Machine Doctors and Disease Detectives

By Sarah Yung

Artificial Intelligence (AI) goes all the way back to the 1970s, with Stanford’s prototype AI Mycin used to treat blood infections. Back then, patient records were packed in file boxes, stowed away in a musty closet in the corner of a hospital. Coming years may dramatically change how we handle patient records and data. The advances in computational power and accumulation of massive amounts of data make many clinical problems ripe for AI applications. Machines have the potential to vastly improve medical care, primarily by augmenting the skills of today’s human physicians.

AI in healthcare spans many of the core fields of medicine. From diagnostics to health and wellness to smart devices, technology is making doctors better and more efficient at what they do. Software’s ability to adapt without human intervention will soon make it indispensable in the field of medicine.

Harnessing the Power of Computational Algorithms


Machine learning is improving disease and symptom detection, enabling doctors to give patients the treatment they need. Algorithms are suited for finding patterns and making connections. Medical care is all about finding and treating diseases, which are defined by different symptoms. Machines have a keener eye for these symptoms than humans do - it’s simply what they are designed to do. A machine’s adeptness at pattern-detection may help us leverage a greater amount of patient data. 

In particular, only 3% of cancer patients are enrolled in clinical trials. AI could leverage data from the other 97%, drawing conclusions and devising potential treatments from an untapped source of information. Pharmaceutical companies can use bioinformatics to discover and develop cures and new treatments. Researchers may also be able to determine regions where certain drugs may or may not be effective.

AI could also enable more consistent interpretation of data. Take radiology. CT scans, MRIs, and X-rays all provide an internal view of a patient’s body. However, different experts will interpret these images differently, which could lead to very different plans of treatment. Computers, uninfluenced by emotions or fatigue, could make identifying symptoms and classifying diseases more uniform. Many companies are employing machine learning software to make minor diagnoses from within smartphone apps. More significant diagnoses are made by machine and human working in tandem.

Most algorithms concerned with disease and symptom detection target abnormal cell growth or development of cancer. The Lymph Node Assistant, or LYNA, was created by Google to identify metastatic breast cancer tumors. Compared to human reviewers, LYNA managed to halve the average slide review time. Algorithms can also be used to determine treatment plans. A new computer program developed at the University of Arizona can personalize drug treatments using a patient’s genetic information. The incredible accuracy and efficiency of machines in detecting potential diseases allows doctors to focus on their patients’ treatments.

Medical Solutions in Developing Nations

But beyond disease detection, machine learning enables accessible medical care everywhere. AI brings healthcare to developing countries while transforming healthcare in first world nations. Faced with a different set of problems, developing countries are focused on providing basic services to those in poverty in remote locations. Although we may not be able to scale down advanced AI tech solutions for smaller regional healthcare providers, it can still assist on administrative tasks, allowing physicians to focus wholly on their patient. With 24-hour availability, AI could even reduce the number of appointments that one has to make with their doctor.

Software is also making the larger medical community an available resource for doctors everywhere. Thousands of clinicians from all over the world are collaborating to use and build a diagnostic and management tool known as Human Dx. Doctors can ask a question and upload their info and the software will return a report of aggregated and prioritized responses. Their work will provide professional consultations to support high-value care, even in areas where there aren’t a lot of specialists.

Man and Machine

A robot’s precision makes it a promising candidate for assisting in surgery. In particular, Accenture projects that robot-assisted surgery will have potential annual benefits around $40 billion by 2026. We’re probably not going to see robot brain surgeons for a long while. Surgery is a delicate field, requiring delicate precision and the ability to make decisions on the fly. Robots are simply not adept at handling this complex task like human surgeons. Plus, many people are leery of a robot performing their surgery.

Many consumers are wearing fitness bands or smart watches to keep track of their exercise and sleep. Similar medical grade devices have even greater capabilities. Depending on their design and sophistication, these devices can track a person’s heart rate, oxygen level, breathing, and other data. This provides a wealth of data to healthcare providers not available otherwise between patients’ appointments. Wearable technology can also alert users immediately if there is a potential problem, increasing the chance that they will get the care they need.

