Consider two individuals diagnosed with the same medical condition, such as high blood pressure. Both are prescribed the same initial medication. Medications have been commonly prescribed to patients by physicians for decades in the same manner. One individual’s experience with the medication has no negative side effects.
The medication also effectively reduces the individual’s blood pressure. The second individual experiences numerous frustrating side effects from the medication. Unfortunately, the medication does not effectively reduce the individual’s blood pressure. Most of us can recall hearing a similar story about someone we know. These stories are a perfect example that an approach to health known as “one size fits all” does not fit everyone.
Imagine your physician had a method of treating you that was not based on guesswork and trial and error. Your physician can create AI for a personalized treatment plan tailored to your specific needs, including your genetic makeup.
This concept of creating a treatment plan tailored to the individual is often referred to as AI for Personalized Treatment and represents a new way of delivering health care. While most treatments ask what will work well for the majority of patients, the AI for Personalized Treatment approach asks a much more significant question: What will work best for you? Precision Medicine is a term used by many experts to describe the use of precision medicine as an approach to delivering health care.
Achieving that vision will require a tool capable of understanding the trillions of minute details that set you apart. The vehicle that can accomplish that is Artificial Intelligence (AI). Think of AI as a very intelligent helper for your doctor, rather than a robot doctor.
It works somewhat like the way that Netflix uses its recommendation engine to recommend a movie that you are going to enjoy, but rather than using your viewing history to make recommendations, AI examines medical data to provide your doctor with information that identifies the most probable successful approach to managing your health.
Machine Learning in Healthcare is quietly shaping many modern physicians’ medical decisions, thanks to the power this technology has developed.
AI is also creating unprecedented amounts of knowledge, enabling doctors to view things in ways they have never been able to before. For example, AI has enabled doctors to detect cancer earlier in the process by analyzing scans and to predict medication side effects before a patient takes any.
In this article, we will explain how this technology works in basic terms, what it means for your health, and how it could represent a brighter future for all of us through AI for Personalized Treatment.
Real patients are individuals, not statistical averages, as they vary by genetics, behavior, symptoms, and risk factors, which is why I see an opportunity for AI to bridge this gap. I propose “AI for personalized treatment” to use patterns in health care data to help providers select treatments that fit each patient, rather than relying on categories (e.g., “diabetes,” “asthma,” “over 65”).
I clearly explain in the article that this is not intended to replace doctors, but rather to support their decision-making by identifying hidden signals in data and rapidly evaluating numerous potential options.
I am describing the AI’s methodology, the types of data used, where the AI is currently applied, and the technology’s limitations—all in simple, practical language and examples.
AI for Personalized Treatment: What It Really Means
At its heart, AI for Personalized Treatment is about using computer models to find the right treatment for the right patient at the right time.
AI for Personalized Treatment is simply “using the experience of many patients to inform the treatment of one.” The system looks at how other people with similar characteristics reacted, then uses their past experiences to predict how well different treatments will perform for you.
AI for Personalized Treatment supports numerous clinical activities, including:
- prediction of your likelihood of experiencing a health concern prior to symptom manifestation.
- suggestion of tests that are most likely to yield relevant results.
- determination of an appropriate dose of a medication for you to take.
- Adapting your treatment plan to reflect changes in your condition.
Some may refer to this as Personalized Medicine AI. What is significant is not the title, but the purpose: providing care tailored to your needs based on actual data.
It is also related to AI in Precision Healthcare, where the focus is on greater precision—reducing uncertainty in decision-making about what is most likely to benefit a particular individual.
Beyond One-Size-Fits-All: What Does “Personalized Medicine AI” Actually Mean?
For many years, medical care has been based upon an approach that could be called “a one size fits most”. While a one-size-fits-all approach is a good place to start, it does not account for the fact that no two people are identical.
Precision medicine or personalized medicine in AI for personalized treatment reverses this model entirely. Rather than beginning with the “average patient”, it begins with you. Precision medicine does not begin by looking at the name of your condition (e.g., cancer), but instead begins to look at your unique biological history.
To do this, it considers your biological makeup, including your genetic code, lifestyle choices, and the environment you live in. If you think of it like reading a specific instruction book for your body rather than a general textbook on medicine, that would help illustrate how this model works. This evolving model is referred to as Personalized Medicine AI in Practice.
