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AI in healthcare

Artificial Intelligence (AI), Machine Learning (ML), and data science have become some of the most significant topics of discussion in today’s technological era. In one of the speakers’ sessions on the ‘Future of Data and AI’, several experts in these fields came together to discuss the latest advancements and how they are using them in their everyday work. 

Introduction of panelists 

The session starts with Hamza, a research science manager at Google, introducing himself and explaining how he runs a few ML models and helps build models that can predict user abuse. Hamza works in the trust and safety group within search, where they prioritize the protection of users. 

Generative AI: Trends, Ethics and Societal Impact – Watch the complete session  

The other experts introduce themselves as well. Batool, who has experience working as an AI scientist at Amazon, focused on dialogue machines and natural language understanding.

Meanwhile, Francesca, a Principal Data Scientist manager at Microsoft, leads teams of data scientists and ML scientists, working on internal problems at Microsoft. Raja, the Founder, and Chief Data Scientist at Data Science Dojo has been working in data science before it was even called data science. 

Use of Generative AI 

The conversation then shifts to the use of generative AI, which has been used in the field of data science and ML for a while. Francesca explains that there are three main categories where generative AI is used every day in her work.  

The first is generating natural language, which includes summarization, translation, and question-answering systems. The second is an image and video generation, which has applications in industries like gaming and advertising. The third is generating music, which can be used for composing, arranging, and creating personalized music. 

A deeper understanding of the current state of the field 

The experts then discuss the latest advancements in these fields. Raja emphasizes the importance of the latest advancements in deep learning, specifically transformers, in NLP tasks. He also mentions the development of large-scale language models like GPT-3, which can perform tasks like translation, summarization, and question-answering. 

Matul discusses how chatbots have evolved from rule-based systems to data-driven systems, where they can use data to train and improve their performance. This includes using natural language processing to understand and respond to user queries more effectively. 

Francesca highlights the importance of democratizing AI and making it accessible to all people, regardless of their technical background. This involves developing user-friendly tools that can be used by people without technical expertise, which can be used to address common business problems. 

Generative AI – The impact of ground-breaking generative AI technologies 

Open AI has brought about a major transformation in the field of artificial intelligence (AI), data science, and machine learning. One of the most significant contributions of open AI is its generative AI capabilities that help in generating code, images, and troubleshooting bugs. These capabilities are particularly useful for data scientists who need to deploy and operationalize their machine-learning applications. 

Ground-breaking Generative AI
Ground-breaking Generative AI

Generating code from one programming language to another is one of the three main categories where generative AI applications have been seeing a lot of demand. Another popular application of generative AI is in generating images, especially for use cases such as generating images from text descriptions. 

For data scientists like the speaker, who work mostly in the AI, data science, and machine learning space, most of their work is done on the cloud. With open AI, data scientists can now access pre-trained generative AI models and customize them with their data. They can also use built-in tools to detect and mitigate any biases or unfair dynamics that may exist in their applications. 

Open AI has made accessing these tools easier through the open AI studio, where one can build AI models and deploy them faster. The speaker has found this to be a privileged situation and has been using generative AI for various communication purposes such as spot-checking, rephrasing, and creating snippets for social media posts. 

Human intelligence in conjunction with AI 

While AI has brought about a significant change in the field of content creation, the speaker warns against relying solely on AI. Human intelligence should be used in conjunction with AI to create the best results. AI is just another tool that should be used with caution, as a few wrong jumps can take you in the wrong direction. 

The other speakers in the panel discussion also shared their experiences with generative AI. One of them is writing a book that covers popular machine learning algorithms using fiction. While, until a few years back, his biggest concern was hiring graphic designers and concept artists, now, with generative AI, he can create his book’s graphics on his own. 

Generative AI’s impact on creative work  

Generative AI is impacting creative work and work in general in many ways. In creative industries, such as marketing, graphic design, animation, and content creation, generative AI is a valuable tool that allows for faster and more efficient production of high-quality content. It can also democratize access to expensive resources like models for photo shoots, making them more accessible to smaller designers. 

In other industries, such as manufacturing, healthcare, and energy, generative AI can also be used to improve efficiency and productivity. For example, it can be used to design new products, optimize manufacturing processes, and analyze medical images. 

Overall, generative AI has the potential to impact work across many different industries, and its adoption is likely to continue to grow as more businesses discover its benefits. While it may not eliminate jobs, it will likely change the nature of work in many industries, requiring workers to learn new skills to work effectively with these tools. 

Read about 12 must-have AI tools to revolutionize your work 

Francesca, emphasizes the importance of considering the ethical implications of working with AI systems, not just generative AI. She has a checklist of principles that she follows, such as fairness, reliability, safety, privacy and security, inclusiveness, accountability, and transparency, which are industry standards developed by tech companies. While principles are essential to keep in mind, Francesco also suggests that tools such as interpretML and fair AI can be leveraged to understand the impact of data on predictions and results better.  

