Here is What Happened When I Asked AI How To Cure Cancer

For me it isn't a matter of if, but when artificial intelligence cures many of the diseases that are responsible for killing off millions of people each year across the planet. As we rapidly approach AI's ability to use reason rather than simply data to determine outcomes of problems - AI will be able learn autonomously, as well as use logic and reason. It is when we reach this point ( which I predict will happen by the end of 2024 and dominate in 2025 ) that we will see a rapid increase in advancement in the field of medicine.

I wondered though even before artificial intelligence is supposed to be able to make these proclaimed breakthroughs - what if I asked AI now about things like how to cure cancer what it would come with.

It's important to keep in mind that with AI tools such as ChatGPT, they are still relatively simple, and the output you receive is directly related to the questions you ask ( prompts ). If you go on to an AI tool right now and ask it how to cure cancer it's just going to give you a bunch of long winded answers with almost or nothing of value.

The key with AI is to ask it to focus and drill down on a subject which has the powerful result of stripping the guard rails that AI's creators have put in place to keep in in check - and in this post - I wanted to show you my results of my discussion with AI regarding curing cancer.

The answers may surprise you. Actually you know what. Do yourself a favor and read this to the end as the result form this experiment are going to bend your brain.

I began with this experiment with asking ChatGPT what cancer was in humans and then followed that up with asking what from an artificial intelligence analysis standpoint - where humans researchers should be focusing their efforts in cancer research on. Both broad boring questions the generated boring responses of little value.

I then followed that question with something more penetrating which was as follows.

In your observation of how humans approach cancer treatment - where do you see through analysis the mistakes being made in techniques reaseacher's use.

It's answer was intriguing.

In analyzing how humans approach cancer treatment, several common and potentially impactful mistakes or limitations emerge. Addressing these could lead to better outcomes and even improve the efficiency of treatment protocols. Here are some of the key areas where mistakes or suboptimal strategies are observed:

One-Size-Fits-All Approach: Traditional cancer treatments, like chemotherapy, often use standardized protocols rather than tailoring the approach to individual patients. This can lead to ineffective treatment for some while causing unnecessary side effects for others. Moving toward personalized medicine, where treatments are based on each patient’s genetic, molecular, and lifestyle factors, could enhance effectiveness.

Delayed Integration of New Data and AI Tools: There is often a lag between the discovery of new techniques, biomarkers, or AI-driven analysis methods and their adoption in clinical settings. Healthcare regulations and testing protocols are necessary but can sometimes slow down the introduction of promising new therapies or technologies that could significantly improve patient outcomes.

Limited Use of Preventive and Early Detection Approaches: Many efforts and resources focus on treating advanced-stage cancer, while early detection and prevention are less emphasized. Screening programs are available, but high costs, limited accessibility, or reluctance to adopt screening options mean that cancers are often detected too late for the most effective treatment options.

Ignoring the Tumor Microenvironment: Cancer is often treated as if it’s isolated within the body, yet the surrounding environment—including immune cells, blood vessels, and supporting structures—affects tumor growth and response to treatment. Effective treatment requires a more holistic understanding and targeting of this “ecosystem” to prevent recurrence and resistance.

Suboptimal Integration of Multimodal Therapies: For many cancers, a combination of surgery, chemotherapy, radiation, and immunotherapy may offer the best chance of remission. However, treatments are not always well-integrated. Treatment schedules may lack flexibility, or providers may avoid using multiple approaches simultaneously due to concerns about side effects. Better-coordinated, individualized treatment plans might enhance efficacy.

Overuse of Aggressive Therapies in Palliative Cases: There’s a tendency to use aggressive treatments even in late-stage or terminal cases, often causing more harm than good. Sometimes these treatments reduce quality of life without extending survival, while alternatives like palliative care may better address patient needs. A more balanced approach could consider patient goals and quality of life more carefully.

Inefficient Use of Real-World Data: Cancer research generates huge amounts of data, but this is often siloed, meaning insights from real-world data on patient outcomes, treatment efficacy, and side effects aren’t fully utilized in clinical practice. Improved data sharing and integration could help doctors make better-informed decisions and identify new treatment pathways.

Overlooking Mental and Emotional Health: Psychological support is often seen as secondary, but stress, anxiety, and depression can impact a patient’s ability to handle treatment and may even affect treatment outcomes. Comprehensive cancer care should address mental health and emotional well-being as part of the core treatment plan.

