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Future of AI in the Mobile App Development Industry

The app development industry is continuously evolving. From adopting technologies such as Blockchain to finding ways of making services more efficient through AI, the industry has been improving app quality. In the app development industry, AI is getting commonly known for chatbots, healthcare apps, IoT apps, and more. The goal is to make them respond better, have increased cybersecurity, and more. 

Per a Statista report, the AI software market is expected to touch an approximate global revenue of $125 billion by 2025. From machine learning usages to automation tasks, the expansion of AI is expected to grow into more industries.

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5 Optimistic Ways That Artificial Intelligence Is Revolutionizing Mental Healthcare

Indeed, William Gibson has very well stated, “The future is already here, the fact is it’s not just very evenly distributed.”

Revolutionary artificial intelligence algorithms are creeping into mental healthcare and are reshaping its dimensions. You might already be discussing with an AI bot right now the question “how does it make you feel to hear that?” Your AI therapist might be successful enough to take you out from the feeling of worry about what direction the future will take with the advent of artificial intelligence. Looking beyond the horrifying headlines of Skynet coming true, the progressive utilization of artificial intelligence in mental healthcare is absolutely splendid news for many of us. 

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How To Measure the Success of a Conversational AI Chatbot

Illustration by Quovantis

Last year, when one of our healthcare partners (we refer to our clients as partners) were looking to build a conversational AI chatbot, I was apprehensive about guiding them. I had only worked on the level 2 (out of the 5 levels of conversational AI) type of bots. But this time our partner wanted to build a contextual/consultative AI-powered chatbot assistant.

I was concerned about how the bot would understand end-users’ problems. What features can we build to make it more humanistic? Would it be successful in replacing human care and compassion? Would it replicate the same emotions of empathy, compassion, and care?

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How Is Artificial Intelligence Impacting Healthcare?

With the advancement in technology, especially AI, its positive impact has been discussed widely and is still creating a buzz as questions are always raised about its impact on several sectors, including healthcare as well. As per the statement of the American Hospital Association, one can conclude the point that the involvement of AI in healthcare is supporting improvement in healthcare services. If we say clearly, then AI has gained a pride place in healthcare sectors in major countries such as Finland, Germany, Israel, China, the UK, the United States, etc. Many more countries are also investing heavily to come up with better AI healthcare systems, and the reason behind this approach is a multitude of impacts. Let’s discuss in brief about AI healthcare systems-

What’s Making the AI Growth Possible in Healthcare?

Today, artificial intelligence is a popular topic to discuss as this technology has several magnitudes in healthcare. From early disease detection to better diagnosis, treatment plan to outcome prediction, the use of AI has increased to a great extent and also replacing the human doctors and practitioners. 

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3 Ways Healthcare Apps Make Use of Machine Learning

The healthcare industry has generated plenty of data. The new method of data collection, such as sensor-generated data, has helped this industry to find a spot in the top.

What if this data can be used to provide better healthcare services at lower costs and increase patient satisfaction? Yes, you heard it right. It’s actually possible by applying machine learning (ML) techniques in the healthcare industry.

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Artificial Intelligence to Transform Healthcare as We Know It

The 20th century has a new dictum: everything that can be automated will be automated. Artificial intelligence’s (AI) unstoppable power is reverberating across all industries. However, in healthcare, it can be truly life-changing. Technological experts promised that AI and machine learning would transform the healthcare industry with novel applications that could streamline workforce, reduce human error, improve drug recovery, and find new, effective drugs.

The concept of AI has been around floating since 1956, but it has made a significant improvement in the last decade. From drug development to clinical research and insurance, AI applications are disrupting the way the health sector works to improve patient outcomes and reduce patients’ bills. The total public and private investments in AI in the healthcare industry are absolutely stunning. According to Allied Market Research, the artificial intelligence in the healthcare market is projected to garner $22.79 billion by 2023 with CAGR of 48.7% from 2017 to 2023.

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Would You Trust an Automated Doctor?

