AI in Healthcare: Use Cases and Increased Adoption of Custom Software Solution

AI in Healthcare: Use Cases and Increased Adoption of Custom Software Solution 

Among all the new-age technologies, artificial intelligence has significantly grown, solving the problems of the modern world over the past few decades. But only a few can understand that AI poses in various forms that impact our lives daily. One such central area of the tech's proliferation is AI in healthcare, especially in treatment management and diagnostics. Yes, there's always a fear that AI will surpass human tasks and abilities in the future. Still, research highlights its positive influence in increasing treatment efficiency, supporting human judgment, and fast-tracking clinical decisions. 

AI’s transformative influence in modernizing global healthcare 

The possibilities of AI in healthcare are numerous. It allows medical practitioners and doctors to access hundreds of diagnostic resources through web databases. Doctors are profoundly educated in their fields and well-aligned with the latest research, and at this point, AI increases quicker outcomes that match their knowledge in clinical sciences. 

A patient often presents multiple symptoms related to various physical characteristics and genetic conditions, resulting in delayed diagnosis. Let’s look into how AI has changed the healthcare landscape. 

• AI benefits the practitioner by efficiently providing the physician with qualitative and quantitative data based on input feedback. This process improves the accuracy of diagnosis, early detection, outcome prediction, and treatment plans.
• AI's ability to' learn' from data allows it to improve its accuracy based on feedback responses. The feedback includes various input and database sources from research institutions, doctors, and practitioners.
• AI systems in healthcare work in real-time, meaning the data is consistently updated, increasing its relevance and accuracy.
• AI's ability to' learn' from the data allows it to improve its accuracy based on feedback responses. The feedback responses include input from research institutes, doctors, practitioners, and back-end database resources. AI in healthcare works in real-time, which hints at its continuous updates striving to achieve relevancy and better accuracy.
• The assembled data consists of various demographics, physical exams, lab images, electronic recordings from multiple notes, and numerous medical notes. This compilation of real-time updated information can provide medical practitioners with endless resources for improving their treatment scopes.
• AI is poised to empower the healthcare workforce unprecedentedly, with far-reaching implications for patient care and outcomes. This shift towards a more data-driven approach will require a new skillset from healthcare professionals, but it also promises to revolutionize how we understand and treat diseases. 

AI is ready to empower the global healthcare workforce. As AI-powered applications become more sophisticated, they will significantly impact patient care worldwide. The sector's delivery will shift substantially, emphasizing early intervention and preventive care. This shift will call for a more dedicated skillset from the future healthcare workforce, who will be expected to understand data and data analytics just to be comfortable, confident, and accurate in working with AI-powered applications. 

The influence of AI on healthcare is expected to be wide-ranging. It is imperative to start now with preparations for the impending transformation. Healthcare organizations must focus on figuring out how AI can make healthcare systems more efficient with improved patient care. 

Leveraging AI for targeted diagnostics 

With ample healthcare data, AI could sort through it and build a network. In the vast healthcare arena, there are two types of data: structured and unstructured. Structured learning involves data that is highly organized and easily searchable, while unstructured learning involves data that is not easily searchable, such as text or images. 

Structured learning includes: 

• Modern deep learning 

Deep learning is similar to Machine Learning, but the data is fed into a computerized neural network—an encrypted layer that files the information further for a more simplified output. This enables medical practitioners to quickly narrow probabilities from various possible diagnoses with a more concrete or definite conclusion. 

• Neural network system 

A neural network is also a type of machine learning but is set within a clinical setting. Also known as supervised learning, it uses the information on the patient's physical traits supported by database information. In cancer gene studies, supervised learning can be used to analyze large datasets of genetic information and identify patterns that may indicate a predisposition to certain types of cancer. 

• Machine learning 

Machine Learning leverages analytical algorithms to extract all information about a patient when they visit the practitioner. This information includes lab test results, genetic expressions, diagnostic imaging results, disease-specific data, vital metrics, medications, symptoms, and physical examination results. With Machine Learning, patient outcomes can be accurately determined. 

Unstructured learning includes: 

• Natural language processing 

Natural Language Processing is identical to structured data processing. In the medical field, it focuses on all kinds of unstructured analysis within a clinical setting. The data is fetched from documented text-to-speech processing and clinical notes when the practitioner sees the patient. The data includes insights from medical examination results, lab reports, and physical examination results. 

NLP uses historical databases with disease-relevant keywords to aid in faster decision-making. Medical practitioners can use these processes to allow medical practitioners to provide more accurate and efficient patient diagnoses, which speeds up the entire treatment process. The more targeted and faster the process, the earlier the patient recovers. 

AI pinpointing impending health risks – key applications 

Cancer, neurological disorders, and cardiovascular diseases have consistently been claimed as the top reasons for death. Implementing AI in the field aids in early analysis, detection, diagnosis, and treatment. Let's have a detailed look at it: 

• Detecting early signs of stroke 

In a study on patients at risk for stroke, AI algorithms were used based on their genetic history and presented symptoms. This helped them to be placed in an early stage of detection. This stage was based on movement—any abnormal activity in the patient would trigger an alert. This allowed the practitioners to get patients quick CT/MRI imaging for early disease evaluation. 