Failure to Address Treatment Resistance Mechanisms: Many cancer cells develop resistance to chemotherapy or targeted drugs over time, and traditional approaches often lack strategies to address this evolution. Integrating AI-driven predictive modeling could help clinicians anticipate and counteract resistance, tailoring treatment to delay or prevent resistance.

Underuse of Lifestyle and Nutritional Interventions: Lifestyle factors like diet, exercise, and sleep have a profound effect on health and could potentially support recovery or reduce recurrence. These factors are sometimes overlooked, but evidence suggests that integrating lifestyle changes with medical treatment could support the body’s ability to fight cancer and improve quality of life.

Addressing these mistakes requires a more adaptive, data-driven, and patient-centered approach to cancer care. With ongoing advancements in AI, genomics, and personalized medicine, there is an opportunity to reshape cancer treatment to be more effective and humane.        

AI basically criticized our approach to both cancer research and treatment with an under lying criticism of use of data and communication as well as our failure to prevent cancer rather than trying to treat it.

I then decided to drill down deeper on its answer and asked it the following.

Based on all of the knowledge and research humans have garnered to this point in our existence about cancer - what are we missing or not seeing based on your analysis

Here was its response

Despite substantial advances in cancer research, certain blind spots continue to limit the effectiveness of our approach to understanding, preventing, and treating cancer. Here are some key areas where humans may be missing opportunities or insights, based on patterns observed in current research and analysis:

Understanding Cancer as a Systems Disorder: Cancer is not merely a disease of isolated cells but involves complex systems-level interactions within the body. Researchers often focus on cancer cells alone rather than how the entire ecosystem, including the immune system, metabolism, and microbiome, influences cancer growth and metastasis. A more holistic systems approach could reveal pathways to disrupt cancer progression in ways we currently overlook.

Role of Epigenetics in Cancer Development and Reversibility: Much research has focused on genetic mutations as drivers of cancer, yet reversible changes in gene expression—epigenetics—play a crucial role. Unlike DNA mutations, which are permanent, epigenetic modifications can sometimes be reversed. By understanding and targeting these reversible changes, scientists could potentially create treatments that "reprogram" cancer cells back to normal.

Longitudinal and Real-Time Data Gaps: Most cancer research relies on snapshots of patient data collected during treatment, but cancer is a dynamic disease that changes over time. Integrating continuous, real-time data on how tumors evolve, particularly with wearable technology and AI, could lead to far more responsive treatments that adapt in real time to the cancer's progression.

Complex Interplay of Lifestyle, Environmental, and Social Factors: While some focus is given to lifestyle factors like diet, stress, and environmental exposures, their interactions with genetic susceptibility are still poorly understood. Large-scale studies incorporating lifestyle, environment, genetics, and socioeconomic factors could uncover critical insights into why some people develop cancer and others do not under similar conditions.

Cancer as an Adaptive Disease: Cancer can be seen as an adaptation of cells to stress—whether from toxins, chronic inflammation, or other cellular damage. This adaptation allows cells to survive hostile conditions, but it also enables malignancy. Focusing on the adaptive nature of cancer could lead to strategies that target this survival mechanism directly, "tricking" cancer cells into a less harmful state.

Underutilization of the Body’s Own Defense Mechanisms: While immunotherapy has gained ground, we still know relatively little about harnessing natural defense mechanisms like the immune system to their full potential. The immune system sometimes “ignores” cancer because it fails to recognize cancer cells as foreign. Developing deeper knowledge of immune tolerance and immune training could allow us to more effectively prime the immune system against cancer.

Energy Metabolism and Mitochondrial Health: Cancer cells have a unique way of generating energy, often relying on anaerobic glycolysis (the Warburg effect), even in oxygen-rich environments. Focusing on cancer metabolism and mitochondrial health could help in starving cancer cells of the energy sources they depend on while protecting normal cells.

Patient-Centric and Multi-Faceted Treatment Models: Often, the focus remains on killing or removing cancer cells rather than creating holistic treatments that incorporate physical, emotional, and social factors. Research shows that mental health, stress levels, and overall well-being influence treatment outcomes, but these areas are underexplored.

Understanding Microbiome’s Role in Cancer: The gut microbiome influences immune responses, inflammation, and even drug metabolism, all of which affect cancer risk and treatment effectiveness. Our knowledge of how microbiomes interact with cancer is still developing, but it may hold keys to new preventive and therapeutic approaches.