There is a lot of budding interest in AI-enabled systems that can be integrated with the human body. Cardiology is a difficult field to make advances in, given the life and death stakes inherent in heart conditions. However, scientists are developing an implantable defibrillator that monitors heart rhythms of at-risk patients, and can administer a shock if necessary. There are also potential applications for artificial intelligence in brain-computer interfaces. AI networks are modeled after the human brain’s function. Researchers hope that brain-computer interfaces can replace other types of computer interfaces. This could be particularly helpful for people with permanent or temporary disabilities.

Records and Research

Although doctors have mostly moved out of the paper world, keeping health records is still a tedious task. The use of electronic health records is pervasive in the medical world, but requires a lot of extra work on the part of doctors and medical assistants. Video-based image recognition may be able to handle the bulk of this task, and add their own extra insight to EHRs, filling in the blanks that humans may miss.

Natural language processing will allow voice recognition capabilities to replace keyboards, removing the need for manual entry. Reworking the record-keeping system would transfer time-consuming tasks to software, reducing the human labor and the costs associated with modern healthcare.

Artificial intelligence also has the capability to transform clinical trials. Traditional research and development is a lengthy and expensive process. Machine learning can analyze and process information about relevant compounds far faster than conventional methods, saving the company time and the cost of manual labor. Dozens of health and pharmaceutical companies are leveraging new technology to help drug discovery and reduce the time it takes to bring drugs to market. With Johnson & Johnson, IBM Watson is taking natural language processing into more pioneering fields. Their collaboration focuses on utilizing natural language processing to analyze medical papers to aid in drug development.

The Black Box Algorithm

Our new technology is not perfect, however. We cannot rely on software and systems to manage patient care at its current state. One significant risk associated with the use of AI systems is bias. Biased results stem from the biases of humans involved in creating and training the algorithm, whether their bias is intentional or unintentional. If a self-learning system is trained on biased or flawed data, it could make erroneous recommendations or decisions. And in medicine, unlike in finance or retail, one decision can be the difference between life and death. Implementing AI will certainly require new ethics rules to address and prevent bias around AI.

However, there are a lot of barriers to overcome before medical professionals would consider fully relying on software. The regulations imposed upon both the algorithms and clinical trials pose an obstacle for artificial intelligence to gain a foothold in medicine. Many algorithms rely on intricate, difficult to deconvolute mathematics, often referred to as a black box. This ‘black box’ makes it difficult to maintain transparency surrounding researchers’ scientific methods. It’s hard for doctors and patients alike to trust a mysterious program without knowing how it works. Many patients are also harbor privacy concerns regarding their data being used and analyzed by robots. Although security is constantly being improved, data breaches sadly continue to be a common occurrence. For now, algorithms cannot operate independently in clinics.

The Art of Medicine

A growing machine presence in medicine may pose a risk to doctors and patients alike. As technologies driven by profit develop the technology behind advanced medical care, some fear how it will affect the human touch in medicine. As surgical as the field may seem, patients also benefit greatly from reassurance from a medical professional, a reminder that somebody besides their family cares about their wellbeing. The biggest application of AI will be in freeing up doctors to truly connect with patients and do things that matter.

Optimism must be tempered with a healthy dose of caution.  Technology has equal potential to close and widen disparities.  In the fever surrounding medical applications of AI, we must take care to protect patient rights.  Patients need to be aware of what their data is being used for and be informed of the algorithm being used on them.  It also isn’t clear yet who will benefit from AI health care. Patients and healthcare systems could benefit, or money could simply flow to tech companies and health care providers.

We must be mindful to avoid a so-called “health care apartheid,” where those of more modest means turn to rely solely on robot doctors.  It would be unsafe to blindly trust the predictions made by deep-learning software. Even large datasets cannot shield us from errors made when researchers apply their algorithms to a new population.  While these systems can apply rigid algorithms to better decision-making, they need the general intelligence of humans to correct possibly harmful predictions with major health and financial consequences.

Luckily, technology will be working with us, not against us. New technology usually doesn’t solve problems - it just makes us better at what we do. It’s still up to humans to put their newfound abilities to the task. We need both machine and human intelligence, to truly make an impact.  If implemented properly, AI can better clinical decision support for physicians and empower patients in preventive medicine. Most powerfully, AI may be able to have a truly life-changing effect by restoring the care in health care.


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