Tag Archive for: ChatGPT

AI Allies: How Artificial Intelligence Can Support Patients To Cope with Cancer

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A cancer diagnosis can be both physically and emotionally challenging. As cancer patients learn how to cope with the disease[1] and manage side effects[2], artificial intelligence (AI) tools like ChatGPT are emerging as valuable allies. This article explores ten ways ChatGPT can help you manage your cancer journey more effectively.

What is ChatGPT

ChatGPT is an AI language model developed by OpenAI[3], designed to simulate human-like conversation. Think of it as a smart chatbot that can craft responses that sound like they came from a person all based on what you ask or tell it.

ChatGPT 4 is the latest version, offering advanced features and improvements over previous versions. [4] However, you have to pay to get the full benefits of ChatGPT 4. ChatGPT 3.5, though older, is still available for free and is a reliable option for those who don’t need the full capabilities of ChatGPT 4.

Are there Alternatives to ChatGPT?

Yes, several other chatbots and AI language models can understand and generate human-like text. Examples include Microsoft Copilot and Perplexity.ai.

10 Ways To Use ChatGPT to Cope with Cancer

1. Explaining Medical Terms

Medical jargon can be confusing. ChatGPT can break down complex terms into understandable language. For instance, you could ask, “What does ‘carcinoma’ mean in simple terms?” This can help you understand essential terms without feeling overwhelmed by medical language.

2. Detailing Treatment Options

ChatGPT can provide an overview of various treatments. For example, you might ask, “Can you explain the different types of chemotherapy?” Understanding your options can empower you to make informed decisions about your care.

3. Treatment Side Effect Management

Knowing what to expect from treatment can ease anxiety. ChatGPT can inform you about common side effects and how to manage them. For instance, you can ask, “How can I manage nausea during chemotherapy?” ChatGPT can provide detailed, user-friendly responses, helping you prepare for and cope with treatment side effects.

4. Medication Questions

Managing cancer medications can be complex, especially with multiple drugs involved. ChatGPT can help you understand your medication schedules, potential interactions, and what to do if you miss a dose. Sample questions might include, “Are there any foods or drinks I should avoid while taking this drug?” or “What should I do if I miss a dose of my cancer medication?”

5. Managing Stress and Anxiety

A cancer diagnosis can take a heavy emotional toll. It’s common to feel anxious, fearful, and uncertain, and having a supportive resource can greatly reduce these feelings. ChatGPT can serve as a virtual companion, available 24/7. While it cannot replace human interaction, it can offer a comforting presence during lonely or anxious moments. For instance, you might express, “I’m feeling overwhelmed,” and in return receive a supportive response such as, “I’m here for you. Let’s discuss what’s troubling you.”

6. Finding Resources and Support Groups

ChatGPT can suggest resources, including support groups and educational materials, to help patients connect with others and stay informed. It can be helpful to ask, “Can you recommend any online support groups for cancer patients?”

7. Exercise Recommendations

Engaging in exercise while undergoing treatment can enhance both your emotional and physical health by boosting your mood and energy levels. If you ask ChatGPT, “What are some safe exercises for someone undergoing cancer treatment?” it will  give you some suitable options.

8. Nutritional Advice

Proper nutrition can support your body’s healing process and improve your overall health. Ask ChatGPT, “Can you recommend a diet that supports cancer treatment?”

9. Preparing for Doctor Visits

ChatGPT can help you prepare questions for doctor visits so you can get the most out of your appointments. For instance, you might ask, “What questions should I ask my oncologist about my treatment plan?”

10. Symptom Tracking and Management

ChatGPT can provide tips on how to monitor and manage your symptoms more effectively. For example, you could ask, “How can I track my symptoms and know when to call my doctor?” This can help you stay proactive about your health.

How to Ask Clear Questions to ChatGPT

If you want the best responses from ChatGPT, it’s important to ask questions that are clear and specific. Below are some tips to help you do this.

  • Be specific: Clearly state what you want to know. Instead of asking, “Tell me about cancer,” ask, “What are some common side effects of breast cancer treatment?”
  • Provide context: Give background information if relevant. For example, “I am undergoing radiation therapy for lung cancer. What side effects should I expect?”
  • Ask follow-up questions: If the initial response isn’t comprehensive, ask additional questions to get more detailed information.

Using AI with Discernment: A Word of Caution

While ChatGPT can provide valuable support, it’s important to use it with discernment. AI is not a doctor; it’s a sophisticated algorithm designed to process and generate human-like text. As much as ChatGPT aims for accuracy, it may occasionally produce incorrect or outdated information. Always cross-check with reputable medical sources before making decisions based on AI advice. As AI continues to evolve, its role in healthcare will likely expand[5], but human oversight remains indispensable.