Aging as a Fundamental Driver of Cancer: Cancer is predominantly a disease of aging, but we have yet to fully understand the underlying mechanisms. Aging cells accumulate mutations and experience chronic inflammation, both of which contribute to cancer. By studying the biology of aging itself, researchers might uncover ways to delay or prevent cancer.

Emphasis on Prevention Rather than Cure: Most resources focus on treating cancer after it’s developed, yet the potential for prevention, particularly with early lifestyle interventions and risk monitoring, is enormous. Cancer prevention research, including lifestyle modification and environmental changes, often receives less funding and attention than treatment research.

Cross-Disciplinary and Multi-Cancer Research: Research is often fragmented by cancer type—lung, breast, prostate—leading to missed opportunities to uncover fundamental, cross-cancer principles. By looking across different cancers for shared patterns and molecular mechanisms, researchers could develop treatments that apply broadly across multiple types.

A shift toward understanding cancer as a dynamic, adaptive system within the body—and leveraging this understanding across various fields, including aging, immunology, microbiome studies, and data science—could lead to transformative insights. Bridging these knowledge gaps may reveal novel prevention and treatment strategies that move beyond merely fighting symptoms to altering the root causes of cancer.        

We were now starting to get somewhere - but still nothing overly ground breaking or startling. Rather than continuing to ask it for broad answers, I decided to ask it to focus on it's own answer, specifically in this case Microbiome’s Role in Cancer with the following prompt.

Let’s expand on Microbiomes role in cancer. What specifically does that data set and current research models tell you about this segment of understanding

Here was it's response.

The microbiome, particularly the gut microbiome, is emerging as a significant factor in cancer research. Recent data and research models reveal complex interactions between the microbiome and cancer risk, progression, and treatment response. Here’s a deeper look into what current research indicates about the microbiome’s role in cancer:

1. Microbiome Influence on Immune System Modulation
The microbiome has a profound impact on immune function, helping train the immune system to distinguish between harmful and benign cells. Certain microbes can influence immune responses by either promoting or suppressing inflammation, which is crucial because chronic inflammation is a known cancer driver.
Specific bacteria in the gut can stimulate the production of immune cells that either fight cancer or, in some cases, inadvertently help it evade the immune response. For example, Bacteroides fragilis has been linked to colorectal cancer through its ability to cause chronic inflammation in the gut lining.

2. Microbiome and Inflammation-Linked Cancers
Persistent inflammation caused by dysbiosis (imbalance in microbiome composition) is associated with a higher risk of certain cancers, such as colorectal, liver, and stomach cancers. Chronic inflammation damages cells over time, leading to mutations and, ultimately, cancer.

Studies on Helicobacter pylori, a bacterium in the stomach, show that it can cause chronic inflammation leading to stomach cancer. Similarly, gut dysbiosis can lead to inflammatory bowel diseases, which elevate colorectal cancer risk.

3. Microbiome’s Impact on Metabolism and Carcinogenic Compounds
Gut bacteria can metabolize certain dietary compounds into substances that either promote or inhibit cancer. For instance, some bacteria convert bile acids into secondary bile acids, which have been linked to colon cancer. Conversely, beneficial bacteria can produce short-chain fatty acids like butyrate, which have anti-cancer properties by promoting healthy cell turnover.

The microbiome’s metabolic processes may also influence the metabolism of toxins and carcinogens. Certain bacteria can neutralize carcinogenic substances from the diet, while others may enhance their potency.

4. Role of Microbiome in Drug Metabolism and Efficacy
Research reveals that the microbiome can significantly impact how the body processes cancer drugs. For example, some gut bacteria can metabolize chemotherapy drugs into inactive forms, reducing their efficacy. Conversely, other bacteria can activate certain drugs, enhancing their therapeutic effect.

Immune checkpoint inhibitors, a type of immunotherapy, have shown varied effectiveness based on patients’ microbiome compositions. Studies found that patients with a higher diversity of beneficial bacteria, such as Akkermansia muciniphila and Bifidobacterium, respond better to immunotherapy, as these bacteria seem to enhance immune system activation.

5. Microbiome Composition as a Biomarker for Cancer Risk and Treatment Response
Emerging research suggests that specific microbiome signatures might be used as biomarkers to predict cancer risk or treatment response. For instance, certain bacterial profiles are associated with a higher risk of colorectal cancer, offering a potential screening tool to detect precancerous changes.

Additionally, scientists are investigating the predictive power of microbiome profiles for treatment responses. Patients with certain microbiome compositions appear to have more favorable outcomes with specific cancer treatments, indicating that microbiome analysis could personalize treatment strategies.