The goal is direct yet highly innovative: to select the best treatment option for the right patient at the right time. With a deeper understanding of the factors that distinguish you from other patients, physicians may be better able to anticipate which therapies will be most effective and which may cause adverse reactions, thereby improving patient outcomes.
However, how will any physician evaluate all this individual data? That is where they receive their new form of superpowers. That is where AI for Personalized Treatment offers a critical benefit.
Your Doctor’s New Superpower: How AI for Personalized Treatment Actually Finds Personalized Insights
Given the vast amount of individual data, Artificial Intelligence comes into play. Artificial Intelligence is not a robotic doctor; it is the world’s greatest research assistant. No matter how dedicated a doctor may be, he/she cannot read all the new medical studies published every day, nor can he/she compare you to the millions of cases of others like you.
However, an AI tool can perform both tasks in minutes by analyzing vast amounts of literature to identify patterns that are often not visible to the human eye.
An AI tool finds clues about a patient’s specific condition by analyzing their health information at multiple levels, including their unique genetic code, lab test results, and other lifestyle and medical history factors. The AI cross-references the patient’s “unique” profile with a large, anonymous collection of health information from tens of thousands to millions of other patients.
For instance, the AI tool asks virtually, “Out of a million patients who have a biologic profile similar to your own, what was the treatment that resulted in the least number of side effects?” This is the role of big data in enabling AI for personalized treatment in healthcare. This is a practical demonstration of Predictive Analytics in Healthcare.
A crucial point is that this technology does not replace doctors in making decisions about your care — it enables them to provide better care for you. The AI-driven predictive analytics does not diagnose you or prescribe medications.
It simply provides the doctor with data analysis (statistical probabilities and hidden patterns), from which they can use their years of training and experience to make an informed decision based on the information provided by the system.
The AI-driven insights inform the treatment planning process, but ultimately, the doctor makes the decision, drawing on years of training and experience to interpret the findings.
Finding Trouble Sooner: How AI Helps Doctors Spot Disease on Medical Scans
The ability to discover hidden patterns will significantly impact one of the most common medical procedures: reading scans such as X-rays, CT scans, and MRIs. The radiologist is a highly trained expert with a heavy workload, which makes interpreting the many complex black-and-white images difficult.
In addition to being difficult, tumors in their very early stages may appear as shadows or as only a slight indication of disease, making them nearly imperceptible.
This is where artificial intelligence serves as a vigilant colleague, providing an additional set of “eyes” that never tire.
This artificial intelligence learns from an extensive visual library.
For example, a system could have analyzed millions of anonymous lung scans, far more than any individual could study in a lifetime. By analyzing this large body of work, the AI can learn to recognize subtle textures and patterns in scans that indicate the earliest signs of cancer, often before the cancerous cells are detectable to the human eye.
This capability is a powerful example of AI for Personalized Treatment in action. This system is not searching for a prominent, identifiable area of abnormality; it identifies subtle variations that suggest an issue and warrant further inspection.
This technology has already improved patient health outcomes in hospitals. The application of AI for Personalized Treatment alerts the radiologist to areas flagged by the AI system as potential disease indicators in mammograms or brain scans, which may otherwise go unnoticed.
AI systems trained using Machine Learning in Healthcare learn patterns and visual clues associated with diseases from millions of medical images. AI for Personalized Treatment can help doctors detect and diagnose conditions more effectively and at an earlier stage than relying solely on their own skills; however, AI will never replace a doctor’s expertise and should always be used to support a doctor’s diagnosis.
Data-Driven Healthcare Solutions: What kinds of data does it use?
Good predictions depend on good data. Most systems use several types of input because a single source is not enough to tell the whole story.
Common input data are:
Electronic medical records (EHRs)
Electronic medical records contain a patient’s complete clinical history, including diagnoses, allergies, prescribed medications, previous treatments/procedures, and clinician notes. With proper care, AI for Personalized Treatment can learn which histories contribute to specific outcomes.
Laboratory test results
Lab tests can measure blood glucose, kidney function, and cholesterol levels. Models typically focus on long-term trends in lab test results rather than individual values.