You're in the park going for a run, and your wearable device is tracking your performance, your heart rate, and various other aspects of your physical health. Pooling this data over a period of time gives you a strong idea about your physical fitness. Combine that data with your diet, your genetic data, and your electronic medical records, and you can paint a comprehensive picture of your physical health.

Making sense of this data, together with any symptoms you volunteer, is increasingly the preserve of autonomous technology that can absorb vast quantities of data at a time when doctors report inputting data into electronic medical records as a key source of stress. Would you trust the diagnoses of such autonomous systems or would you prefer to have a human doctor have the ultimate say in the recommendations you receive?

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Using AI to Predict Breast Cancer Five Years Out

One of the most common applications of AI in healthcare has been in analyzing medical images to provide earlier diagnoses of various conditions. The latest project of this kind has recently emerged from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), who have developed a deep learning model that can predict the likelihood of breast cancer up to five years into the future.

The system was trained using mammograms from over 60,000 patients for whom the outcome was known. The idea was to train the system so that it could spot the subtle patterns in breast tissue that ultimately lead to tumors emerging.

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3 Industries Where AI Is Supercharging the Customer Experience

Artificial Intelligence (AI) has the potential to revolutionize almost every industry, and it’s already seeping its way into most. The technology allows people to work differently and more effectively, providing a better result for the consumer. Another result of AI implementation is an overall better customer experience.

AI is already dramatically changing industries such as manufacturing, retail, and finance. The customer experience, which most have probably realized, is improving. Other sectors are slower to change, but the AI implications are still there. Here’s a look at how artificial intelligence is already improving the customer experience across different verticals and which industry will be next.

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Can AI Help Identify Alcohol Abuse?

I've written before about some interesting applications of big data and AI to predict which patients might progress to substance abuse, with a team from the University of Colorado developing technology to predict potential opioid abuse among patients.

A second project, from Loyola Medicine and Loyola University Chicago, now aims to do a similar thing for alcohol abuse. The work, which was documented in a recently published paper, uses natural language processing to assess each patient's electronic medical records to identify potential alcohol misusers.

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Using AI to Improve Diagnoses and Prognoses of Diseases

Smarter diagnosis and prognosis of a disease has obvious benefits to patients and healthcare providers alike. A team from Cardiff University believes that artificial intelligence can play a big role in doing just that. In a recently published study, the team describes how AI can help improve risk assessments for patients with cardiovascular disease in an efficient manner that requires neither expertise or human interaction.

"If we can refine these methods, they will allow us to determine much earlier those people who require preventative measures. This will extend people's lives and conserve NHS resources," the researchers explain.

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How Doctors Can Use AI to Have Better Conversations With Patients

Rarely are conversations as important as those between a doctor and their patient. Being able to communicate often complex and distressing information in a clear and understandable manner is crucial. A recent paper from researchers at Trinity College Dublin, the University of Edinburgh and The Dartmouth Institute for Health Policy and Clinical Practice explores the possibility of using AI to improve the communication between doctor and patient.

"Many clinicians' communications skills aren't formerly assessed-either during school or in early practice. At the same time, there is a lot of evidence that clinicians often struggle when communicating with their patients. It's hard to improve on something when you're not being given any feedback and don't know how you're doing," the authors say.Image title

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Can AI Predict If You Will Age Healthily Or Not?

As societies across the world age, the goal in healthcare has turned towards ensuring people age as healthily as possible. A recent study from Salk's Integrative Biology Laboratory suggests AI can help to predict the likelihood of that happening.

It's fairly well established that our biological and chronological ages are not always the same. Some of the factors that influence this discrepancy are also becoming better understood, with things like diet and physical activity being believed to play a part.

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Medical AI Systems Struggle to Perform Well Across IT Systems

The level of expectation surrounding AI in healthcare has reached fever pitch in recent years, with a number of pilot projects achieving positive early results. Most of these projects involved AI systems being trained on a sample dataset of medical data, such as x-rays or other medical imagery, after which the system was capable of providing early detection of various conditions.

The challenge for many of these systems is that they were usually trained on data from a single healthcare provider, with a common health IT system. A recent study highlights how when faced with data from different health systems, such AI technologies often perform much worse than doctors.