[1] Optimizing ChatGPT: How Patients With Cancer Can Use AI as a Thought Partner | Cancer Nursing Today

[2] Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift | JNCI Cancer Spectrum | Oxford Academic (oup.com)

[3] Introducing ChatGPT | OpenAI

[4] GPT-4 vs. ChatGPT-3.5: What’s the Difference? | PCMag

[5] Walker H, Ghani S, Kuemmerli C, Nebiker C, Müller B, Raptis D, Staubli S Reliability of Medical Information Provided by ChatGPT: Assessment Against Clinical Guidelines and Patient Information Quality Instrument J Med Internet Res 2023;25:e47479

URL: https://www.jmir.org/2023/1/e47479

DOI: 10.2196/47479

What Are Potential Impacts of Artificial Intelligence on AML Patient Care?

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What Are Potential Impacts of Artificial Intelligence on AML Patient Care? from Patient Empowerment Network on Vimeo.

How might acute myeloid leukemia (AML) patient care be impacted by artificial intelligence? Expert Dr. Andrew Hantel from Dana-Farber Cancer Institute and Harvard Medical School shares his perspective on potential risks and benefits of the impact of AI on AML patient care.

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Transcript: 

Lisa Hatfield:

Dr. Hantel, can you elaborate on the significance of oncologists believing that AI-based clinical decision models need to be explainable? And how might this impact AML patient care and decision-making processes?

Dr. Andrew Hantel:

Sure. So I think just taking a step back and saying you know what is AI, and what does explainability of AI even mean? So AI or artificial intelligence is essentially computer algorithms that learn to some extent like us, but in other ways differently, kind of how to process information and make decisions based on that information or make recommendations, at least.

And to some extent, like you or I, we can’t really explain “Why did I decide to have Cheerios this morning versus having like whole wheat toast or something?” It’s kind of difficult for me to say, “Oh, I just felt like I wanted to do that instead of that.” To some extent, AI also does that. It can kind of arrive at a decision after digesting a lot of different data over its lifetime to say that it prefers Cheerios versus whole wheat toast.

But it can’t necessarily tell you why it wanted one versus the other. And in medical decisions, to some extent, the same things can happen. It can’t really adequately explain to some extent why it might recommend one treatment versus another. And we like to think that in medicine, we’re making evidence-based recommendations that we choose treatment one or treatment two over treatment three, because the evidence for one and two is better for the person in front of us.

And AI can also kind of explain things some ways to that extent, but in other ways it might not know all of the other characteristics of the person that aren’t in that computer that make us think treatment one or two is better than three. And so our ability to actuallyd say, “Is the AI making this decision appropriately and able to explain why it came to decision one and two?”

If it can’t do that, we can’t actually understand whether or not it’s gone wrong and whether or not we should trust what it’s recommending. And so for that, we kind of have to create artificial intelligence models that are explainable by saying, “I’m telling you, you should choose this option versus that option because of reasons A, B, and C as they apply to this patient who is being taken care of.” And the hope is that there are ways computer scientists are using to try and get AI towards that.

But we really need to make sure that we create an AI that’s trustworthy in order for us to make you know AML patient care decisions that do better for our patients, because we know that AI is powerful, and it can bring in a lot of different data sources that are difficult for any human to make in any kind of scenario. But to be able to do that in a way that doesn’t put patients at risk and that really improves their care and improves our ability to maintain and optimize people’s health is essential. And so while AI is not kind of right now being used to make decisions in AML patient care, it’s going to be tested probably in the near future to help out with that in clinical trials and controlled settings.

And so you as a patient or somebody who is very interested in the power of AI, I would say once we start to hear about those things, it might be something that you’re interested in participating in a trial, or you’re interested in kind of learning more about that. We could come back and talk about that more. For the moment though, I think it’s just more of a risk that we’re trying to avoid of making AI that’s not explainable and potentially harms patients rather than helps them.

Lisa Hatfield:

Okay, thank you. One of the things I know in some cancer research is they are using artificial intelligence and machine learning models to help predict outcomes based on certain therapies. And I wonder if you have any comments on, because the data used is historical and real time coming in all the time, but we know there are inherent biases based on disparities in healthcare anyway from underrepresented communities. Do you think that those biases can be overcome in future models that are used to predict outcomes to treatment for different types of cancers?

Dr. Andrew Hantel:

Yes. So I think there’s a number of different biases that can come into artificial intelligence models. And it’s the same, a lot of the same biases that we have in our current clinical trials, and that historically marginalized groups have not been well-represented, either in participating in trials or in their data that’s input into these AI models. And for kind of the same reason, we don’t really know how generalizable the data that we have from the trials or from the AI really apply to those populations.