Medical Images
Medical images such as X-rays, CT scans, MRI scans, and pathology slides have high-resolution detail that image models can use to identify patterns related to disease type and severity.
Wearable and Home Devices
Wearable devices such as heart rate monitors, sleep trackers, activity trackers, blood pressure cuffs, and glucose monitors can provide context for a patient’s daily activities. When patients do not have regular check-ins with their clinicians, these devices will further support Data-Driven Health Care Solutions.
Genomics/Other “Omics”
Some genetic testing can help explain why one drug works well for one patient but not for another. However, genetics is only part of the picture.
Patient Lifestyle Factors/Social Factors (When Appropriate and Ethical)
Patient food availability, stress, job schedules, and support network can influence a patient’s overall health. When handled appropriately and safely, with respect for privacy, patient lifestyle, and social factors, AI can also enhance Personalized Treatment.
Not all patients have all types of data. Therefore, many systems may need to operate in environments with missing data. In addition, systems must clearly identify the uncertainty of the missing data.
Machine Learning in Healthcare: How the system learns: a plain-language view of the “AI” part
Most current medical applications of Clinical Artificial Intelligence are not magical. They are based on identifying patterns.
In Machine Learning in Healthcare, a Model has learned from previous instances. The previous instance includes Input Data (e.g., age, lab results, and diagnosis) and an Outcome (e.g., hospitalization, complications, or recovery).
As more instances are used to train the Model over time, it will adjust its internal workings to produce more accurate predictions for New Cases. This prediction from the Model may be used as an additional consideration when Clinicians make decisions. There are several common types of Models:
Supervised learning
This type of Model learns using Labeled Outcomes such as “Did this Patient Experience a Complication? Yes/No.”
Unsupervised learning
In this type of machine learning, there is no “right answer” for the model to learn from; it simply identifies different patient populations based on their similarities. It may identify subpopulations within a disease population that have previously been overlooked or underexplored.
Reinforcement learning (less common in direct care today)
In reinforcement learning, the model learns by trying different action sequences in a simulated environment and determining which yields a better outcome.
While some of the most effective machine learning models in healthcare may still fail if the underlying data is biased or outdated, this does not diminish the need to evaluate, monitor, and set boundaries for their use.
AI’s ability to flag issues for human clinicians to review and make a definitive diagnosis has led to earlier and more accurate detection of various diseases. While AI’s ability to understand and interpret images is significant, the technology can also analyze the code of life itself—the genetic material that defines us all. When AI begins to read DNA, the possibilities for improving diagnostics and treatments will expand exponentially.
Unlocking Your Personal Health Code: How AI Reads Your DNA
AI can analyze genetic information and identify biomarkers that help doctors predict how you will respond to drugs. Using AI in this way is the foundation of AI in Precision Medicine and enables doctors to adjust treatment before it causes adverse reactions or becomes ineffective.
A medical scan provides doctors with a picture of what is wrong with your body today, but your DNA provides doctors with a complete blueprint of how your body works.
Your genome contains billions of pieces of information. With so much data available, for a doctor to try to locate a single piece of relevant information in all of that data would be equivalent to searching for a single book title in a library filled with millions of books. This is something that the human mind cannot accomplish, which is why AI for Personalized Treatment plays such a critical role.
This is an area where AI’s ability to process vast amounts of information quickly becomes particularly important. The reason is that AI can scan your entire genetic code for specific “health signals” called biomarkers, as identified by medical professionals. Biomarkers can be considered the small highlighted areas in your body’s instruction manual.
A biomarker could be a genetic variation that would explain why you have high cholesterol, but it could also tell your doctor how well your body will react to a particular medication—an essential capability of AI for Personalized Treatment.
The potential impact of this technology on many individuals’ lives is becoming evident. There is a relatively common blood thinner that has been used to reduce the risk of stroke. Doctors had traditionally used a standard starting dose of the medication for years; this dose was too large for many patients and too low for others.
Using AI to analyze a patient’s genetic data, a biomarker can be identified that indicates how the patient’s body metabolizes the medication. This is the basis of a new discipline called pharmacogenomics, and by using it, a physician can determine the exact starting dose of medication to administer on the first day of treatment, thereby significantly decreasing the risk of serious side effects—one of the clearest benefits of AI for Personalized Treatment.
