Cebo Daniel Thus, machine learning can help biological scientists and medical professionals by discovering and summarizing relevant patterns in vast datasets (Rajkomar et al., 2019). In diagnosis and therapy, it is critical to carefully evaluate the patterns discovered and predictions provided by machine learning algorithms.

AI has not only helped policymakers and frontline health workers to track and contain the spread of Covid-19, but it has also hastened the development of viable vaccinations. In the future, there is little question that AI will continue to play a central role in global healthcare projects.

AI has been utilized effectively in the current pandemic to find prospective medications that might be used to treat Covid-19. Google's Deepmind AI system AlphaFold recently discovered a solution to how proteins fold into their 3D shape, which may open up new avenues for structure-based medication creation.

The attached paper provides an argument for the HeartMath coherence model in such a context.

I don't understand the question, since machine learning and artificial [general] intelligence are not mutually exclusive. E.g. what if artificial [general] intelligence is machine-learning based? To my understanding, the term AI is supposed to be the higher umbrella that underneath it is various methods, one of which is machine learning. However, as the machine learning sub-branch of AI grew far faster, it eventually made many think that it is a separate thing, but it is not; it's just that the child became bigger than the parent.

Further, we, humans, ourselves are machines, too, and essentially implementations of "machine learning". Just biological machines that use neurons as some type of transistors. Part of our training includes the many years of evolution that carried out since the big bang (i.e. not simply what we started to learn after birth; this is only a small part of our training phase).

To me, the question is rather: will the next smartest life form evolve by nature via the boring way (same way humans came out of apes)? Or will it be a man made discovery/invention? And the answer is: does not matter, whichever happens to come sooner is the better one.

My prediction is that we will start modifying our genes, and the genes of other animals, and eventually create desired mutations faster than evolution would accidentally discover after. Add: and it's going to be a hybrid mix in my view: partially biological, and partial electrical/mechanical/etc (nitpick: even though, biological is mechanical, just at a smaller scale).

You represent a philosophical approach in the field of basic science. I deal with the applications of AI in medicine. See my discussion "Artificial Intelligence in Statistical Analysis of Clinical Data". For the overall approach, see the Nathan Lambert discussion on TDS. https://towardsdatascience.com/the-bio-medicine-singularity-a9615708a925.

AI and Machine learning at the current state of the art cannot revolutionize medicine from the following reasons:

Inertia of thinking of medical professionals is high. This is slowing down the implementation of novel methods. In general, this is a good thing, as it protects patients from unnecessary and unexpected harms.

Mathematics and numerical methods in AI & ML are often black-boxes, which doesn't allow human interpretation, which is a great drawback due to our inability to understand the inner workings of such methods, viz above.

We shouldn't be fooled by the assumption that biology is equal to genes. Already now, we know for sure that morphological growth of tissues, organs, and bodies is driven by electric potentials expressed on cell membranes, see Michael Levin research on bioceuticals.

This methodology was already used to grow extra limbs, organs, and body parts of various lower level animals, heal breast cancer, and a lot more.

AI & ML are not some miraculous tools discovering unknown phenomena on a daily basis. Very important is that data are fed into them, and how those data are preprocessed, see paper below where this issue is discussed in depth.

GI can discover other mechanisms operating withing living entities that are so far escaping our attention.

The GI singularity is not discussed here. Just one remark. How can we trust something more intelligent that it is going genuinely help without any hidden agenda? We know that such behavior is common among people.

There should be some kind of multilevel control. This is ethically very hard question, as we can create a beast that will destroy the whole humanity.

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You might be interested about more detailed explanation of here discussed issues in the paper:

Jiri Kroc & Dmitryi Bobir, "Prediction of Medical Drugs-Induced Arrhythmias (Torsades de Pointes, Ventricular Tachycardia, and Ventricular Fibrillation) on Rabbit Model Up to One Hour Before Their Onset Using Computational Method Based on Complexity and Machine Learning", ResearchGate (2020) DOI: 10.13140/RG.2.2.14274.53442

https://www.researchgate.net/publication/340460356_Prediction_of_Medical_Drugs-Induced_Arrhythmias_Torsades_de_Pointes_Ventricular_Tachycardia_and_Ventricular_Fibrillation_on_Rabbit_Model_Up_to_One_Hour_Before_Their_Onset_Using_Computational_Method_Ba

GI is considered by natural philosophers and science fiction writers. GI is attractive to the public, e.g., the movies of the "Matrix" series. AI is not GI, it has a specific domain , given task to perform and methods . AI mimics the way humans think and act (John McCarthy,

widely recognized as the father of Artificial Intelligence), more or less successfully. In medicine there are a number of applications such as control of surgical robots, image recognition or diagnosis of disease entities. Statistical methods, machine learning, deep learning are used to create algorithms for medical guidlines. In recent years, the work on computerization of clinical guidelines has become more and more visible. Nowadays they outperform physicians in the art of diagnosis for single diseases, but in the case of syndrome of many diseases physician is still more effective.

The current standard for practicing medicine is EBM (Evidence Based Medicine). According to Sackett: "EBM is the reasoned, clear, deliberate application of the latest and most reliable scientific data in decision making." The first principle of EBM is, to remind the physician that scientific data is not enough to make decisions.