OpenAI and generative AI have many benefits, such as improving content quality, variety, and personalization. However, to ensure these benefits follow ethical principles, the model life cycle, which starts with data collection, pre-processing, model building, and tuning model parameters and ends with prediction and interpretation, must involve humans in all stages.

Generative AI in healthcare and energy

Generative AI in Healthcare
Generative AI in Healthcare

AI in healthcare

There are many exciting ways that generative AI is being used to tackle important problems in the fields of healthcare and energy. One area where generative AI is being used in healthcare is in the creation of medical images such as X-rays and MRIs. With the help of generative AI, researchers can generate high-quality medical images that can help in the diagnosis and treatment of various medical conditions. 

It is also being used to develop new drugs and treatments. With the help of deep learning algorithms, researchers can analyze large amounts of medical data to identify new drug candidates and develop personalized treatment plans for patients. 

In the field of energy, generative AI is being used to optimize energy systems and reduce energy consumption. For example, AI models can be trained to predict energy usage patterns and adjust energy supply, accordingly, reducing waste and increasing efficiency. 

Another area where generative AI is being used is in the creation of virtual environments for training purposes. With the help of generative AI, researchers can create realistic virtual environments that can be used to train individuals in various fields such as medicine, engineering, and military training. This can help to reduce the risk of accidents and injuries during training and improve overall safety. 

Generative AI and government regulations

Overall, the role of the government in regulating the use of generative AI to create content is a highly debated topic. Some believe that the government should intervene to prevent monopolies from happening and to fund open-source projects to democratize data. Others argue that too much regulation could stifle innovation and competition.  

It is essential to strike a balance between promoting innovation and protecting consumers’ interests. Legislation and regulations could be created to define what constitutes fair use and set standards for the ethical use of AI, such as the AI bill of rights. Ultimately, governments will act following the general culture and society’s values in their region, making laws that align with what is considered acceptable. 

Closing of the session – Generative AI  

In conclusion, AI, ML, and data science have become vital to our daily lives, with advancements in these fields impacting various industries. With the continuous development of new technology, it is essential to keep up to date with the latest trends and advancements to stay competitive in the industry. The experts in the session provided valuable insights into the latest advancements and how they are using them in their everyday work. As these fields continue to evolve, it will be exciting to see what new advancements will come next. 

 

March 31, 2023

This blog discusses the applications of AI in healthcare. We will learn about some businesses and startups that are using AI to revolutionize the healthcare industry. This advancement in AI has helped in fighting against Covid19.

Introduction:

COVID-19 was first recognized on December 30, 2019, by BlueDot. It did so nine days before the World Health Organization released its alert for coronavirus. How did BlueDot do it? BlueDot used the power of AI and data science to predict and track infectious diseases. It identified an emerging risk of unusual pneumonia happening around a market in Wuhan.

The role of data science and AI in the Healthcare industry is not limited to that. Now, it has become possible to learn the causes of whatever symptoms you are experiencing, such as cough, fever, and body pain, without visiting a doctor and self-treating it at home. Platforms like Ada Health and Sensely can diagnose the symptoms you report.

The Healthcare industry generates 30% of 1.145 trillion MB of data generated every day. This enormous amount of data is the driving force for revolutionizing the industry and bringing convenience to people’s lives.

Applications of Data Science in Healthcare:

1. Prediction and spread of diseases

Predictive analytics process

Predictive analysis, using historical data to find patterns and predict future outcomes, can find the correlation between symptoms, patients’ habits, and diseases to derive meaningful predictions from the data. Here are some examples of how predictive analytics plays a role in improving the quality of life and medical condition of the patients:

  • Magic Box, built by the UNICEF office of innovation, uses real-time data from public sources and private sector partners to generate actionable insights. It provides health workers with disease spread predictions and countermeasures. During the early stage of COVID-19, Magic Box correctly predicted which African states were most likely to see imported cases using airline data. This prediction proved beneficial in planning and strategizing quarantine, travel restrictions, and enforcing social distancing.
  • Another use of analytics in healthcare is AIME. It is an AI platform that helps health professionals in tackling mosquito-borne diseases like dengue. AIME uses data like health center notification of dengue, population density, and water accumulation spots to predict outbreaks in advance with an accuracy of 80%. It aids health professionals in Malaysia, Brazil, and the Philippines. The Penang district of Malaysia saw a cost reduction of USD 500,000 by using AIME.
  • BlueDot is an intelligent platform that warns about the spread of infectious diseases. In 2014, it identified the Ebola outbreak risk in West Africa accurately. It also predicted the spread of the Zika virus in Florida six months before the official reports.
  • Sensely uses data from trusted sources like the Mayo Clinic and the NHS to diagnose the disease. The patient enters symptoms through a chatbot used for diagnosis. Sensely launched a series of customized COVID-19 screening and education tools with enterprises around the world, which played a role in supplying trusted advice urgently.

Want to learn more about predictive analytics? Join our Data Science Bootcamp today.

2. Optimizing clinic performance

According to a survey carried out in January 2020, 85 percent of the respondents working in smart hospitals reported being satisfied with their work, compared to 80 percent of the respondents from digital hospitals. Similarly, 74 percent of the respondents from smart hospitals would recommend the medical profession to others, while only 66 percent of the respondents from digital hospitals would recommend it.