Early detection in the study yielded 87.6% accuracy in prognosis and diagnosis evaluation. This helped practitioners implement faster treatments and predict whether the patient had a future chance of reencountering a stroke. 

• Forecasting kidney injuries 

The Department of Veterans Affairs and DeepMind Health created an AI tool in 2019 to predict acute kidney injury up to two days earlier than traditional processes. This AI tool, which uses a combination of machine learning and data analysis, can identify patterns in patient data that may indicate the onset of acute kidney injury, enabling healthcare practitioners to intervene earlier and potentially prevent the condition from worsening. 

• Cancer treatment and research 

  AI has significantly contributed to the research and treatment of cancer radiation therapy – which generally lacks a digital database. Oncora Medical built a platform that assisted clinicians in making well-informed cancer radiation decisions. This platform: 

  • Optimizes treatments
  • Evaluates care and quality
  • Collects medical data of patients
  • Provides information on imaging and treatment outcomes
     

• Predictive analytics 

A popular healthcare technology company, CloudMedX, built an AI tool that turned electronic health records into innovative tools. This tool helped clinicians make more effective and accurate decisions before diseases became fatal. The tool also helped doctors analyze patients' medical histories and connect the symptoms of chronic conditions and diseases in the family. 

• Remote monitoring 

AI-powered remote monitoring tools enable patients to monitor their vitals, such as heart rate and glucose levels, right from the comfort of their homes. The systems are built on the core functions of early detection of health issues, improved patient engagement, and reduced in-person physician visits. Some systems provide alerts to healthcare providers during real-time incidents. 

Proquantic Software built a cloud-based solution in patient monitoring with the help of cutting-edge technologies that were in tune with the customer's vision of a solution that carried out: 

• Remote patient monitoring
• Annual wellness visit
• Chronic care management 

The client registered a whopping 35% increase in sales with a 50% increase in product queries. 

You can learn more about our Remote Patient Monitoring Software Development here.

Ethical considerations and current limitations 

Despite the numerous benefits, AI in healthcare is still embroiled in several challenges, and various ethical considerations have to be addressed before the technology is implemented successfully. 

The World Health Organization released a detailed report called 'Ethics and Governance of Artificial Intelligence for Health,' which identifies the crucial challenging areas and principles and highlights that AI has to put human rights and ethics at the core of design, use, and deployment. 

AI depends on a vast pool of patient data that ensures confidentiality and security. Healthcare providers must navigate strict regulations like HIPAA to protect these data while using these technologies. 

Another significant challenge is that AI algorithms can produce biased data if the database is not diversified. This can create healthcare disparities, hampering accurate treatments. To provide equitable care, it is crucial to ensure that datasets are diversely trained and free from any inherent biases. 

The future contours of AI in healthcare 

As AI applications become more advanced, we can expect a significant shift in healthcare from reactive to proactive care, including early intervention. This transformative shift has the potential to revolutionize patient care, leading to better outcomes and a more efficient healthcare system, inspiring hope for the future of medicine. 

AI's potential is limitless. Instead of a one-size-fits-all approach, AI will empower healthcare practitioners to provide tailored care to each patient. This reassures the audience about the personalization of healthcare, resulting in a more efficient healthcare system and better outcomes.

Proquantic's commitment to revolutionizing global healthcare 

With more than 10 years of hands-on experience, we at Proquantic know how to transform AI in healthcare to drive value for researchers, patients, doctors, and other healthcare practitioners. Our team of thinkers and developers is well-versed in various AI subsets that rely on standard industry practices for conceptualizing and building robust AI solutions that revolutionize medical research, care, and delivery securely, ethically, and with unparalleled patient safety. 

Our USPs in AI solutions in healthcare: 

Data-powered innovation: Harnessing vast healthcare data to fuel AI models, enabling treatments, and predictive analytics. 

Ethical development of AI solutions: They prioritize ethical standards, ensuring transparency, fairness, and bias-free algorithms. 

Human-AI collaboration: By integrating clinical expertise with our AI tools, we enable healthcare professionals to work comfortably and confidently with AI systems. 

Regulatory compliance: We adhere to our focus on uncompromised compliance and quality. Our solutions comply with relevant standards like DICOM, HITECH, GDPR, FDA, and, of course, HIPAA. 

Our AI solutions in healthcare 

Biotech and life science labs:

We assist you by enabling you with data that boosts your R&D and lab effectiveness and drives digitization through conceptualizing commercially viable products.

Wellness & Medtech startups:

We can build apps, remote patient monitoring devices, and other AI-powered solutions to empower you to win the market.

Clinics and hospitals:

We will help you eliminate inefficiencies by automating workflows and strengthening in-patient care with value-driven AI applications for tailored treatment and accurate diagnosis.

R&D and Pharmaceutical companies:

We facilitate advanced clinical trials, medical research, and other solutions that help vaccine and medicine development.

Conclusion 

Proquantic Software, a leading and trusted software development company, aims to deliver professional AI solutions that enhance efficiency, streamline processes, and boost business growth. We offer a broad range of AI development solutions that enable enterprises to upscale their infrastructure and offerings. If you have an idea and a vision, let us know. Your hunt for a dedicated AI development company ends with us.