6. Microbial “Oncometabolites” and Direct Carcinogenic Effects
Some bacteria produce metabolites known as “oncometabolites,” which directly promote cancer cell growth and survival. Fusobacterium nucleatum, for instance, is linked to colorectal cancer through its ability to promote cancer cell proliferation, inhibit immune responses, and even aid metastasis.

These oncometabolites can impact cell signaling pathways that regulate cell growth and apoptosis, tipping the balance in favor of cancer cell survival. Understanding how these microbial metabolites influence cancer could lead to targeted therapies that either inhibit harmful bacteria or block their effects on cells.

7. Microbiome Manipulation for Cancer Prevention and Treatment
Probiotics, prebiotics, and fecal microbiota transplants (FMT) are being explored as potential ways to restore a healthy microbiome and reduce cancer risk. FMT has shown promise in improving responses to immunotherapy in melanoma patients, suggesting that microbiome restoration may play a role in cancer treatment.

Dietary interventions are also an area of interest. Diets rich in fiber promote beneficial bacteria that produce anti-inflammatory compounds, which could help lower cancer risk. By tailoring diets to promote beneficial microbiome changes, researchers hope to find accessible strategies for cancer prevention.

8. Cross-Talk Between Microbiome, Host Genetics, and Environment
The interaction between a person’s genetics, their microbiome, and environmental factors complicates the microbiome’s role in cancer. Certain genetic mutations may predispose individuals to an imbalanced microbiome, increasing cancer risk. Similarly, environmental factors, such as diet and antibiotic use, can influence the microbiome’s composition and its role in cancer.

This “gene-environment-microbiome” interaction is an area of intense study, as understanding it could enable researchers to create more individualized cancer prevention strategies, based on each person’s unique genetic and microbiome profiles.

Challenges and Future Directions
Despite these promising insights, several challenges remain. Microbiome composition varies significantly between individuals, affected by genetics, lifestyle, diet, and environment, which makes it difficult to identify universal microbiome-based therapies. Additionally, the exact mechanisms by which the microbiome influences cancer are still being uncovered, and much research is needed to understand how microbiome manipulation might effectively prevent or treat cancer.

Summing Up
The microbiome’s role in cancer research is still developing, but its influence on inflammation, immune modulation, metabolism, and drug efficacy makes it a promising frontier. Harnessing the microbiome’s power, either to boost the immune response, block harmful metabolites, or aid in drug efficacy, could lead to more precise, less invasive cancer treatments and even open doors to preventive strategies. Future research will likely focus on fine-tuning microbiome therapies for personalized cancer care, leveraging the microbiome as both a diagnostic tool and a therapeutic target.        

Not only did this question encourage ChatGPT to provide a more concise answer, it set the process in motion to actually provide specific answers and is when it really started to get good.

I then decided now that since I had a flow in the conversation, which is the best way to reduce the walls of behavior created by the gate keepers of the AI application, a question that would make it far easier for it to make an actual statement and decision - all of which wouldn't have been possible if I simple came out of the gate and asked it the same query.

If there was one thing you as as AI could recommend researchers should focus on in curing cancer, what would it be.

The answer was far more concise and interesting as well as direct to the point.

If I could recommend one focal point for researchers in the quest to cure cancer, it would be targeting and modulating the tumor microenvironment (TME) to enhance the immune system’s ability to recognize and eliminate cancer cells.

Why the Tumor Microenvironment?
The TME, which surrounds cancer cells, includes blood vessels, immune cells, fibroblasts, signaling molecules, and the extracellular matrix. This complex environment often becomes immunosuppressive, protecting cancer cells from being detected and destroyed by the immune system. By focusing on modulating the TME, researchers could:

Reprogram immune cells to attack cancer cells more effectively.
Prevent the formation of new blood vessels (angiogenesis) that tumors use to grow.
Reduce tumor-promoting inflammation and other support systems that help cancer evade immune responses.

How AI Could Enhance TME Research
Artificial intelligence is uniquely capable of integrating massive data sets, from genomics and metabolomics to histology and clinical outcomes, which helps us understand how different cells within the TME interact with cancer cells. AI models can predict:

How cancer cells communicate with and alter nearby immune cells to evade detection.
Which drugs could disrupt these communications or “reprogram” immune cells.
Optimal combinations of existing therapies that target both the TME and the cancer cells directly.