Staff retention has been a challenge but is now becoming an enormous challenge, especially post-pandemic. For instance, after six months of the COVID-19 outbreak, almost a quarter of care staff quit their jobs in Flanders & Belgium. The care staff felt exhausted, experienced sleep deprivation, and could not relax properly. A smart healthcare system can solve these issues.

Smart healthcare systems can help optimize operations and provide prompt service to patients. It forecasts the patient load at a particular time and plans resources to improve patient care. It can optimize clinic staff scheduling and supply, which reduces the waiting time and overall experience.

Getting data from partners and other third-party sources can be beneficial too. Data from various sources can help in process management, real-time monitoring, and operational efficiency. It leads to overall clinic performance optimization. We can perform deep analytics of this data to make predictions for the next 24 hours, which helps the staff focus on delivering care.

3. Data science for medical imaging

According to the World Health Organization (WHO), radiology services are not accessible to two-thirds of the world population. Patients must wait for weeks and travel distances for simple ultrasound scans. One of the foremost uses of data science in the healthcare industry is medical imaging. Data Science is now used to inspect images from X-rays, MRIs, and CT scan to find irregularities. Traditionally, radiologists did this task manually, but it was difficult for them to find microscopic deformities. The patient’s treatment depends highly on insights gained from these images.

Data science can help radiologists with image segmentation to identify different anatomical regions. Applying some image processing techniques like noise reduction & removal, edge detection, image recognition, image enhancement, and reconstruction can also help with inspecting images and gaining insights.

One example of a platform that uses data science for medical imaging is Medo. It provides a fully automated platform that enables quick and accurate imaging evaluations. Medo transforms scans taken from different angles into a 3D model. They compare this 3D model against a database of millions of other scans using machine learning to produce a recommended diagnosis in real-time. Platforms like Medo make radiology services more accessible to the population worldwide.

4. Drug discovery with data science

Traditionally, it took decades to discover a new drug, but the time has now been reduced to less than a year using data science. Drug discovery is a complex task. Pharmaceutical industries rely heavily on data science to develop better drugs. Researchers need to identify the causative agent and understand its characteristics, which may require millions of test cases to understand. This is a huge problem for pharmaceutical companies because it can take decades to perform these tests. Data science has solved this problem and can perform this task in a month or even a few weeks.

For example, the causative agent for COVID-19 is the SARS-CoV-2 virus. For discovering an effective drug for COVID-19, deep learning is used to identify and design a molecule that binds to SARS-CoV-2 to inhibit its function by using extracted data from scientific literature through NLP (Natural Language Processing).

5. Monitoring patients’ health

The human body generates two terabytes of data daily. Humans are trying to collect most of this data using smart home devices and wearables. The data these devices collect includes heart rate, blood sugar, and even brain activity. Data can revolutionize the healthcare industry if known how to use it.

Every 36 seconds, a person dies from cardiovascular disease in the United States. Data science can identify common conditions and predict disorders by identifying the slightest change in health indicators. A timely alert of changes in health indicators can save thousands of lives. Personal health coaches are designed to help to gain deep insights into the patient’s health and alert if the health indicator reaches a dangerous level.

Companies like Corti can detect cardiac arrest in 48 seconds through phone calls. This solution uses real-time natural language processing to listen to emergency calls and look out for several verbal and non-verbal patterns of communication. It is trained on a dataset of emergency calls and acts as a personal assistant of the call responder. It helps the responder ask relevant questions, provide insights, and predict if the caller is suffering from cardiac arrest. Corti finds cardiac arrest more accurately and faster than humans.

6. Virtual assistants in healthcare

The WHO estimated that by 2030, the world will need an extra 18 million health workers worldwide. Using virtual assistant platforms can fulfill this need. According to a survey by Nuance, 92% of clinicians believe virtual assistant capabilities would reduce the burden on the care team and patient experience.

Patients can enter their symptoms as input to the platform and ask questions. The platform would tell you about your medical condition using the data of symptoms and causes. It is possible because of the predictive modeling of disease. These platforms can also assist patients in many other ways, like reminding them to take medication on time.

An example of such a platform is Ada Health, an AI-enabled symptom checker. A person enters symptoms through a chatbot, and Ada uses all available data from patients, past medical history, EHR implementation, and other sources to predict a potential health issue. Over 11 million people (about twice the population of Arizona) use this platform.

Other examples of health chatbots are Babylon Health, Sensely, and Florence.

Conclusion:

In this blog, we discussed the applications of AI in healthcare. We learned about some businesses and startups that are using AI to revolutionize the healthcare industry. This advancement in AI has helped in fighting against Covid19. To learn more about data science enroll in our Data Science Bootcamp, a remote instructor-led Bootcamp where you will learn data science through a series of lectures and hands-on exercises. Next, we will be creating a prognosis prediction system in python. You can follow along with my next blog post here.

Want to create data science applications with python? checkout our Python for Data Science training. 

August 18, 2022

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