By translating each individual’s genetic code into personalized health recommendations, AI has enabled medicine to become even more AI for personalized treatment
and predictive than ever before. In doing so, AI is enabling us to transition from simply treating symptoms to identifying and addressing the root causes of the problems we face, as indicated by the underlying biological mechanisms encoded in our genetic code.
Additionally, using the same type of analysis (i.e., determining the specific genetic weaknesses of a disease), scientists are now beginning to apply it to another of our greatest foes. Cancer is a prime example of a disease in which the specific genetic makeup of a tumor can be analyzed to create a customized approach to fighting the disease.
AI-Driven Treatment Planning: From prediction to a plan: how treatment gets AI for personalized treatment
A single prediction is not enough to develop an actionable plan that a clinician can use.
The next step will be translating the results into a plan that the clinician can evaluate.
AI-Driven Treatment Planning helps clinicians compare potential plans (i.e., treatment options) by predicting the probability and magnitude of expected benefit(s), risk(s), and response(s) for a particular patient profile.
Potential examples of how AI-Driven Treatment Planning could assist clinicians include:
- Evaluating two medications that have different levels of side effects associated with their administration
- Determining whether to initiate a particular therapy or “wait and watch.”
- Suggesting additional supportive care to potentially prevent/limit future complications
- Identifying when a patient’s current plan should be evaluated earlier than planned
- A clinical decision support system in a hospital may detect an increase in a patient’s risk and recommend an earlier lab test. An example of this is a clinical decision support system used in a clinic that alerts to potential changes in a patient’s medications due to their trending kidney function.
- AI for personalized treatment can also decrease trial-and-error if used properly. The AI system should always show its level of confidence in its results and explain why it reached them.
Predictive Analytics in Healthcare: Predicting risk and response: what analytics adds
Predictive analytics in health care is used to answer many uncertain questions, such as “How will drug A affect that individual?” “What are the chances that he/she will experience adverse reactions from drug A?” “Is there a chance that she/he will be hospitalized in the next thirty days?”
The use of statistical and machine learning techniques in predictive analytics in health care does not guarantee an answer; it provides a probability that can inform a more intelligent conversation.
Predictive analytics in healthcare AI for personalized treatment may include as primary elements:
- risk (probability of an event occurring)
- response (probability of a treatment working)
- harm (probability of adverse reactions or complications)
- timing (time at which an adverse reaction will occur)
The predictive analytics model will determine which treatment a patient should receive based on their individual characteristics, such as specific laboratory results or medical history.
Predictive analytics in healthcare can help professionals in disease prevention by identifying at-risk patients sooner than would be possible without it. With this advance notice, a healthcare professional may begin monitoring the patient more closely, provide additional support to help reduce the potential consequences of illness, or modify existing barriers to further reduce the risk of illness.
As noted earlier, models are only as good as the data on which they were trained and may perform poorly when applied to a different population (hospital) or practice setting.
Where it helps today (and what “transformative” really looks like)
One of the most realistic promises isn’t an immediate miracle. The real transformation will be in the steady development of targeted treatments with less avoidable harm and quicker learning from clinical data.
The following are the areas where AI for Personalized Treatment is currently being used, and/or rapidly developing:
Medication selection and dosing
Different people may metabolize medications differently. Depending on the inputs provided to the Personalized Medicine AI, it can assist with safer dosing recommendations, particularly when kidney/liver function has changed.
Cancer Care
Cancer is not just one single disease; there are multiple types of cancer. An AI tool for Precision Health can assist in analyzing pathology images, combining patient test results, and discussing possible treatment options with patients.
Diabetes/ Cardiometabolic Care
Glucose monitor data, along with data from other lifestyle trackers, can also help adjust specific treatments on an individual basis; therefore, they can support Data-Driven Healthcare Solutions that adjust as the patient’s daily habits change.
Mental Health Support
The digital tools available can help track symptoms and patterns of engagement (or lack thereof). The intention is to help clinicians identify when a patient may require earlier outreach rather than later.
Hospital Care / Complications
Predictive models can estimate the likelihood of developing a hospital-acquired infection, sepsis, or other adverse outcomes, and of readmission. Therefore, the use of AI for Treatment Planning can support early assessments and potentially preventive actions.
