Background Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. Objective This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. Methods A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. Results Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. Conclusions Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

The peripheral dose outside the applicators in electron beams was studied using a Varian 21 EX linear accelerator. To measure the peripheral dose profiles and point doses for the applicator, a solid water phantom was used with calibrated Kodak TL films. Peak dose spot was observed in the 4 MeV beam outside the applicator. The peripheral dose peak was very small in the 6 MeV beam and was ignorable at higher energies. Using the 10 x 10 cm(2) cutout and applicator, the dose peak for the 4 MeV beam was about 12 cm away from the field central beam axis (CAX) and the peripheral dose profiles did not change with depths measured at 0.2, 0.5 and 1 cm. The peripheral doses and profiles were further measured by varying the angle of obliquity, cutout and applicator size for the 4 MeV beam. The local peak dose was increased with about 3% per degree angle of obliquity, and was about 1% of the prescribed dose (angle of obliquity equals zero) at 1 cm depth in the phantom using the 10 x 10 cm(2) cutout and applicator. The peak dose position was also shifted 7 mm towards the CAX when the angle of obliquity was increased from 0 to 15 degrees.

In this topical review, we will explore and challenge how artificial intelligence (AI) and mathematical modeling apply towards the future in medical applications, focusing on their interactions with gold nanotechnology. There have been rapid advancements towards the applications of AI and mathematical modeling in medical biophysics. These specific techniques help to improve studies related to nanoscale technology. Many works have been published in relation to this topic; it is now time to collectively analyze and review them to assess the contributions these applications made within nanotechnology. Through this review, both theoretical and clinical data is examined for a fresh and present-day understanding. Observations of set parameters and defined equations through AI and mathematical modeling are made to help give explanation towards variable interaction. This review focuses on gold nanoparticle synthesis and preparation via the Turkevich and Brust and Schiffrins one-pot method. From this, findings show that gold nanoparticle size, shape, and overall functionality affect its synthetic properties. Depending on the characteristics within the gold nanoparticle, its ability to maximize light absorbency, wavelengths, and optical densities within the particle is limited. Finding an ideal wavelength (dependent on nanoparticle sizing) allows for higher absorbency of light within the nanoparticle itself. Examining the cellular uptake and cytotoxicity within the nanoparticle is done so via transmission electron microscope (TEM) and Fourier transform infrared radiation (FT-IR) spectroscopy. By manipulating AI and stochastic and diagnostic models, nanoparticle efficiency within precision cancer therapy is set to ensure maximal treatment. Set conditions allow ideal tumor treatment planning, where manipulated nano-probes are used in gold nanoparticle-based therapy. Versatility in nanoparticle sensors allow for multimodal imaging and assistance towards further diagnostic and therapeutic imaging practices. Drawn conclusions will help expand further knowledge and growth for future gold nanoparticle technology research in medical biophysics application using AI and mathematical modeling.