We assume because they have a lot of the other same characteristics as the people who are in the trials or kind of in these models that we can apply those data to them. But I think the push is to use both data sets and to encourage participation in trials for those communities, such that we know that these drugs and that AI are safe and effective for them.

And so there are both efforts to do that in leukemia and cancer broadly, and across healthcare even more broadly. And that can be either by working together with kind of multinational consortia of physicians and researchers to kind of pool data that includes patient populations from around the world. And the same thing is being done for trials as well as to kind of help make sure that the people who are underserved also kind of within our own communities are included in both of these processes.

Lisa Hatfield:

If a patient were to come on to you and said, “Dr. Hantel, I looked up on ChatGPT, what is the best treatment for me given these mutations or this characteristic of my disease?” What might you say to them? Would you involve that in your decision-making? Would you discuss that with them a little bit more? How would you handle that?

Dr. Andrew Hantel:

I think I would just generally be curious about you know what the actual transcript of the conversation was like. I think right now one of the major concerns for a lot of AI is that it can hallucinate things. And so there are some famous examples of lawyers putting in you know kind of briefs that they wanted to file and the AI coming up with like court cases that never existed to justify things. And so the last thing that we want is in medical decisions for people to rely on kind of made-up facts to make treatment choices.

And so, I’d be interested in kind of its medical decision-making process and kind of the data that it was able to rely on to make the decision. More from the standpoint of curiosity and education for myself to understand how patients are interacting with these things, as well as to make sure that the patient was also understanding kind of the information that was being put out and wasn’t having any misconceptions.

I think that the potential for these AI to help patients is vast in terms of their ability to understand a lot of the medical jargon and a lot of the information that’s coming at patients through portals and everything else, that could be very scary. But I also want to make sure that we’re not kind of overloading patients with what we think is an answer, but actually can come with a lot of falsehoods and harm.

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What Are the Latest Artificial Intelligence Advancements for Myeloma?

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What Are the Latest Artificial Intelligence Advancements for Myeloma? from Patient Empowerment Network on Vimeo.

What artificial intelligence advancements have emerged for myeloma? Expert Dr. Ola Landgren from University of Miami Sylvester Comprehensive Cancer Center discusses the IRMMa prediction model for myeloma care, factors that go into the IRMMaa model, and potential AI advancements for myeloma.

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Transcript:

Lisa Hatfield:

So that kind of leads to the next question that is really an exciting area. I know it’s not necessarily new, but newer is artificial intelligence. And I know I was reading an article about one of, that you and your colleagues have worked on a newer project and I don’t know if you pronounce it IRMMa or not, but using these large databases to help predict I think, it’s the response of treatment in some patients. So can you talk about that a little bit and tell us about that development and what developments are exciting with artificial intelligence in cancer, in particular myeloma?

Dr. Ola Landgren:

Yeah. So you mentioned the study we just published. We published a model that we call IRMMa and that stands for individual risk prediction for patients with multiple myeloma. So what we were thinking was at the current time, all the existing models are pretty much providing the average patient’s predicted outcome. So think about it is like it’s a probability measure.

So you say, if I take this about therapy, what’s the predicted average outcome for patients that take this therapy, say, five years later? So on average, say 70 percent of patients are free from progression. That sounds pretty good. The problem is that you don’t know if you are in the group, 70 percent group that didn’t progress or if you’re in the 30 percent that did progress.

So where are you as an individual? So it’s almost like looking at the weather app on your phone. If it says it’s a 70 percent probability of sunshine and then you go outside and it’s raining, it’s because it didn’t say that it’s 100 percent probability of sunshine. So if you think about another situation would be, say, in a GYN clinic, if a woman were to come and ask the doctor, am I pregnant? Yes or no? You couldn’t say it’s 70 percent probability. You would say, yes, you’re pregnant or not pregnant.

So for myeloma, we have for a long time been living in these weather report systems where we say 70 percent or 30 percent. And we want to go in the other direction of the pregnancy test, where we actually can say for someone with this particular disease profile, with this treatment, this is where this is going to take us. We worked on this project for almost four years, and we worked with a lot of other groups around the world that have a lot of data. And they have graciously agreed to collaborate with us and share their data sets. The beauty with this collaboration, there are many beauties of it, but one of them is that people don’t treat patients the same way.

And that actually has allowed us to say for patients that have a particular biological or genomic makeup, if you’re treated this way or that way or the other way or a fourth way and so forth, which of these different treatments would make patients have the longest progression and overall survival? So if you have a large database, you can actually ask those questions. So you can say that you profile individual patients in full detail and you put them in detailed buckets instead of grouping everybody together.