In all of the aforementioned areas, the most successful applications of Machine Learning in Healthcare occur when they are implemented as part of established clinical workflows, and not as a standalone “answer machine” in healthcare
A Custom-Built Weapon: How AI Creates Unique Cancer Treatment Plans
Doctors have used traditional methods to treat cancer that damage both healthy and unhealthy cells. With today’s Artificial Intelligence (AI), doctors can now develop targeted treatments that focus on a tumor’s genetic makeup and identify its weaknesses. As a result, doctors can use AI-driven treatment planning that targets cancer cells while leaving healthy tissue intact.
Chemotherapy has been the primary tool doctors have utilized to treat cancer for many years; however, although effective at saving lives, it tends to be an all-or-nothing approach when it comes to targeting cancer cells. Chemotherapy uses a “sledgehammer” method that causes significant harm to healthy cells as well as the cancerous cells.
For this reason, chemotherapy can create severe side effects as the entire body is being treated for the disease rather than just the disease itself. A sledgehammer may be a strong and powerful tool, but in the fight against cancer, a surgeon requires precision—something AI for Personalized Treatment aims to support.
Therefore, what if doctors could use a “key” that would selectively unlock and kill cancer cells? The potential for Targeted Therapy lies in the premise that scientists can use AI to analyze a tumor’s individualized genetic structure (its “personal instruction book”) to identify the specific weaknesses driving its growth.
This precision-based approach is a core example of AI for Personalized Treatment, as the analysis provides the information necessary to create a customized plan to destroy the cancer while sparing the remainder of the body.
Consider a patient who has failed to respond to conventional treatments for his/her lung cancer. Prior to the advent of AI, the number of viable treatment options may have been limited.
The AI program analyzes tumor genetic mutations and identifies an unusual mutation driving the cancer’s continued growth. Moreover, the AI program cross-references that mutation against a large database of drugs and flags a targeted therapy developed to inhibit it. This information could enable this patient to receive a treatment previously overlooked by AI for Personalized Treatment.
The transformational approach to treating cancer using AI is shifting cancer care from a strategy of a general or “broad” attack to one of an “intelligent”, targeted strike.
The use of AI will lead to the development of not only more effective but also less toxic treatments, ultimately improving patient outcomes and quality of life. Identifying the appropriate treatment for an individual using AI represents a huge advancement; however, what if AI could help develop entirely new cures at an accelerated rate compared to previous methods?
Using AI for Personalized Treatment, oncologists can identify the most suitable treatment options for each patient based on the specific mutation driving their disease.
From Decades to Days: How AI Is Speeding Up the Search for New Cures
Traditionally, it would take drug developers decades to develop new medicines through trial and error. Today, artificial intelligence (AI) allows researchers to model diseases digitally and run millions of compound tests for months of data in mere days. The use of these Data-Driven Healthcare Solutions has significantly accelerated the development and availability of new treatments for patients, while reducing the costs of their development.
Traditionally, creating an entirely new medicine was a monumental challenge of trial and error. Think of trying to locate a single key that will fit a unique lock buried among millions of other keys. For over a decade, researchers had to perform the same function in their laboratory – using trial and error, they tested each of potentially millions of chemical compounds individually.
This process is extremely time-consuming and costly; the average time to get a new drug approved is more than a decade, and the cost of approval is estimated at more than $2 billion.
AI converts this search process from a marathon to a sprint, accelerating innovation for AI for Personalized Treatment. Years of laboratory work testing chemical compounds on live cells in a Petri dish will be replaced by AI that generates an extremely detailed digital representation of a disease (e.g., a cancer cell or a virus). This represents an electronic version of a flight simulator, but for medicine.
In this simulated world, the AI can test millions of potential drug compounds over a few days to predict which is most likely to be effective against the digital disease. The AI will show the scientist only the best drug compounds, enabling them to use their limited lab resources (time and money) to develop a compound with the greatest chance of success, strengthening the future of AI for Personalized Treatment.
The impact of AI in drug discovery is substantial, particularly when time is a constraint. The ability to rapidly identify new treatments creates a new resource that enables researchers to respond to pandemics and other public health emergencies more quickly than before.
