neglecta Wallr and their quantum chemical, molecular docking exploration as active drugs against COVID-19. J Saudi Chem Soc 2021;25:101358.
    KoreaMed CrossRef
  26. Schroedl S. Current methods and challenges for deep learning in drug discovery. Drug Discov Today: Technol 2019;32-33:9-17.
    Pubmed CrossRef
  27. Gruson D, Bernardini S, Dabla PK, Gouget B, Stankovic S. Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine. Clin Chim Acta 2020;509:67-71.
    Pubmed CrossRef
  28. Meroueh C, Chen ZE. Artificial intelligence in anatomical pathology: building a strong foundation for precision medicine. Hum Pathol 2023;132:31-8.
    Pubmed CrossRef
  29. Singh S, Sarma DK, Verma V, Nagpal R, Kumar M. Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders. Biochem Biophys Res Commun 2023;682:1-20.
    Pubmed CrossRef
  30. Waisberg E, Ong J, Kamran SA, Paladugu P, Zaman N, Lee AG, et al. Transfer learning as an AI-based solution to address limited datasets in space medicine. Life Sci Space Res (Amst) 2023;36:36-8.
    Pubmed CrossRef
  31. Ploug T, Holm S. The four dimensions of contestable AI diagnostics - a patient-centric approach to explainable AI. Artif Intell Med 2020;107:101901.
    Pubmed CrossRef
  32. Formosa P, Rogers W, Griep Y, Bankins S, Richards D. Medical AI and human dignity: contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts. Comput Hum Behav 2022;133:107296.
    CrossRef
  33. Mukhmetov O, Zhao Y, Mashekova A, Zarikas V, Ng EYK, Aidossov N. Physics-informed neural network for fast prediction of temperature distributions in cancerous breasts as a potential efficient portable AI-based diagnostic tool. Comput Methods Programs Biomed 2023;242:107834.
    Pubmed CrossRef
  34. Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Cardiovascular events prediction using artificial intelligence models and heart rate variability. Proced Comput Sci 2022;203:231-8.
    CrossRef
  35. Sood SK, Rawat KS, Kumar D. A visual review of artificial intelligence and Industry 4.0 in healthcare. Comput Electr Eng 2022;101:107948.
    Pubmed KoreaMed CrossRef
  36. Sujan MA, White S, Habli I, Reynolds N. Stakeholder perceptions of the safety and assurance of artificial intelligence in healthcare. Saf Sci 2022;155:105870.
    CrossRef
  37. Voets MM, Veltman J, Slump CH, Siesling S, Koffijberg H. Systematic review of health economic evaluations focused on artificial intelligence in healthcare: the tortoise and the cheetah. Value Health 2022;25:340-9.
    Pubmed CrossRef
  38. Wang JW, Williams M. Registries, databases and repositories for developing artificial intelligence in cancer care. Clin Oncol (R Coll Radiol) 2022;34:e97-103.
    Pubmed CrossRef
  39. Yang Z, Silcox C, Sendak M, Rose S, Rehkopf D, Phillips R, et al. Advancing primary care with Artificial Intelligence and Machine Learning. Healthc (Amst) 2022;10:100594.
    Pubmed CrossRef
  40. Young AS. AI in healthcare startups and special challenges. Intell-Base Med 2022;6:100050.
    CrossRef
  41. Abramov PI, Budarin AS, Kuznetsov EV, Skvortsov LA. Quantum-cascade lasers in atmospheric optical communication lines: challenges and prospects (review). J Appl Spectrosc 2020;87:579-600.
    CrossRef
  42. Pilling MJ, Henderson A, Gardner P. Quantum cascade laser spectral histopathology: breast cancer diagnostics using high throughput chemical imaging. Anal Chem 2017;89:7348-55.
