CHATGPT and the future of Healthcare

CHATGPT and the future of Healthcare

CHATGPT is an artificial intelligence chatbot system, that OpenAI released in November 2022 to show off and test what a very large, powerful AI system can accomplish. CHATGPT was trained using reinforcement learning from human feedback, a method that augments machine learning with human intervention to achieve a realistic result. It is based on the GPT-3.5 architecture, which was trained on Microsoft Azure's supercomputing infrastructure and fine-tuned through Proximal Policy Optimization algorithms.

These algorithms present a cost-effective benefit to trust region policy optimization algorithms; they negate many of the computationally expensive operations with faster performance.

Unlike most chatbots, CHATGPT is stateful and can remember previous conversations. This allows it to be used as a personalized therapist. However, CHATGPT's reward model—designed around human oversight—may result in over-optimization; this can hinder performance. In addition, reviewers prefer longer answers irrespective of actual comprehension or factual content. Training data may also suffer from algorithmic bias; prompts including vague descriptors of people, such as CEO, could generate a response that assumes such a person is white male.

What are the Chatbots?

Chatbots are software applications developed with machine learning algorithms to provide real-time assistance to patients. The chatbot uses natural language processing (NLP) to stimulate and engage in a conversation with a user.

Chatbots have gained traction in retail, news media, social media, banking, and customer service. They are used every day on smartphones without users even realizing it. From catching up on sports news to navigating bank applications to playing conversation-based games on Facebook Messenger, chatbots are revolutionizing the way we live.

What are Chatbots in the Healthcare Industry?

The healthcare industry is beginning to leverage AI-enabled tools to simplify patient care and cut unnecessary costs. The advantages of using hybrid chatbots in healthcare are enormous – and all stakeholders share the benefits.

Medical chatbots can help healthcare professionals reduce their workload by reducing hospital visits, reducing unnecessary treatments and procedures, and decreasing hospital admissions and readmissions as treatment compliance and knowledge about their symptoms improve. For patients, this comes with a lot of benefits like helping patients with simple tasks, such as reminding them to take their medication or scheduling appointments. They can also help patients with complex tasks, such as identifying the symptoms of a certain illness and giving information about treatment options. In addition to these basic functions, chatbots have been shown to be especially effective at providing emotional support for those who are suffering from mental health issues.

Chatbots are driving healthcare cost savings with experts estimating that they will reach $3.6 billion globally by 2022. Chatbots are gradually reducing hospital wait times, consultation times, unnecessary treatments, and hospital readmissions by connecting patients with the right healthcare providers and helping patients understand their conditions and treatments even without visiting a doctor.

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Furthermore, Hospitals and private clinics use medical chatbots to triage and clerk patients even before they come into the consulting room. These bots ask relevant questions about the patient’s symptoms, with automated responses that aim to produce a sufficient history for the doctor. The doctor then looks at this information and determines which patients need to be seen first and which patients require a brief consultation.

The truth is that chatbots can't replace a doctor's expertise or take over patient care. However, combining the best of both worlds can improve the efficiency of patient care delivery and simplify and fast-track care without compromising quality.

Use Cases of Chatbots in Healthcare:

There are three primary use cases for the use of chatbot technology in healthcare: informational, conversational, and prescriptive. The conversational style, depth of communication, and type of solutions provided by these chatbots vary significantly.

1.?????Informative Chatbots:

Informative chatbots provide users with helpful information—such as notifications, pop-ups, and breaking news—that are generally not available through traditional media. These bots can also help answer customer support questions and provide information about a topic. Health news websites and mental health websites also use this technology to help them access more detailed information about a topic. For example, while reading about alcohol addiction and withdrawal, a chatbot may pop-up with this: “Do you need help with alcohol addiction? Speak with any of our mental health professionals.”

2.?????Conversational Chatbots:

Conversational chatbots are designed to respond to specific user requests in a conversational manner. Conversational chatbots use natural language processing (NLP) and natural language understanding (NLU), applications of artificial intelligence (AI) that enable machines to understand human language and intent.

3.?????Prescriptive Chatbots:

Prescriptive chatbots are designed to provide answers or direction, but they also offer therapeutic solutions. One example of a prescriptive chatbot is Woebot—a therapeutic chatbot designed by researchers at Stanford University that uses cognitive behavioral therapy (CBT) techniques to treat mental health issues. People who suffer from depression, anxiety disorders, or mood disorders can converse with this chatbot and help treat themselves by reshaping their behaviors and thought patterns.

CHATGPT Impact of Healthcare:

The impact of CHATGPT on healthcare has been significant and wide-reaching. As a large language model trained by OpenAI, CHATGPT can process and generate human-like text, making it an ideal tool for natural language processing tasks in the healthcare industry.

With the emergence of CHATGPT, a new generation of medical records is being generated. By analyzing a patient’s medical history and current symptoms, CHATGPT can generate a comprehensive and accurate medical record, reducing the time and effort required by healthcare professionals. This can also improve the accuracy and consistency of medical records, as CHATGPT is less susceptible to human error.

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n addition to providing a platform for healthcare professionals to interact with patients, CHATGPT can also be used as a diagnostic tool. By analyzing a patient's medical history and symptoms, CHATGPT can recommend further testing or treatment options. This can aid healthcare professionals in making more accurate and timely diagnoses, ultimately improving patient care.

Conclusion

Healthcare providers can benefit from CHATGPT's innovative technology by improving their services and positively impacting the lives of their patients. It is a simple yet powerful tool that could make an enormous difference in this industry.

CHATGPT has limits, but it is the first chatbot that is enjoyable enough to speak with and useful enough to ask for information. It can engage in philosophical discussions and help with practical matters. And it is strikingly good at each. After years of false hype, the real thing is here.

References:

1. https://openai.com/
2. https://en.wikipedia.org/wiki/CHATGPT
3. Knox, W. Bradley;?Stone, Peter.?Augmenting Reinforcement Learning with Human Feedback?(PDF).?University of Texas at Austin. Retrieved?December 5,?2022.
4. Ahmed, Zohaib (December 2, 2022).?"What is CHATGPT, the AI chatbot that everyone is talking about".?The Indian Express. Retrieved?December 5,?2022.
5. Jump up to:a?b?c?d?OpenAI?(November 30, 2022).?"CHATGPT: Optimizing Language Models for Dialogue". Retrieved?December 5,?2022.
6. Schulman, John; Wolski, Filip; Dhariwal, Prafulla; Radford, Alec; Klimov, Oleg (2017). "Proximal Policy Optimization Algorithms".?arXiv:1707.06347?[cs.LG].

7. van Heeswijk, Wouter (November 29, 2022).?"Proximal Policy Optimization (PPO) Explained".?Towards Data Science. Retrieved?December 5,?2022.

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