Additionally, the rapid development of treatments for rare diseases may offer viable options for conditions that would have been too expensive to study using traditional methods. However, finding treatments for diseases is only half the battle. Can AI for Personalized Treatment help detect diseases early so patients can receive treatment before symptoms appear?
What If You Could Predict a Health Problem Years in Advance?
Rather than responding to illness, AI may enable forecasting disease risk well in advance of any physical or symptomatic presentation. In Predictive Analytics in Healthcare, an individual’s genetic information, lifestyle habits, and medical history are combined to develop proactive preventive measures and informed long-term care planning decisions.
While finding new treatments for diseases is a major breakthrough in healthcare, the most significant advancement may be preventing disease before it occurs. This preventative shift is a key goal of AI for Personalized Treatment. For example, when a weather forecast provides you with current conditions (it’s currently raining), it also provides you with the probability of precipitation (rain) for the upcoming week.
In addition, a good weather forecast will give you enough lead time to make preparations for the impending rain. The AI technology’s disease-risk forecasting function is very similar to a weather forecast. An AI system uses an individual’s health data to serve as a personal health forecast. The AI system then identifies the early indicators of a potential storm on the individual’s health horizon, enabling AI for Personalized Treatment to act before symptoms appear.
These predictions are made possible by the A.I.’s use as a master detective, connecting the dots that no individual could link. These “dots” include analysis of your unique genetic makeup, your family members’ health history, data from a fitness tracking device, and data collected during previous medical visits.
The AI uses this information to generate a “Risk Score” based on its assessment of the likelihood of developing conditions such as heart disease and/or diabetes in the future. This proactive insight represents one of the most powerful benefits of AI for Personalized Treatment.
The potential of looking ahead is quite strong. If your doctor determines that your Risk Score indicates that you are at high risk of having a heart attack, instead of waiting until you experience a serious health problem, your doctor may want to work with you now to develop a preventative plan.
Your doctor’s preventative plan may consist of simply changing your diet, creating a customized workout plan, and/or prescribing medication to reduce your risk of developing heart disease many years before any damage occurs to your body.
Proactively managing your health is a fundamental aspect of what the future of AI in Personalized Healthcare promises to deliver. However, for an A.I. to provide this type of powerful prediction, it will need the correct fuel to do so.
One of the most powerful applications of AI for Personalized Treatment is this predictive capability.
The Fuel for AI’s Engine: Your Health Data (and How It’s Kept Safe)
To run AI applications, large amounts of health data are needed. With strong anonymizing (removing names) and with regulatory oversight (guidance) to protect patient confidentiality, Data Driven Healthcare Solutions can both facilitate innovation and maintain confidentiality.
Health data – lots of it – is what fuels those innovations. An AI needs to analyze millions of cases to learn how to identify the earliest signs of a heart attack or determine the best drug for a particular individual.
This data-driven approach is foundational to AI for Personalized Treatment. It’s very similar to a medical student studying numerous medical texts and seeing thousands of patients to become a specialist. By analyzing health data from a wide range of people, the AI discovers connections and patterns too subtle for humans to notice.
Because of this reliance on data, there is a significant issue regarding patient confidentiality. Many people would not want their sensitive health records accessed by a computer system, even when used for AI-driven personalized treatment. Your medical history is arguably the most private thing you have. This is not something that researchers or hospitals take lightly. Maintaining confidentiality of the health record is the basis upon which these systems operate.
To protect privacy, researchers strip identifiable information from their data before using it for artificial intelligence research. Any data used for artificial intelligence research will have all personally identifiable information removed, such as your name, address, and date of birth. The only remaining identifiable information will be medical information—such as an electrocardiogram (EKG) reading, laboratory results, etc.—with no ability to identify who generated the data.
U.S. law, particularly HIPAA and similar Indian legislation such as the DPDP Act, DISHA (proposed), and the IT Act, governs how your data can be collected, stored, and protected. This delicate balance enables scientific progress without violating individuals’ rights. While the AI receives the massive, anonymous dataset needed to grow into a more intelligent tool, the individual’s identity remains separate from it.
While AI can review data at a level beyond that of humans, there remains a reasonable concern about the future of the doctors and nurses we rely on for our care. Trust and transparency will remain the foundation for the successful application of AI in Precision Healthcare.