    Pubmed CrossRef
  43. Razeghi M, Lu QY, Bandyopadhyay N, Zhou W, Heydari D, Bai Y, et al. Quantum cascade lasers: from tool to product. Opt Express 2015;23:8462-75.
    Pubmed CrossRef
  44. Schwaighofer A, Brandstetter M, Lendl B. Quantum cascade lasers (QCLs) in biomedical spectroscopy. Chem Soc Rev 2017;46:5903-24.
    Pubmed CrossRef
  45. Isensee K, Kröger-Lui N, Petrich W. Biomedical applications of mid-infrared quantum cascade lasers - a review. Analyst 2018;143:5888-911.
    Pubmed CrossRef
  46. Schwaighofer A, Montemurro M, Freitag S, Kristament C, Culzoni MJ, Lendl B. Beyond Fourier transform infrared spectroscopy: external cavity quantum cascade laser-based mid-infrared transmission spectroscopy of proteins in the amide I and amide II region. Anal Chem 2018;90:7072-9.
    Pubmed CrossRef
  47. Hu L, Ma YC, Xu Y, Wang WT, Ma YW, Liu K, et al. Simulation of molecular spectroscopy with circuit quantum electrodynamics. Sci Bull (Beijing) 2018;63:293-9.
    Pubmed CrossRef
  48. Kira M, Roumpos G, Cundiff ST. Quantum-light shaping and quantum spectroscopy in semiconductors. In: Cundiff ST, Kira M, editors, Semiconductors and semimetals. Elsevier; 2020. p. 417-60.
    CrossRef
  49. Sjolander TF, Blanchard JW, Budker D, Pines A. Two-dimensional single- and multiple-quantum correlation spectroscopy in zero-field nuclear magnetic resonance. J Magn Reson 2020;318:106781.
    Pubmed CrossRef
  50. Surendranath A, Mohanan PV. Solvothermal exfoliation assisted synthesis of transition metal dichalcogenide based tungsten disulphide quantum dots (WS2 QDs) and cellular QD-bio interaction in LN-229 human glioblastoma cells. Mater Sci Eng:. B 2022;284:115907.
    CrossRef
  51. Vargas-Vargas AD, Mejorada-Sánchez JL, Castellanos-Durán FR, Vargas E, Isidro-Ojeda MA, Cedeño E, et al. Dual beam transient thermal lens spectroscopy with high repetition pulsed IR-Laser Excitation: photothermal and fluorescence quantum yields determination. Infrared Phys Technol 2023;129:104561.
    CrossRef
  52. Wang L, Mu P, Zhou Z, Zhang X, Liu D, Liang Y, et al. Ultrafast spectroscopy of size-dependent hole-transfer-mediated triplet energy transfer in a quantum dot-molecule complex. Results Phys 2023;55:107150.
    CrossRef
  53. Wittkop M, Marmolejo-Tejada JM, Mosquera MA. Multichromatic quantum superpositions in entangled two-photon absorption spectroscopy. Org Electron 2023;120:106858.
    CrossRef
  54. Manna L. The bright and enlightening science of quantum dots. Nano Lett 2023;23:9673-6.
    Pubmed KoreaMed CrossRef
  55. Singh UB, Singh D, Kumar S, Dhar R, Pandey MB. The optical properties of quantum dots in anisotropic media. J Mol Liq 2017;241:1009-12.
    CrossRef
  56. Cheng B, Cao L, Li C, Huo FY, Meng QF, Tong G, et al. Fluorine-doped carbon quantum dots with deep-red emission for hypochlorite determination and cancer cell imaging. Chin Chem Lett 2023 Aug 27. [Epub]. https://doi.org/10.1016/j.cclet. 2023.108969.
    CrossRef
  57. He YF, Cheng K, Zhong ZT, Hou XL, An CZ, Zhang J, et al. Carbon quantum dot fluorescent probe for labeling and imaging of stellate cell on liver frozen section below freezing point. Anal Chim Acta 2023;1260:341210.
    Pubmed CrossRef