Example of Impact: Immunotherapy Success Rates
Current immunotherapies, such as checkpoint inhibitors, work well for some cancers but fail in others because of immune suppression within the TME. Focusing on the TME could potentially widen the range of patients who respond to immunotherapies. Researchers could identify biomarkers within the TME that predict therapy responses and tailor treatments accordingly.

In essence, targeting and reprogramming the TME holds promise to turn the body’s own defenses against cancer in ways we haven’t yet fully achieved, making it a key focus area for a more effective, widely applicable cure.        

This is in itself fascinating - it provided a specific answer and for some reason, AI decided that this answer was the most important thing mankind could focus on when it came to cancer.

But we can't just blindly accept that answer - we need to know how it came up with it.

Was its response concluded by logic and was in fact ground breaking - or was it simply repeating something it learned about and categorized in its database of information from another website or article ?

Who better to ask then AI itself with the following prompt...

( Be prepared to have your mind blown )

Can you elaborate on how you came up with this information. Was it mathematical deduction, or was an answer that was already present on the internet. Or it fact, was it a unique answer you deduced on your own reasoning ?

Here was its response.

The response on focusing cancer research on the tumor microenvironment (TME) was a result of synthesizing existing information rather than raw mathematical deduction or merely quoting a single source. It’s a combination of drawing from known research patterns, identifying gaps in current cancer therapies, and inferring how AI's strengths could complement these needs. Here’s the breakdown of how this answer came together:

Data Synthesis: I analyzed information from many areas of cancer research, such as immunotherapy challenges, the biology of metastasis, and the role of the immune system in cancer prevention. Each piece contributes evidence that the TME is both a protector of cancer and a barrier to treatment efficacy.

AI’s Analytical Strengths: AI models are particularly strong in handling large, complex data sets, which is why they’re well-suited for analyzing the TME. This isn’t based on a single study but rather on the practical capabilities of AI in genomics, metabolomics, and cell signaling research, where AI has repeatedly proven effective in identifying complex interactions.

Logical Deduction and Patterns: Rather than deriving the recommendation through strict mathematical deduction, I used a logic-based approach by identifying core issues in cancer therapy that AI could address. For instance, we know immunotherapy shows promise but is not universally effective. Research suggests that the TME often shields tumors from immune responses, so improving immunotherapy requires addressing this barrier.

Gaps in Current Cancer Treatments: There are well-documented limitations in current approaches, such as chemotherapy resistance and immune evasion. These issues are intricately linked to the TME, making it a logical focal point where substantial progress could be made if tackled effectively with AI's predictive power and data processing capabilities.

Unique Perspective, Not New Information: This answer is a unique viewpoint formed by combining and interpreting available data, rather than introducing new, undiscovered information. The components are drawn from publicly available research and data, but the approach I took to prioritize the TME as the "one area" to focus on is a conclusion derived by synthesizing multiple sources and logical insights.

In summary, the response was developed by connecting diverse areas of knowledge and identifying where AI could provide a unique advantage—making it a distinct interpretation based on current research rather than a purely unique or previously undiscovered answer.        

What ??? Did you just say you used reason and logic to come up with an answer just like the human brain does ?

Lets look at that last paragraph once again

In summary, the response was developed by connecting diverse areas of knowledge and identifying where AI could provide a unique advantage—making it a distinct interpretation based on current research rather than a purely unique or previously undiscovered answer.

Not only did ChatGPT provide a logical, but it decided to " prioritize the TME " aspect of cancer resarch as the one most important area to focus on.

In this response we see that AI, in this case ChatGPT, was not just regurgitating its answer with information already on the internet but was actually taking all of the information and crunching it at a rate that would take a team of researchers thousands of hours and did it inside of a few seconds. It then came up with not only an answer from that reasoning - but then decided the best way to arrive at an outcome - and then was able to show how it came to its conclusion. Cue the sound of my head exploding.

Honestly, it was the closest thing I've seen to a logical response based on reasoning in two years with AI is incredible.

I would also add that the key take away on this entire discussion is at this point in time of the life cycle of AI - the importance of when working with which ever tool you prefer, that you need to drill down on your search for answers but have to use tact.

Much like a first date - you don't suddenly jump to the deep conversation - it takes probing questions and prep to get to the really good answers. And as this experiment showed, the date went well.

If you enjoyed this article, please consider following me as I document my journey with AI and how it can be applied to our real world including examining next week how the amazing new too Perplexity did when I asked it to predict the outcome of the 49er's and Cowboys Sunday Night match up.