What must go right: privacy, fairness, and safety
For AI for Personalized Treatment to be trusted, it must protect patients and improve care without causing additional harm.
The primary issues that will need to be addressed are:
Privacy and consent
Healthcare data is highly sensitive; therefore, strong security measures and transparency are essential. Patients must know which data was used to create their model and why.
Bias and fairness
If a training dataset underrepresents certain populations, predictive models may have lower accuracy when making predictions for those populations. In addition, this could exacerbate already existing inequities in healthcare delivery. To be responsible AI in Precision Medicine, model development will need to assess performance within each group and make necessary corrections, rather than ignore the issue.
Transparency (in plain language)
Both clinicians and patients want to know why a particular decision was made. If a predictive model is too complicated for humans to understand, it should at least provide clues on how to answer questions such as “what recent lab trends” or “do you have a history of reactions,” rather than simply providing a number.
Ongoing Safety Checks
Just because a model has been trained on data from past years does not mean the same will hold in future years. New treatments are being developed, new documentation systems are being implemented, and new subpopulations are entering the healthcare system each year. Good Data-Driven Healthcare Solutions include both an active, ongoing process to monitor the model and a well-defined plan to roll back changes if problems occur.
Clinical Responsibility Remains with Humans
Although models may assist in clinical decision-making, they cannot replace the clinician’s decision-making authority. The clinician still needs to consider the patient’s personal goals, preferences, and specific circumstances — all of which are difficult or impossible to capture in a model.
What patients can ask their care team?
To help you be informed about tools that may be used to assist in your healthcare, consider asking the following questions:
- What is this tool’s intended purpose? What does it support with its decisions?
- What data am I providing to this tool?
- Is this tool designed to provide accurate results based on my characteristics (such as age, gender, etc.)?
- Are there any potential risks associated with incorrect outcomes from using this tool?
- Will the tool’s output result in a change to my treatment plan, or will it simply provide additional information?
- Can I opt out of collecting specific data through this tool whenever possible?
These types of questions support informed patient care, regardless of how complex or simple the tool being utilized is (e.g., Personalized Medicine AI or a basic risk assessment calculator).
Is an AI Going to Replace My Doctor?
The first thought when hearing about such intelligent AI is “will doctors be out of a job?” The answer is absolutely NO. AI was created to help and support medical doctors. A doctor is part of a Human-in-the-Loop system, where AI provides insights and ideas, and the doctor makes all their decisions by considering the patient’s well-being, years of experience, and clinical judgment.
Think of AI in Healthcare as a modern airliner with an advanced autopilot. The autopilot has access to massive amounts of information and will operate the aircraft smoothly and efficiently. However, the experienced human pilot is always at the controls, ready to take over if anything goes wrong or requires critical decision-making.
The idea behind using AI in healthcare is to make it a highly effective clinical assistant to physicians, rather than replacing them. Physicians can perform much of the heavy lifting by using AI to sift through all the data a clinician would need in seconds, helping them find a needle in a haystack.
This approach is central to AI-enabled personalized treatment, where technology supports clinical decision-making without replacing human judgment. For example, an AI will take a chest X-ray and highlight a small spot that the radiologist had difficulty seeing.
This is how the AI provides a second opinion based solely on the data, so when the physician has enough information, they can apply their years of education and knowledge of the patient’s health history to reach the correct diagnosis. This type of system is called a “human-in-the-loop” system. The technology provides insight into the patient’s health condition, but ultimately, the physician decides the course of action.
In addition, many aspects of medicine are uniquely human. A physician cannot provide empathy, comfort, and compassion when having difficult conversations with a patient about treatment options. In the future, AI for personalized treatment and medicine will not eliminate physicians but rather empower them with better tools and free up more of their time and attention for their patients.
For the physician to truly partner with the AI, we must ensure that the AI’s guidance is fair and unbiased for each patient, enabling personalized treatment to benefit everyone equitably.
This partnership is the future of AI for Personalized Medicine.
Making AI Fair: The Important Ethical Hurdles We Must Overcome
To create an opportunity for everyone to experience the extraordinary possibilities of the future of medicine enabled by Artificial Intelligence (AI), we must first overcome several significant ethical barriers. As previously mentioned, the potential of AI as a medical treatment option depends entirely on the data from which it has been developed and/or trained.
