Natalia Sniadanko

Healthcare is profoundly transforming as new technologies and business models reshape delivering care. Digitalization is reaching all spheres, and healthcare is the industry that benefits from introducing modern technologies. Not to mention that mobile technologies fuel how patient-doctor interactions can be shaped nowadays. But how does one proceed with healthcare application software development and execute it flawlessly to benefit the final audience?

The Discovery Phase in software development is an initial collaboration step that paves the way for successful project execution. Often overlooked or undervalued, this phase is crucial for understanding the project's scope, requirements, risks, and feasibility. The Discovery Phase sets the stage for a structured, efficient, and effective development process, allowing the alignment of business goals with the user's needs for the final product.

Big data analytics are the top-notch of every industry today. The healthcare market is applying big data apps significantly faster than other industries. Big data analytics for the healthcare market bring numerous merits for physicians and patients. Big data analytics for the healthcare industry have vast and visible potential, making medical help better planned, preventative, personalized, and affordable. Innovative technologies with data analytics allow healthcare workers to forecast and avert infection outbreaks, decrease death rates, cut personal expenses, and improve patient outcomes.

In-house vs outsourcing software development has always been a big question for businesses. There are many pros and cons to each. Whether your business has stood the test of time and you’re looking for upgraded software development, or your organization is new on its journey, and you need a solution, we’re going to break down the benefits and differences of in-house vs outsourcing your software development.

Computer vision (CV) creates ML-based models applied for medical assistance in the prescription of medication, and identification, monitoring or the development of specific illnesses. Computer vision helps physicians to complete daily work, free up physician’s time, prevent avoidable procedures and diagnostics, which allows doctors to concentrate on difficult cases in healthcare. It helps to boost strategies of better medical help and give more attention to each case. The implementation of computer vision software based on medical imaging or predictive analytics offers several advantages to the healthcare market.

As the world progressively shifts digital, Artificial Intelligence (AI) has significantly altered the way humans work for the better.

Everyone is talking about big data analytics these days, making it the hottest buzzword in town. But why is that?

Health systems began developing remote monitoring services, particularly to control chronic conditions, long before the onslaught of the COVID-19 pandemic. Virtual hospitals, or remote, hospital-at-home projects, are a powerful representation of this effort.

MVP development costs a lot because every software is unique, mostly built from scratch, and it brings a comparative advantage for businesses. The most crucial question for startups is the possibility of reducing MVP development costs. The first step you take in this direction is launching a minimum viable product (MVP) instead of full-fledged product development. When you build an MVP first, it allows you to test your business idea and minimize the risk of startup failure.

In 2021, we saw a revolution in the digital space as businesses continued to move their services online. This change has opened many new doors of opportunities for businesses in the digital market space. We see retail, automobile, and insurance industries quickly adopting solutions that offer automation and AI. The healthcare sector is no exception.

The coronavirus pandemic has accelerated the development of biotechnology and the entire medical field. Big data helps to research more efficiently, to diagnose more quickly, and to treat more effectively. Machine learning and data analysis in medicine allow you to create data warehouses and services, update and optimize the infrastructure of registries, and engage in advanced research in evidence-based medicine, pharmaceuticals, and pharmacology. Still, some things could be improved with big data in medicine.

What is computer vision in healthcare? Different industries, including medical care, widely use Artificial intelligence (AI) and machine learning (ML) tools. One of the essential characteristics of AI-powered technologies is the possibility to identify, understand, and maintain visual data. This technology is called Computer Vision (CV).The essential goal of Computer Vision in healthcare is to enable computers and systems to pull out valuable information from digital assets like photos, videos, and other data — and to act on or make recommendations based on that data.