Machine learning also referred to as Artificial intelligence is some circles provides lots of benefits for the healthcare industry. Though machine learning and Artificial Intelligence are a little bit different, they bear lots of similarities one of which is the reservations held against them. Many are reluctant in accepting this technology. There have been several discussion (most still ongoing) on the ethical implication of Machine Learning in Health Care.
A common reservation (and with good reason if I might add) is the proposition that Machine learning in health care might lead to the belief that there won’t be much need for humans. Proponents of machine learning claim that with an analytics platform and machine learning running in the background, the human algorithm or the extra layer of a backup physician wouldn’t be necessary. The analytics engine would have infinitely more data than any one person could ever process. It would have a library of patients and their diagnosis and tissue type. It would have treatment options available with predictions of how long they would be effective, mortality rates, side effects, and cost. Regardless of all the effort by a human caregiver, an analytics platform could put in infinitely more work behind the scenes and deliver decisive information to the physician in real time. But at what expense?
Machine Learning and Ethics in Healthcare
It’s been said before that the best machine learning tool in healthcare is the doctor’s brain. Could there be a tendency for physicians to view machine learning as an unwanted second opinion? Or could there be a tendency for patients to trust doctors less and choose to rely on artificial intelligence? At one point, auto workers feared that robotics would eliminate their jobs. Similarly, there may be physicians who fear that machine learning is the beginning of a process that could render them obsolete. But it’s the art of medicine that can never be replaced. Patients will always need the human touch, and the caring and compassionate relationship with the people who deliver care. Neither machine learning, nor any other future technologies in medicine, will eliminate this, but will become tools that clinicians use to improve ongoing care. Instead of proposing that machine learning replace doctors, the focus should be on how to use machine learning to augment patient care. For example, a machine learning algorithm that can review the pathology slides and assist the pathologist with a diagnosis as opposed to the machine learning making the diagnosis. If Doctors can get the results in a fraction of the time with an identical degree of accuracy, then, ultimately, this is going to improve patient care and satisfaction.
Healthcare needs to move from thinking of machine learning as a futuristic concept to seeing it as a real-world tool that can be deployed today. If machine learning is to have a role in healthcare, then we must take an incremental approach. We must find specific use cases in which machine learning capabilities provide value from a specific technological application. This will be a step-by-step pathway to incorporating more analytics, machine learning, and predictive algorithms into everyday clinical practice.
The Role of Data in Machine Learning in Health Care
Put simply, data drive machine learning. As more data is available, physicians have better information to give patients. Predictive algorithms and machine learning can give hospitals a better predictive model of mortality that doctors can use to educate patients.
But machine learning needs a certain amount of data to generate an effective algorithm and so much of machine learning will initially come from organizations with big datasets. These datasets will enable comparative effectiveness, research, and produce unique, powerful machine learning algorithms. For smaller organizations or entities with smaller data sets, they will be able to merge their data with larger systems or even with several other small systems to create one large data set. As larger datasets begin to run machine learning, healthcare organizations can improve care in more specific ways for each region. And considering rare diseases with low data volumes, it should be possible to merge regional data into national sets to scale the volume needed for machine learning.