Therefore, if there are gaps or “blind spots” in the data used to develop the AI, the AI will also have those gaps or “blind spots”. Two of the greatest obstacles that developers and researchers are working diligently to resolve are:
- Fairness Problem (Bias): Assuring that AI-based tools function effectively for people regardless of race, ethnicity, or background.
- Access Problem (Equity): Assuring that the expensive, customized treatments generated through AI are accessible to all who need them – and not limited to the wealthier populations of society.
Bias is perhaps the greatest challenge to the use of AI in medicine. Consider an example of an AI tool created to identify skin cancer. If the system’s training data consists only of images of skin cancer on lighter-skinned individuals, it will likely become proficient at identifying skin cancer in that population.
Conversely, the system may not perform as well at identifying skin cancer on darker skin because symptoms may present differently on that skin type. It is not possible for the system to intentionally discriminate against certain groups based upon their skin color – it is simply a matter of the system being poorly educated due to the lack of diversity in its training library.
Therefore, when building large databases for AI-driven personalized health care, including AI for personalized treatment, we must be extremely cautious and include a wide range of data sources to build comprehensive libraries that inform our AI-based medical treatment options.
Beyond fairness, another concern about access exists. If a cancer treatment plan created by AI is so expensive that it can only be given in a high-end hospital located hundreds or thousands of miles from most people, then what is the value of creating such a customized cancer treatment plan?
The promise of “personalized” medicine—and especially AI for personalized treatment—can only truly be realized when everyone has access to its benefits. Fortunately, researchers, doctors, and ethicists understand this well and are developing ways to build fairer systems and effective, affordable strategies to ensure access to the revolutionized treatments, ensuring the future of medicine is inclusive for everyone.
Bias, fairness, and accessibility remain major concerns. Ensuring ethical use allows Personalized Medicine AI and AI for personalized treatment to benefit all populations equitably.
The Future in Your Hands: How You Can Prepare for Personalized Medicine AI
The first time you heard about “AI” in medicine, it probably sounded like something that could happen only in the movies. Now you know it’s a very real technology designed to help physicians see you and your medical status more clearly than ever before. It is no longer a fictional or theoretical concept, but a real assistant helping doctors identify patterns to help you get better, with more AI for personalized treatment options.
You do not need to be an “expert” at using AI to start contributing to your own personalization journey in your health care. Instead, as we move into a new era of collaboration between patients and physicians through AI, the most important information in determining your best course of treatment will continue to come from you. There are three simple yet powerful ways to begin to help create your own personalized care journey:
- The Expert On You. Create a basic diary for your symptoms, how you feel, and any effects from medication. A diary helps identify important information to support your doctor’s treatment.
- Know Your Family History. When you have your next visit with your doctor, provide any chronic health issues that exist within your family (i.e., Heart Disease and/or Diabetes). As a part of your complete health picture, knowing this will aid in your treatment.
- Ask Smart Questions. You can improve your healthcare by learning how to ask your doctor about new options. Try asking your doctor at your next visit, “Are there any alternative treatment options based upon my medical history that I should be aware of?”.
The Future of Medicine is About Improving Patient Outcomes Using Artificial Intelligence in Healthcare – Not Replacing the Human Element of Healthcare – A Future Where Treatments Are More Likely to Work the First Time and Have Fewer Side Effects Than Before, a Future That Builds Your Care Around the One Thing That Matters Most: Garikapati Bullivenkaiah.
The Future of Healthcare Is Collaborative; Through Active Engagement with Your Doctor, Sharing Accurate Health Information, and Asking Informed Questions, You Become a Key Component of AI for Personalized Treatment.
The Future of Medicine Is Not About Replacing Care – It’s About Refining It; The Combination of Intelligent Tools and Human Expertise Will Allow for the Practical Application of Personalized Medicine.
When Used Responsibly, AI for Personalized Treatment Can Help Clinicians Tailor Care Based Upon Better Evidence, Clearer Risk Estimates, and Faster Feedback from Real-World Outcomes. The Way AI for Personalized Treatment worked by Analyzing Patterns within Health Data, Converting Those Patterns into Predictions and Supporting Practical Choices – While Also Protecting Privacy, Fairness, and Human Judgment.