  58. Agarwal K, Rai H, Mondal S. Quantum dots: an overview of synthesis, properties, and applications. Mater Res Express 2023;10:062001.
    CrossRef
  59. Celikbas E, Saymaz A, Gunduz H, Koc I, Cakir E, Sennaroglu A, et al. Image-guided enhanced PDT/PTT combination therapy using brominated hemicyanine-loaded folate receptor-targeting Ag2S quantum dots. Bioconjug Chem 2023;34:880-92.
    Pubmed KoreaMed CrossRef
  60. Ma M, Wu X, Liang Q, Yu F, Xu J, Li Z, et al. Aptamer-modified carbon dots for enhancement of photodynamic therapy of cancer cells. Talanta Open 2022;6:100161.
    CrossRef
  61. Magaela NB, Matshitse R, Nyokong T. The effect of charge on Zn tetra morpholine porphyrin conjugated to folic acid-nitrogen doped graphene quantum dots for photodynamic therapy studies. Photodiagnosis Photodyn Ther 2022;39:102898.
    Pubmed CrossRef
  62. Nene LC, Managa M, Nyokong T. Photo-physicochemical properties and in vitro photodynamic therapy activity of morpholine-substituted Zinc(II)-Phthalocyanines π-π stacked on biotinylated graphene quantum dots. Dyes Pigments 2019;165:488-98.
    CrossRef
  63. Wen F, Li P, Meng H, Yan H, Huang X, Cui H, et al. Nitrogen-doped carbon dots/curcumin nanocomposite for combined Photodynamic/photothermal dual-mode antibacterial therapy. Photodiagnosis Photodyn Ther 2022;39:103033.
    Pubmed CrossRef
  64. Brkić S. Optical properties of quantum dots. Eur Int J Sci Technol 2016;5:98-107.
  65. Antoine C, Sahylí Ortega Pijeira M, Ricci-Junior E, Magalhães Rebelo Alencar L, Santos-Oliveira R. Graphene quantum dots as bimodal imaging agent for X-ray and Computed Tomography. Eur J Pharm Biopharm 2022;179:74-8.
    Pubmed CrossRef
  66. Ncapayi V, Ninan N, Lebepe TC, Parani S, Girija AR, Bright R, et al. Photostable fluorescent Near-infrared colloidal Mn-doped CuInSe/ZnS quantum dots for cancer-bacteria dual imaging. J Lumin 2023;258:119785.
    CrossRef
  67. Yao F, Wang ZG, Liu SL, Wang H, Zhu J, He R, et al. Purified fluorescent nanohybrids based on quantum dot-HER2-antibody for breast tumor target imaging. Talanta 2023;260:124560.
    Pubmed CrossRef
  68. Zhu P, Liu Y, Tang Y, Zhu S, Liu X, Yin L, et al. Bi-doped carbon quantum dots functionalized liposomes with fluorescence visualization imaging for tumor diagnosis and treatment. Chin Chem Lett 2023 Jun 15. [Epub]. https://doi.org/10.1016/j.cclet.2023.108689.
    CrossRef
  69. Ahirwar S, Mallick S, Bahadur D. Photodynamic therapy using graphene quantum dot derivatives. J Solid State Chem 2020;282:121107.
    CrossRef
  70. Cui G, Wu J, Lin J, Liu W, Chen P, Yu M, et al. Graphene-based nanomaterials for breast cancer treatment: promising therapeutic strategies. J Nanobiotechnology 2021;19:211.
    Pubmed KoreaMed CrossRef
  71. Łoczechin A, Séron K, Barras A, Giovanelli E, Belouzard S, Chen YT, et al. Functional carbon quantum dots as medical countermeasures to human coronavirus. ACS Appl Mater Interfaces 2019;11:42964-74.
    Pubmed KoreaMed CrossRef
  72. Soltan AAS, Kouchaki S, Zhu T, Kiyasseh D, Taylor T, Hussain ZB, et al. Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test. Lancet Digit Health 2021;3:e78-87.
    Pubmed CrossRef


This Article


Cited By Articles
  • CrossRef (0)
  • Download (165)

Author ORCID Information

Funding Information

Services

Social Network Service

e-submission

Archives