And now if you add a new case, if a new patient is being added and you say, which bucket would this individual fit? Well, this is the right biological bucket. You can then use this database to say out of all the different treatment options, which treatment option would last the longest, which would give the best overall survival? Other questions you could ask is also, for example, you have a patient with a certain biological workup or makeup. And you say, if I treat with these drugs, will the addition of, say, transplant, will that prolong progression for his survival?

And you can go into the database and the computer will then say, I have these many patients that have this genomic makeup and these many people that were treated with this treatment with transplant versus the same treatment without transplant. There was no difference in their progression or overall survival. So then the computer would say, it doesn’t add any clinical benefit, but there could be another makeup where the answer is opposite, but transplant actually would provide longer progression for his survival. I think the whole field of medicine is probably going to go more and more in this direction.

So what we want to do is to expand the number of cases. So we are asking other groups around the world, if they have data sets with thousands of patients, they could be added to this database and we could then have more and more detailed information on sub-types of disease and more and more treatment. So it will be better as we train it with larger data sets. The model is built as an open interface so we can import new data. And that’s also important because the treatments will continue to change. So we, for example, say I have a patient that has this genetic makeup. I was thinking of using a bispecific antibody for the newly diagnosed setting.

How is that going to work? The computer will say, I don’t know, because we don’t have any patients like that in the database because that’s not the data, type of data that currently exists from larger studies. But let’s say in the future, if there were datasets like that, you could ask the computer and the computer will tell you what the database finds as the answer. But if you go for another combination, if that’s in the database, it would answer that too. That is where I think the field is going.

And lastly, I would say we are also using these types of technologies to evaluate the biopsies, the material. We work with the HealthTree Foundation on a large project where we are trying to use computational models to get out a lot of the biological data out of the biopsies and also to predict outcomes. So I think artificial intelligence is going to come in so many different areas in the myeloma field and probably in many, many other fields in medicine.

Lisa Hatfield:

Yeah, that’s wonderful. So if you have a newly diagnosed patient coming in to see you, do you use this model and explain to them what came back to you? Or is it right now just collecting the data for this dataset?

Dr. Ola Landgren:

So at the current time, we have to be cautious. We cannot promise things that we cannot deliver. So we have clearly said this is a research tool. It was just published less than a month ago in the Journal of Clinical Oncology. It is publicly available. The paper is available. Anyone can go there and download the paper and anyone can also in this paper find there’s a website. You can actually see the database as well. And there is a lot of corresponding material online on how to interpret. So for now, it is a research tool. But I think it’s possible in the future that we would start considering using it. And if other people find it useful, maybe they will do that, too. But for now, it is a research tool.

Lisa Hatfield:

As a patient, I would be very intrigued with that and what might come back. Just like you said, it’s a tool to maybe help identify new treatments or whatever. I did ask ChatGPT if there’s a cure for myeloma, if there will be one in the next 10 years. I didn’t really love the answer. It was a little bit vague. But yeah, I like looking into AI a little bit and ChatGPT and all that. So thank you so much for that overview.

Dr. Ola Landgren:

This is exactly like ChatGPT. It works the same way, but it’s only centered around multiple myeloma. For now, the way we have done it is that we have to start somewhere. As I told you, it’s a four-year work effort with a lot of people. We have like 10, 15 people working day and night on this project. So we started on the newly diagnosed patients. But we intend to scale it up. We intend to build in a lot of new features. And, of course, we want to add more datasets to it. And last thing I want to say to you, I find it very, very fascinating how you can, as a human being, as a researcher, you can ask the computer, and it will give you answers back that you didn’t think about yourself.

So you talk about ChatGPT. So we are using our model. We can have the model looking at biopsies. We can ask the computer, what is this biopsy? What’s going on? And the computer will say this and this and this genetic feature is going on. And then we ask the question, how did you conclude that? And then the computer will say, look here. So it would then label areas in the biopsy and say, I looked here. It doesn’t yet tell us what it found, but it tells us where it found it or where it looked to come to its conclusion.

So when it finds the right conclusion, we are looking in these areas to see what’s going on in these areas and how does it look different from other areas or in other samples. So having a dialogue with the computer can give us new insight. It’s almost like taking a young kid and you go out for a walk and you look and you see a lot of buildings and the kid looks down and looks and finds a little flower on the ground. And you say, oh, my gosh, I missed that one. The kid would not miss it. The computer is the same way.

Lisa Hatfield:

Yeah, that’s a great analogy, too. And I think we could have a 10-hour conversation about that, particularly with myeloma, because it’s so complicated, complex. So I hope we can in the future again talk about that. 

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